WorldWideScience

Sample records for network traffic prediction

  1. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Haiyang Yu

    2017-06-01

    Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

  2. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

    Science.gov (United States)

    Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei

    2017-01-01

    Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction. PMID:28672867

  3. Network traffic anomaly prediction using Artificial Neural Network

    Science.gov (United States)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  4. Network-wide BGP route prediction for traffic engineering

    Science.gov (United States)

    Feamster, Nick; Rexford, Jennifer

    2002-07-01

    The Internet consists of about 13,000 Autonomous Systems (AS's) that exchange routing information using the Border Gateway Protocol (BGP). The operators of each AS must have control over the flow of traffic through their network and between neighboring AS's. However, BGP is a complicated, policy-based protocol that does not include any direct support for traffic engineering. In previous work, we have demonstrated that network operators can adapt the flow of traffic in an efficient and predictable fashion through careful adjustments to the BGP policies running on their edge routers. Nevertheless, many details of the BGP protocol and decision process make predicting the effects of these policy changes difficult. In this paper, we describe a tool that predicts traffic flow at network exit points based on the network topology, the import policy associated with each BGP session, and the routing advertisements received from neighboring AS's. We present a linear-time algorithm that computes a network-wide view of the best BGP routes for each destination prefix given a static snapshot of the network state, without simulating the complex details of BGP message passing. We describe how to construct this snapshot using the BGP routing tables and router configuration files available from operational routers. We verify the accuracy of our algorithm by applying our tool to routing and configuration data from AT&T's commercial IP network. Our route prediction techniques help support the operation of large IP backbone networks, where interdomain routing is an important aspect of traffic engineering.

  5. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

    Directory of Open Access Journals (Sweden)

    Meng Fan-Bo

    2016-01-01

    Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

  6. Dynamic Network Traffic Flow Prediction Model based on Modified Quantum-Behaved Particle Swarm Optimization

    OpenAIRE

    Hongying Jin; Linhao Li

    2013-01-01

    This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination devi...

  7. A Traffic Prediction Model for Self-Adapting Routing Overlay Network in Publish/Subscribe System

    Directory of Open Access Journals (Sweden)

    Meng Chi

    2017-01-01

    Full Text Available In large-scale location-based service, an ideal situation is that self-adapting routing strategies use future traffic data as input to generate a topology which could adapt to the changing traffic well. In the paper, we propose a traffic prediction model for the broker in publish/subscribe system, which can predict the traffic of the link in future by neural network. We first introduced our traffic prediction model and then described the model integration. Finally, the experimental results show that our traffic prediction model could predict the traffic of link well.

  8. A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

    OpenAIRE

    Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang

    2016-01-01

    Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...

  9. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

    Directory of Open Access Journals (Sweden)

    Laisen Nie

    2018-01-01

    Full Text Available Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

  10. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    Science.gov (United States)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  11. Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks

    Science.gov (United States)

    Pamuła, Teresa

    2012-12-01

    This paper presents a method of classification of time series of traffic flow, on the section of the main road leading into the city of Gliwice. Video detectors recorded traffic volume data was used, covering the period of one year in 5-minute intervals - from June 2011 to May 2012. In order to classify the data a statistical analysis was performed, which resulted in the proposition of splitting the daily time series into four classes. The series were smoothed to obtain hourly flow rates. The classification was performed using neural networks with different structures and using a variable number of input data. The purpose of classification is the prediction of traffic flow rates in the afternoon basing on the morning traffic and the assessment of daily traffic volumes for a particular day of the week. The results can be utilized by intelligent urban traffic management systems.

  12. Predicting Traffic Flow in Local Area Networks by the Largest Lyapunov Exponent

    Directory of Open Access Journals (Sweden)

    Yan Liu

    2016-01-01

    Full Text Available The dynamics of network traffic are complex and nonlinear, and chaotic behaviors and their prediction, which play an important role in local area networks (LANs, are studied in detail, using the largest Lyapunov exponent. With the introduction of phase space reconstruction based on the time sequence, the high-dimensional traffic is projected onto the low dimension reconstructed phase space, and a reduced dynamic system is obtained from the dynamic system viewpoint. Then, a numerical method for computing the largest Lyapunov exponent of the low-dimensional dynamic system is presented. Further, the longest predictable time, which is related to chaotic behaviors in the system, is studied using the largest Lyapunov exponent, and the Wolf method is used to predict the evolution of the traffic in a local area network by both Dot and Interval predictions, and a reliable result is obtained by the presented method. As the conclusion, the results show that the largest Lyapunov exponent can be used to describe the sensitivity of the trajectory in the reconstructed phase space to the initial values. Moreover, Dot Prediction can effectively predict the flow burst. The numerical simulation also shows that the presented method is feasible and efficient for predicting the complex dynamic behaviors in LAN traffic, especially for congestion and attack in networks, which are the main two complex phenomena behaving as chaos in networks.

  13. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

    Science.gov (United States)

    Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

    2017-04-10

    This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  14. A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Kun Zhang

    2016-01-01

    Full Text Available Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO was introduced. Then, the structure and operation algorithms of WFNN are presented. The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.

  15. Sensitivity Analysis of Wavelet Neural Network Model for Short-Term Traffic Volume Prediction

    Directory of Open Access Journals (Sweden)

    Jinxing Shen

    2013-01-01

    Full Text Available In order to achieve a more accurate and robust traffic volume prediction model, the sensitivity of wavelet neural network model (WNNM is analyzed in this study. Based on real loop detector data which is provided by traffic police detachment of Maanshan, WNNM is discussed with different numbers of input neurons, different number of hidden neurons, and traffic volume for different time intervals. The test results show that the performance of WNNM depends heavily on network parameters and time interval of traffic volume. In addition, the WNNM with 4 input neurons and 6 hidden neurons is the optimal predictor with more accuracy, stability, and adaptability. At the same time, a much better prediction record will be achieved with the time interval of traffic volume are 15 minutes. In addition, the optimized WNNM is compared with the widely used back-propagation neural network (BPNN. The comparison results indicated that WNNM produce much lower values of MAE, MAPE, and VAPE than BPNN, which proves that WNNM performs better on short-term traffic volume prediction.

  16. Efficient model predictive control for large-scale urban traffic networks

    NARCIS (Netherlands)

    Lin, S.

    2011-01-01

    Model Predictive Control is applied to control and coordinate large-scale urban traffic networks. However, due to the large scale or the nonlinear, non-convex nature of the on-line optimization problems solved, the MPC controllers become real-time infeasible in practice, even though the problem is

  17. Integrated Control of Mixed Traffic Networks using Model Predictive Control

    NARCIS (Netherlands)

    Van den Berg, M.

    2010-01-01

    Motivation The growth of our road infrastructure cannot keep up with the growing mobility of people, and the corresponding increase in traffic demand. This results in daily congestion on the freeways. It is an illusion that the problem of congestion can be solved completely within a few years, but

  18. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  19. Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

    Science.gov (United States)

    Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

    2015-01-01

    Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

  20. Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model

    Directory of Open Access Journals (Sweden)

    Lv Pengfei

    2016-01-01

    Full Text Available This paper discusses the distribution regularity of ship arrival and departure and the method of prediction of ship traffic flow. Depict the frequency histograms of ships arriving to port every day and fit the curve of the frequency histograms with a variety of distribution density function by using the mathematical statistic methods based on the samples of ship-to-port statistics of Fangcheng port nearly a year. By the chi-square testing: the fitting with Negative Binomial distribution and t-Location Scale distribution are superior to normal distribution and Logistic distribution in the branch channel; the fitting with Logistic distribution is superior to normal distribution, Negative Binomial distribution and t-Location Scale distribution in main channel. Build the BP neural network and Markov model based on BP neural network model to forecast ship traffic flow of Fangcheng port. The new prediction model is superior to BP neural network model by comparing the relative residuals of predictive value, which means the new model can improve the prediction accuracy.

  1. Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model

    OpenAIRE

    Lv Pengfei; Zhuang Yuan; Yang Kun

    2016-01-01

    This paper discusses the distribution regularity of ship arrival and departure and the method of prediction of ship traffic flow. Depict the frequency histograms of ships arriving to port every day and fit the curve of the frequency histograms with a variety of distribution density function by using the mathematical statistic methods based on the samples of ship-to-port statistics of Fangcheng port nearly a year. By the chi-square testing: the fitting with Negative Binomial distribution and t...

  2. Traffic accident reconstruction and an approach for prediction of fault rates using artificial neural networks: A case study in Turkey.

    Science.gov (United States)

    Can Yilmaz, Ali; Aci, Cigdem; Aydin, Kadir

    2016-08-17

    Currently, in Turkey, fault rates in traffic accidents are determined according to the initiative of accident experts (no speed analyses of vehicles just considering accident type) and there are no specific quantitative instructions on fault rates related to procession of accidents which just represents the type of collision (side impact, head to head, rear end, etc.) in No. 2918 Turkish Highway Traffic Act (THTA 1983). The aim of this study is to introduce a scientific and systematic approach for determination of fault rates in most frequent property damage-only (PDO) traffic accidents in Turkey. In this study, data (police reports, skid marks, deformation, crush depth, etc.) collected from the most frequent and controversial accident types (4 sample vehicle-vehicle scenarios) that consist of PDO were inserted into a reconstruction software called vCrash. Sample real-world scenarios were simulated on the software to generate different vehicle deformations that also correspond to energy-equivalent speed data just before the crash. These values were used to train a multilayer feedforward artificial neural network (MFANN), function fitting neural network (FITNET, a specialized version of MFANN), and generalized regression neural network (GRNN) models within 10-fold cross-validation to predict fault rates without using software. The performance of the artificial neural network (ANN) prediction models was evaluated using mean square error (MSE) and multiple correlation coefficient (R). It was shown that the MFANN model performed better for predicting fault rates (i.e., lower MSE and higher R) than FITNET and GRNN models for accident scenarios 1, 2, and 3, whereas FITNET performed the best for scenario 4. The FITNET model showed the second best results for prediction for the first 3 scenarios. Because there is no training phase in GRNN, the GRNN model produced results much faster than MFANN and FITNET models. However, the GRNN model had the worst prediction results. The

  3. A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

    Directory of Open Access Journals (Sweden)

    Enrique Castillo

    2015-01-01

    Full Text Available A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations.

  4. Fluctuations in Urban Traffic Networks

    Science.gov (United States)

    Chen, Yu-Dong; Li, Li; Zhang, Yi; Hu, Jian-Ming; Jin, Xue-Xiang

    Urban traffic network is a typical complex system, in which movements of tremendous microscopic traffic participants (pedestrians, bicyclists and vehicles) form complicated spatial and temporal dynamics. We collected flow volumes data on the time-dependent activity of a typical urban traffic network, finding that the coupling between the average flux and the fluctuation on individual links obeys a certain scaling law, with a wide variety of scaling exponents between 1/2 and 1. These scaling phenomena can explain the interaction between the nodes' internal dynamics (i.e. queuing at intersections, car-following in driving) and changes in the external (network-wide) traffic demand (i.e. the every day increase of traffic amount during peak hours and shocking caused by traffic accidents), allowing us to further understand the mechanisms governing the transportation system's collective behavior. Multiscaling and hotspot features are observed in the traffic flow data as well. But the reason why the separated internal dynamics are comparable to the external dynamics in magnitude is still unclear and needs further investigations.

  5. Modelling traffic congestion using queuing networks

    Indian Academy of Sciences (India)

    Traffic Flow-Density diagrams are obtained using simple Jackson queuing network analysis. Such simple analytical models can be used to capture the effect of non- homogenous traffic. Keywords. Flow-density curves; uninterrupted traffic; Jackson networks. 1. Introduction. Traffic management has become very essential in ...

  6. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values....... The model investigates the traffic generation mechanisms, and grouping traffic into flows and applications....

  7. Best Practices Handbook: Traffic Engineering in Range Networks

    Science.gov (United States)

    2016-03-01

    accommodate an offered load or if traffic streams are inefficiently mapped onto available resources, causing subsets of network resources to become over...access equipment and video encoding equipment. 3. A response system, consisting of protocols and access mechanisms that allow the flow of traffic ...Multiprotocol Label Switching (MPLS) that enable the predictable traffic flow across the MRTFBs. A detailed description of network elements relevant to the

  8. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  9. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  10. Routing strategies in traffic network and phase transition in network ...

    Indian Academy of Sciences (India)

    Routing strategy; network traffic flow; hysteretic loop; phase transition from free flow state to congestion state; scale-free network; bi-stable state; traffic dynamics. PACS Nos 89.75.Hc; 89.20.Hh; 05.10.-a; 89.75.Fb. 1. Traffic dynamics based on local routing strategy on scale-free networks. Communication networks such as ...

  11. Wireless traffic steering for green cellular networks

    CERN Document Server

    Zhang, Shan; Zhou, Sheng; Niu, Zhisheng; Shen, Xuemin (Sherman)

    2016-01-01

    This book introduces wireless traffic steering as a paradigm to realize green communication in multi-tier heterogeneous cellular networks. By matching network resources and dynamic mobile traffic demand, traffic steering helps to reduce on-grid power consumption with on-demand services provided. This book reviews existing solutions from the perspectives of energy consumption reduction and renewable energy harvesting. Specifically, it explains how traffic steering can improve energy efficiency through intelligent traffic-resource matching. Several promising traffic steering approaches for dynamic network planning and renewable energy demand-supply balancing are discussed. This book presents an energy-aware traffic steering method for networks with energy harvesting, which optimizes the traffic allocated to each cell based on the renewable energy status. Renewable energy demand-supply balancing is a key factor in energy dynamics, aimed at enhancing renewable energy sustainability to reduce on-grid energy consum...

  12. Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

    National Research Council Canada - National Science Library

    Antonio Artuñedo; Raúl M del Toro; Rodolfo E Haber

    2017-01-01

    .... The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks...

  13. Traffic Management for Satellite-ATM Networks

    Science.gov (United States)

    Goyal, Rohit; Jain, Raj; Fahmy, Sonia; Vandalore, Bobby; Goyal, Mukul

    1998-01-01

    Various issues associated with "Traffic Management for Satellite-ATM Networks" are presented in viewgraph form. Specific topics include: 1) Traffic management issues for TCP/IP based data services over satellite-ATM networks; 2) Design issues for TCP/IP over ATM; 3) Optimization of the performance of TCP/IP over ATM for long delay networks; and 4) Evaluation of ATM service categories for TCP/IP traffic.

  14. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    for traffic classification, which can be used for nearly real-time processing of big amounts of data using affordable CPU and memory resources. Other questions are related to methods for real-time estimation of the application Quality of Service (QoS) level based on the results obtained by the traffic......Traffic monitoring and analysis can be done for multiple different reasons: to investigate the usage of network resources, assess the performance of network applications, adjust Quality of Service (QoS) policies in the network, log the traffic to comply with the law, or create realistic models...... classifier. This thesis is focused on topics connected with traffic classification and analysis, while the work on methods for QoS assessment is limited to defining the connections with the traffic classification and proposing a general algorithm. We introduced the already known methods for traffic...

  15. Traffic incidents analysis on Slovenian motorway network

    OpenAIRE

    Jakše, Bojan

    2013-01-01

    In my bachelor thesis we were analysing traffic incidents (such as accidents, congestions, heavy snow, etc.) on Slovenian road network, specifically we focused on incidents on motorways. We were starting from database of incidents provided by Prometno-informacijski center (Traffic information center) and added information about hourly traffic at the moment of incident. We were also researching possible correlations between weather and traffic congestions and accidents as well as behaviour of ...

  16. A Network Traffic Control Enhancement Approach over Bluetooth Networks

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2003-01-01

    This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...

  17. On traffic modelling in GPRS networks

    DEFF Research Database (Denmark)

    Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee

    2005-01-01

    Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...

  18. GSM Network Traffic Analysis | Ani | Nigerian Journal of Technology

    African Journals Online (AJOL)

    GSM networks are traffic intensive specifically the signaling traffic. Evolvement of effective and efficient performance management strategy requires accurate quantification of network signaling traffic volume along side with the user traffic volume. Inaccurate quantification may lead to serious network traffic congestion and ...

  19. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  20. Evaluation and Simulation of Common Video Conference Traffics in Communication Networks

    Directory of Open Access Journals (Sweden)

    Farhad faghani

    2014-01-01

    Full Text Available Multimedia traffics are the basic traffics in data communication networks. Especially Video conferences are the most desirable traffics in huge networks(wired, wireless, …. Traffic modeling can help us to evaluate the real networks. So, in order to have good services in data communication networks which provide multimedia services, QoS will be very important .In this research we tried to have an exact traffic model design and simulation to overcome QoS challenges. Also, we predict bandwidth by Kalman filter in Ethernet networks.

  1. Evaluation of traffic pollution on Moscow network

    Energy Technology Data Exchange (ETDEWEB)

    Lukanin, V.N.; Buslaev, A.P.; Yashina, M. [Moscow State Automobile and Road Technical University, MADI-TU, Mscow (Russian Federation)

    2000-07-01

    The Moscow vehicle fleet grows each year and traffic is a major source of air pollutants in Moscow. We have developed a model for air quality evaluation and management in a megalopolis in order to improve ecological parameters of vehicles. We have received estimations of traffic influence on human health. These analytical methods allow to regulate traffic flows on road network and structure of car fleet in order to minimise damage to the environment in large cities. Simulation methods are tested on traffic data of Moscow City. The purpose of the research is development of principles, modelling and experimental methods of air quality management in a large city in order to improve ecological parameters of vehicles, to regulate traffic flows on road network and structure of car fleet. (authors)

  2. Traffic Management for Next Generation Transport Networks

    DEFF Research Database (Denmark)

    Yu, Hao

    their network capacities. However, in order to provide more advanced video services than simply porting the traditional television services to the network, the service provider needs to do more than just augment the network capacity. Advanced traffic management capability is one of the relevant abilities...... slacken the steps of some network operators towards providing IPTV services. In this dissertation, the topology-based hierarchical scheduling scheme is proposed to tackle the problem addressed. The scheme simplifies the deployment process by placing an intelligent switch with centralized traffic...... management functions at the edge of the network, scheduling traffic on behalf of the other nodes. The topology-based hierarchical scheduling scheme is able to provide outstanding flow isolation due to its centralized scheduling ability, which is essential for providing IPTV services. In order to reduce...

  3. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  4. 4K Video Traffic Prediction using Seasonal Autoregressive Modeling

    Directory of Open Access Journals (Sweden)

    D. R. Marković

    2017-06-01

    Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.

  5. Detecting Target Data in Network Traffic

    Science.gov (United States)

    2017-03-01

    packets, such as unauthorized connections to services like FTP and SSH connections, as well as RDP and MSSQL. Stateful firewalls are designed to...Hashdb can also be used to analyze network traffic and embedded content in other documents. There are hashdb libraries for the Python and C...amount of data that it logs. Bro will look at to DNS traffic, HTTP requests, and if any other connections attempted to be made over FTP, SSH and other

  6. Neural network system for traffic flow management

    Science.gov (United States)

    Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard

    1992-09-01

    Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.

  7. TRAFFIC TIME SERIES FORECASTING BY FEEDFORWARD NEURAL NETWORK: A CASE STUDY BASED ON TRAFFIC DATA OF MONROE

    Directory of Open Access Journals (Sweden)

    M. Raeesi

    2014-10-01

    Full Text Available Short time prediction is one of the most important factors in intelligence transportation system (ITS. In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.

  8. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles.

    Science.gov (United States)

    Wan, Jiafu; Liu, Jianqi; Shao, Zehui; Vasilakos, Athanasios V; Imran, Muhammad; Zhou, Keliang

    2016-01-11

    The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.

  9. Competitive Traffic Assignment in Road Networks

    Directory of Open Access Journals (Sweden)

    Krylatov Alexander Y.

    2016-09-01

    Full Text Available Recently in-vehicle route guidance and information systems are rapidly developing. Such systems are expected to reduce congestion in an urban traffic area. This social benefit is believed to be reached by imposing the route choices on the network users that lead to the system optimum traffic assignment. However, guidance service could be offered by different competitive business companies. Then route choices of different mutually independent groups of users may reject traffic assignment from the system optimum state. In this paper, a game theoretic approach is shown to be very efficient to formalize competitive traffic assignment problem with various groups of users in the form of non-cooperative network game with the Nash equilibrium search. The relationships between the Wardrop’s system optimum associated with the traffic assignment problem and the Nash equilibrium associated with the competitive traffic assignment problem are investigated. Moreover, some related aspects of the Nash equilibrium and the Wardrop’s user equilibrium assignments are also discussed.

  10. Scaling in Computer Network Traffic

    Science.gov (United States)

    2005-01-07

    behaviour, measurement .... 29 The Self-Similar Traffic Model Fractional Gaussian Noise (fGn) and Fractional Brownian Motion (fBm) The unique...AVAILABILITY STATEMENT Approved for public release, distribution unlimited 13. SUPPLEMENTARY NOTES See also ADM001750, Wavelets and Multifractal Analysis... Multifractality Wavelet qth order moments: IE|dX(j, k)|q ∼ C 2αqj, j → 0. Estimating the LHS from data using Sq(j) = 1 nj ∑ k |dX(j, k)|q, and measure the slopes

  11. Analyzing Worms and Network Traffic using Compression

    OpenAIRE

    Wehner, Stephanie

    2005-01-01

    Internet worms have become a widespread threat to system and network operations. In order to fight them more efficiently, it is necessary to analyze newly discovered worms and attack patterns. This paper shows how techniques based on Kolmogorov Complexity can help in the analysis of internet worms and network traffic. Using compression, different species of worms can be clustered by type. This allows us to determine whether an unknown worm binary could in fact be a later version of an existin...

  12. Multimodale trafiknet i GIS (Multimodal Traffic Network in GIS)

    DEFF Research Database (Denmark)

    Kronbak, Jacob; Brems, Camilla Riff

    1996-01-01

    The report introduces the use of multi-modal traffic networks within a geographical Information System (GIS). The necessary theory of modelling multi-modal traffic network is reviewed and applied to the ARC/INFO GIS by an explorative example.......The report introduces the use of multi-modal traffic networks within a geographical Information System (GIS). The necessary theory of modelling multi-modal traffic network is reviewed and applied to the ARC/INFO GIS by an explorative example....

  13. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    Directory of Open Access Journals (Sweden)

    Maciej Szmit

    2012-01-01

    Full Text Available The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  14. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    OpenAIRE

    Maciej Szmit; Anna Szmit

    2012-01-01

    The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  15. Highway traffic noise prediction based on GIS

    Science.gov (United States)

    Zhao, Jianghua; Qin, Qiming

    2014-05-01

    Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

  16. Dynamic traffic grooming with multigranularity traffic in WDM optical mesh networks

    Science.gov (United States)

    Huang, Jun; Zeng, Qingji; Liu, Jimin; Xiao, Pengcheng; Liu, Hua; Xiao, Shilin

    2004-04-01

    In this paper, a traffic-grooming problem for multi-granularity traffic of SDH/SONET in WDM grooming mesh networks is investigated. We propose a path select routing algorithm to solve this problem. The performances of this traffic grooming path select routing algorithm are evaluated in WDM grooming networks. Finally, we presented and compared the simulation results of this algorithm in dynamic traffic grooming WDM mesh networks with that of other algorithms.

  17. Broadband Traffic Forecasting in the Transport Network

    Directory of Open Access Journals (Sweden)

    Valentina Radojičić

    2012-07-01

    Full Text Available This paper proposes a modification of traffic forecast model generated by residential and small business (SOHO, Small Office Home Office users. The model includes forecasted values of different relevant factors and competition on broadband market. It allows forecasting the number of users for various broadband technologies and interaction impact of long-standing technologies as well as the impact of the new technology entrant on the market. All the necessary parameters are evaluated for the Serbian broadband market. The long-term forecasted results of broadband traffic are given. The analyses and evaluations performed are important inputs for the transport network resources planning.

  18. Networks and their traffic in multiplayer games

    Directory of Open Access Journals (Sweden)

    Cristian Andrés Melo López

    2016-06-01

    Full Text Available Computer games called multiplayer real-time, or (MCG are at the forefront of the use of the possibilities of the network. Research on this subject have been made for military simulations, virtual reality systems, computer support teamwork, the solutions diverge on the problems posed by MCG. With this in mind, this document provides an overview of the four issues affecting networking at the MCG. First, network resources (bandwidth, latency and computing capacity, together with the technical limits within which the MCG must operate. Second, the distribution concepts include communication architectures (peer-to-peer, client / server, server / network, and data and control architectures (centralized, distributed and reproduced .Thirdly, scalability allows the MCG to adapt to changes in parameterization resources. Finally, security is intended to fend off the traps and vandalism, which are common in online games; to check traffic, particularly these games we decided to take the massively multiplayer game League of Legends, a scene corresponding to a situation of real life in a network of ADSL access network is deployed has been simulated by using NS2 Three variants of TCP, it means SACK TCP, New Reno TCP, and TCP Vegas, have been considered for the cross traffic. The results show that TCP Vegas is able to maintain a constant speed while racing against the game traffic, since it avoids the packet loss and the delays in the tail caused by high peaks, without increasing the size of the sender window. SACK TCP and TCP New Reno, on the other hand, tend to increase continuously the sender window size, which could allow a greater loss of packages and also to cause unwanted delays for the game traffic.

  19. Scheduling Network Traffic for Grid Purposes

    DEFF Research Database (Denmark)

    Gamst, Mette

    This thesis concerns scheduling of network traffic in grid context. Grid computing consists of a number of geographically distributed computers, which work together for solving large problems. The computers are connected through a network. When scheduling job execution in grid computing, data...... transmission has so far not been taken into account. This causes stability problems, because data transmission takes time and thus causes delays to the execution plan. This thesis proposes the integration of job scheduling and network routing. The scientific contribution is based on methods from operations...

  20. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  1. Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2012-01-01

    Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.

  2. Estimating Traffic and Anomaly Maps via Network Tomography

    OpenAIRE

    Mardani, Morteza; Giannakis, Georgios B.

    2014-01-01

    Mapping origin-destination (OD) network traffic is pivotal for network management and proactive security tasks. However, lack of sufficient flow-level measurements as well as potential anomalies pose major challenges towards this goal. Leveraging the spatiotemporal correlation of nominal traffic, and the sparse nature of anomalies, this paper brings forth a novel framework to map out nominal and anomalous traffic, which treats jointly important network monitoring tasks including traffic estim...

  3. A Traffic Prediction Algorithm for Street Lighting Control Efficiency

    Directory of Open Access Journals (Sweden)

    POPA Valentin

    2013-01-01

    Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.

  4. Traffic measurement for big network data

    CERN Document Server

    Chen, Shigang; Xiao, Qingjun

    2017-01-01

    This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems. The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achi...

  5. Network traffic model using GIPP and GIBP

    Science.gov (United States)

    Lee, Yong Duk; Van de Liefvoort, Appie; Wallace, Victor L.

    1998-10-01

    In telecommunication networks, the correlated nature of teletraffic patterns can have significant impact on queuing measures such as queue length, blocking and delay. There is, however, not yet a good general analytical description which can easily incorporate the correlation effect of the traffic, while at the same time maintaining the ease of modeling. The authors have shown elsewhere, that the covariance structures of the generalized Interrupted Poisson Process (GIPP) and the generalized Interrupted Bernoulli Process (GIBP) have an invariance property which makes them reasonably general, yet algebraically manageable, models for representing correlated network traffic. The GIPP and GIBP have a surprisingly rich sets of parameters, yet these invariance properties enable us to easily incorporate the covariance function as well as the interarrival time distribution into the model to better matchobservations. In this paper, we show an application of GIPP and GIBP for matching an analytical model to observed or experimental data.

  6. Discovering urban mobility patterns with PageRank based traffic modeling and prediction

    Science.gov (United States)

    Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

    2017-11-01

    Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

  7. The effects of redundancy and information manipulation on traffic networks

    OpenAIRE

    Özel, Berk; Ozel, Berk

    2014-01-01

    Traffic congestion is one of the most frequently encountered problems in real life. It is not only a scientific concern of scholars, but also an inevitable issue for most of the individuals living in urban areas. Since every driver in traffic networks tries to minimize own journey length, and volume of the traffic prevents coordination between individuals, a cooperative behavior will not be provided spontaneously in order to decrease the total cost of the network and the time spent on traffic...

  8. Spatio-Temporal Ensemble Prediction on Mobile Broadband Network Data

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pitarch, Yoann; Pedersen, Torben Bach

    2013-01-01

    network nodes, fully or partly, in low traffic loads. To accomplish such a dynamic network optimization, it is crucial to predict very accurately low traffic periods. In this paper, we tackle this problem using data mining and propose Spatio-Temporal Ensemble Prediction (STEP). In a nutshell, STEP...

  9. Routing strategies in traffic network and phase transition in network ...

    Indian Academy of Sciences (India)

    The dynamics of information traffic over scale-free networks has been investigated systematically. A series of routing strategies of data packets have been proposed, including the local routing strategy, the next-nearest-neighbour routing strategy, and the mixed routing strategy based on local static and dynamic information.

  10. A Headway to QoS on Traffic Prediction over VANETs using RRSCM Statistical Classifier

    Directory of Open Access Journals (Sweden)

    ISHTIAQUE MAHMOOD

    2016-07-01

    Full Text Available In this paper, a novel throughput measurement forecast model is recommended for VANETs. The model is based on a statistical technique adopted and deployed over a high speed IP network traffic. Network traffic would always experience more QoS (Quality of Service issues such as jitter, delay, packet loss and degradation due to very low bit rate codification too. Despite of all such dictated issues the traffic throughput is to be predicted with at most accuracy using a proposed multivariate analysis scheme represented as a RRSCM (Refined Regression Statistical Classifier Model that optimizes parting parameters. Henceforth, the focus is towards the measurement methodology that estimates the traffic parameters that triggers to predict the accurate traffic and extemporize the QoS for the end-users. Finally, the proposed RRSCM classification model?s end-results are compared with the ANN (Artificial Neural Network classification model to showcase its better act on the projected model

  11. Breakdown in traffic networks fundamentals of transportation science

    CERN Document Server

    Kerner, Boris S

    2017-01-01

    This book offers a detailed investigation of breakdowns in traffic and transportation networks. It shows empirically that transitions from free flow to so-called synchronized flow, initiated by local disturbances at network bottlenecks, display a nucleation-type behavior: while small disturbances in free flow decay, larger ones grow further and lead to breakdowns at the bottlenecks. Further, it discusses in detail the significance of this nucleation effect for traffic and transportation theories, and the consequences this has for future automatic driving, traffic control, dynamic traffic assignment, and optimization in traffic and transportation networks. Starting from a large volume of field traffic data collected from various sources obtained solely through measurements in real world traffic, the author develops his insights, with an emphasis less on reviewing existing methodologies, models and theories, and more on providing a detailed analysis of empirical traffic data and drawing consequences regarding t...

  12. Encapsulating Urban Traffic Rhythms into Road Networks

    Science.gov (United States)

    Wang, Junjie; Wei, Dong; He, Kun; Gong, Hang; Wang, Pu

    2014-02-01

    Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution.

  13. Network Analysis of Urban Traffic with Big Bus Data

    CERN Document Server

    Zhao, Kai

    2016-01-01

    Urban traffic analysis is crucial for traffic forecasting systems, urban planning and, more recently, various mobile and network applications. In this paper, we analyse urban traffic with network and statistical methods. Our analysis is based on one big bus dataset containing 45 million bus arrival samples in Helsinki. We mainly address following questions: 1. How can we identify the areas that cause most of the traffic in the city? 2. Why there is a urban traffic? Is bus traffic a key cause of the urban traffic? 3. How can we improve the urban traffic systems? To answer these questions, first, the betweenness is used to identify the most import areas that cause most traffics. Second, we find that bus traffic is not an important cause of urban traffic using statistical methods. We differentiate the urban traffic and the bus traffic in a city. We use bus delay as an identification of the urban traffic, and the number of bus as an identification of the bus traffic. Third, we give our solutions on how to improve...

  14. Real-time network traffic classification technique for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

  15. An Architectural Concept for Intrusion Tolerance in Air Traffic Networks

    Science.gov (United States)

    Maddalon, Jeffrey M.; Miner, Paul S.

    2003-01-01

    The goal of an intrusion tolerant network is to continue to provide predictable and reliable communication in the presence of a limited num ber of compromised network components. The behavior of a compromised network component ranges from a node that no longer responds to a nod e that is under the control of a malicious entity that is actively tr ying to cause other nodes to fail. Most current data communication ne tworks do not include support for tolerating unconstrained misbehavio r of components in the network. However, the fault tolerance communit y has developed protocols that provide both predictable and reliable communication in the presence of the worst possible behavior of a limited number of nodes in the system. One may view a malicious entity in a communication network as a node that has failed and is behaving in an arbitrary manner. NASA/Langley Research Center has developed one such fault-tolerant computing platform called SPIDER (Scalable Proces sor-Independent Design for Electromagnetic Resilience). The protocols and interconnection mechanisms of SPIDER may be adapted to large-sca le, distributed communication networks such as would be required for future Air Traffic Management systems. The predictability and reliabi lity guarantees provided by the SPIDER protocols have been formally v erified. This analysis can be readily adapted to similar network stru ctures.

  16. Robust, Optimal, Predictive, and Integrated Road Traffic Control : Research proposal

    NARCIS (Netherlands)

    Van de Weg, G.S.; Hegyi, A.; Hoogendoorn, S.P.

    2014-01-01

    The development of control strategies for traffic lights, ramp metering installations, and variable speed limits to improve the throughput of road traffic networks can contribute to a more efficient use of road networks. In this project, a hierarchical controller will be developed for the

  17. Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

    Directory of Open Access Journals (Sweden)

    Antonio Artuñedo

    2017-04-01

    Full Text Available Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

  18. Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks.

    Science.gov (United States)

    Artuñedo, Antonio; Del Toro, Raúl M; Haber, Rodolfo E

    2017-04-26

    Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

  19. Self-Adapting Routing Overlay Network for Frequently Changing Application Traffic in Content-Based Publish/Subscribe System

    Directory of Open Access Journals (Sweden)

    Meng Chi

    2014-01-01

    Full Text Available In the large-scale distributed simulation area, the topology of the overlay network cannot always rapidly adapt to frequently changing application traffic to reduce the overall traffic cost. In this paper, we propose a self-adapting routing strategy for frequently changing application traffic in content-based publish/subscribe system. The strategy firstly trains the traffic information and then uses this training information to predict the application traffic in the future. Finally, the strategy reconfigures the topology of the overlay network based on this predicting information to reduce the overall traffic cost. A predicting path is also introduced in this paper to reduce the reconfiguration numbers in the process of the reconfigurations. Compared to other strategies, the experimental results show that the strategy proposed in this paper could reduce the overall traffic cost of the publish/subscribe system in less reconfigurations.

  20. City traffic flow breakdown prediction based on fuzzy rough set

    Science.gov (United States)

    Yang, Xu; Da-wei, Hu; Bing, Su; Duo-jia, Zhang

    2017-05-01

    In city traffic management, traffic breakdown is a very important issue, which is defined as a speed drop of a certain amount within a dense traffic situation. In order to predict city traffic flow breakdown accurately, in this paper, we propose a novel city traffic flow breakdown prediction algorithm based on fuzzy rough set. Firstly, we illustrate the city traffic flow breakdown problem, in which three definitions are given, that is, 1) Pre-breakdown flow rate, 2) Rate, density, and speed of the traffic flow breakdown, and 3) Duration of the traffic flow breakdown. Moreover, we define a hazard function to represent the probability of the breakdown ending at a given time point. Secondly, as there are many redundant and irrelevant attributes in city flow breakdown prediction, we propose an attribute reduction algorithm using the fuzzy rough set. Thirdly, we discuss how to predict the city traffic flow breakdown based on attribute reduction and SVM classifier. Finally, experiments are conducted by collecting data from I-405 Freeway, which is located at Irvine, California. Experimental results demonstrate that the proposed algorithm is able to achieve lower average error rate of city traffic flow breakdown prediction.

  1. Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

    Directory of Open Access Journals (Sweden)

    Hua-pu Lu

    2015-01-01

    Full Text Available With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

  2. Accurate Multisteps Traffic Flow Prediction Based on SVM

    Directory of Open Access Journals (Sweden)

    Zhang Mingheng

    2013-01-01

    Full Text Available Accurate traffic flow prediction is prerequisite and important for realizing intelligent traffic control and guidance, and it is also the objective requirement for intelligent traffic management. Due to the strong nonlinear, stochastic, time-varying characteristics of urban transport system, artificial intelligence methods such as support vector machine (SVM are now receiving more and more attentions in this research field. Compared with the traditional single-step prediction method, the multisteps prediction has the ability that can predict the traffic state trends over a certain period in the future. From the perspective of dynamic decision, it is far important than the current traffic condition obtained. Thus, in this paper, an accurate multi-steps traffic flow prediction model based on SVM was proposed. In which, the input vectors were comprised of actual traffic volume and four different types of input vectors were compared to verify their prediction performance with each other. Finally, the model was verified with actual data in the empirical analysis phase and the test results showed that the proposed SVM model had a good ability for traffic flow prediction and the SVM-HPT model outperformed the other three models for prediction.

  3. Prediction Models of Free-Field Vibrations from Railway Traffic

    DEFF Research Database (Denmark)

    Malmborg, Jens; Persson, Kent; Persson, Peter

    2017-01-01

    and railways close to where people work and live. Annoyance from traffic-induced vibrations and noise is expected to be a growing issue. To predict the level of vibration and noise in buildings caused by railway and road traffic, calculation models are needed. In the present paper, a simplified prediction...

  4. An analysis of network traffic classification for botnet detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2015-01-01

    Botnets represent one of the most serious threats to the Internet security today. This paper explores how can network traffic classification be used for accurate and efficient identification of botnet network activity at local and enterprise networks. The paper examines the effectiveness of detec......Botnets represent one of the most serious threats to the Internet security today. This paper explores how can network traffic classification be used for accurate and efficient identification of botnet network activity at local and enterprise networks. The paper examines the effectiveness...

  5. Determining optimal speed limits in traffic networks

    Directory of Open Access Journals (Sweden)

    Mansour Hadji Hosseinlou

    2015-07-01

    Full Text Available Determining the speed limit of road transport systems has a significant role in the speed management of vehicles. In most cases, setting a speed limit is considered as a trade-off between reducing travel time on one hand and reducing road accidents on the other, and the two factors of vehicle fuel consumption and emission rate of air pollutants have been neglected. This paper aims to evaluate optimal speed limits in traffic networks in a way that economized societal costs are incurred. In this study, experimental and field data as well as data from simulations are used to determine how speed is related to the emission of pollutants, fuel consumption, travel time, and the number of accidents. This paper also proposes a simple model to calculate the societal costs of travel and relate them to speed. As a case study, using emission test results on cars manufactured domestically and by simulating the suburban traffic flow by Aimsun software, the total societal costs of the Shiraz-Marvdasht motorway, which is one of the most traversed routes in Iran, have been estimated. The results of the study show that from a societal perspective, the optimal speed would be 73 km/h, and from a road user perspective, it would be 82 km/h (in 2011, the average speed of the passing vehicles on that motorway was 82 km/h. The experiments in this paper were run on three different vehicles with different types of fuel. In a comparative study, the results show that the calculated speed limit is lower than the optimal speed limits in Sweden, Norway, and Australia.

  6. Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

    Directory of Open Access Journals (Sweden)

    Su Yang

    Full Text Available Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1 Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2 The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3 The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

  7. Wavelength converter placement in optical networks with dynamic traffic

    DEFF Research Database (Denmark)

    Buron, Jakob Due; Ruepp, Sarah Renée; Wessing, Henrik

    2008-01-01

    We evaluate the connection provisioning performance of GMPLS-controlled wavelength routed networks under dynamic traffic load and using three different wavelength converter placement heuristics. Results show that a simple uniform placement heuristic matches the performance of complex heuristics...... under dynamic traffic assumptions....

  8. Cooperative driving in mixed traffic networks - Optimizing for performance

    NARCIS (Netherlands)

    Calvert, S.C.; Broek, T.H.A. van den; Noort, M. van

    2012-01-01

    This paper discusses a cooperative adaptive cruise control application and its effects on the traffic system. In previous work this application has been tested on the road, and traffic simulation has been used to scale up the results of the field test to larger networks and more vehicles. The

  9. Robustness of Interrelated Traffic Networks to Cascading Failures

    Science.gov (United States)

    Su, Zhen; Li, Lixiang; Peng, Haipeng; Kurths, Jürgen; Xiao, Jinghua; Yang, Yixian

    2014-06-01

    The vulnerability to real-life networks against small initial attacks has been one of outstanding challenges in the study of interrelated networks. We study cascading failures in two interrelated networks S and B composed from dependency chains and connectivity links respectively. This work proposes a realistic model for cascading failures based on the redistribution of traffic flow. We study the Barabási-Albert networks (BA) and Erdős-Rényi graphs (ER) with such structure, and found that the efficiency sharply decreases with increasing percentages of the dependency nodes for removing a node randomly. Furthermore, we study the robustness of interrelated traffic networks, especially the subway and bus network in Beijing. By analyzing different attacking strategies, we uncover that the efficiency of the city traffic system has a non-equilibrium phase transition at low capacity of the networks. This explains why the pressure of the traffic overload is relaxed by singly increasing the number of small buses during rush hours. We also found that the increment of some buses may release traffic jam caused by removing a node of the bus network randomly if the damage is limited. However, the efficiencies to transfer people flow will sharper increase when the capacity of the subway network αS > α0.

  10. Delayed Correlations in Inter-Domain Network Traffic

    OpenAIRE

    Rojkova, Viktoria; Kantardzic, Mehmed

    2007-01-01

    To observe the evolution of network traffic correlations we analyze the eigenvalue spectra and eigenvectors statistics of delayed correlation matrices of network traffic counts time series. Delayed correlation matrix D is composed of the correlations between one variable in the multivariable time series and another at a time delay \\tau . Inverse participation ratio (IPR) of eigenvectors of D deviates substantially from the IPR of eigenvectors of the equal time correlation matrix C. We relate ...

  11. Performance Modeling for Heterogeneous Wireless Networks with Multiservice Overflow Traffic

    DEFF Research Database (Denmark)

    Huang, Qian; Ko, King-Tim; Iversen, Villy Bæk

    2009-01-01

    Performance modeling is important for the purpose of developing efficient dimensioning tools for large complicated networks. But it is difficult to achieve in heterogeneous wireless networks, where different networks have different statistical characteristics in service and traffic models....... Multiservice loss analysis based on multi-dimensional Markov chain becomes intractable in these networks due to intensive computations required. This paper focuses on performance modeling for heterogeneous wireless networks based on a hierarchical overlay infrastructure. A method based on decomposition...... of the correlated traffic is used to achieve an approximate performance modeling for multiservice in hierarchical heterogeneous wireless networks with overflow traffic. The accuracy of the approximate performance obtained by our proposed modeling is verified by simulations....

  12. Algorithm for queueing networks with multi-rate traffic

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk; King-Tim, Ko

    2011-01-01

    In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... is reversibility which implies that the arrival process and departure process are identical processes, for example state-dependent Poisson processes. This property is equivalent to reversibility. Due to product form, an open network with multi-rate traffic is easy to evaluate by convolution algorithms because...

  13. INTELLIGENT TRAFFIC-SAFETY MIRROR BY USING WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    Peter Danišovič

    2014-03-01

    Full Text Available This article is focused on the problematic of traffic safety, dealing with the problem of car intersections with blocked view crossing by a special wireless sensor network (WSN proposed for the traffic monitoring, concretely for vehicle’s detection at places, where it is necessary. Some ultra-low-power TI products were developed due to this reason: microcontroller MSP430F2232, 868MHz RF transceiver CC1101 and LDO voltage regulator TPS7033. The WSN consist of four network nodes supplied with the special safety lightings which serve the function of intelligent traffic safety mirror.

  14. FPGA Based Real-time Network Traffic Analysis using Traffic Dispersion Patterns

    Energy Technology Data Exchange (ETDEWEB)

    Khan, F; Gokhale, M; Chuah, C N

    2010-03-26

    The problem of Network Traffic Classification (NTC) has attracted significant amount of interest in the research community, offering a wide range of solutions at various levels. The core challenge is in addressing high amounts of traffic diversity found in today's networks. The problem becomes more challenging if a quick detection is required as in the case of identifying malicious network behavior or new applications like peer-to-peer traffic that have potential to quickly throttle the network bandwidth or cause significant damage. Recently, Traffic Dispersion Graphs (TDGs) have been introduced as a viable candidate for NTC. The TDGs work by forming a network wide communication graphs that embed characteristic patterns of underlying network applications. However, these patterns need to be quickly evaluated for mounting real-time response against them. This paper addresses these concerns and presents a novel solution for real-time analysis of Traffic Dispersion Metrics (TDMs) in the TDGs. We evaluate the dispersion metrics of interest and present a dedicated solution on an FPGA for their analysis. We also present analytical measures and empirically evaluate operating effectiveness of our design. The mapped design on Virtex-5 device can process 7.4 million packets/second for a TDG comprising of 10k flows at very high accuracies of over 96%.

  15. SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

    Science.gov (United States)

    Zhengfeng, Huang; Pengjun, Zheng; Wenjun, Xu; Gang, Ren

    Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

  16. Traffic Management in ATM Networks Over Satellite Links

    Science.gov (United States)

    Goyal, Rohit; Jain, Raj; Goyal, Mukul; Fahmy, Sonia; Vandalore, Bobby; vonDeak, Thomas

    1999-01-01

    This report presents a survey of the traffic management Issues in the design and implementation of satellite Asynchronous Transfer Mode (ATM) networks. The report focuses on the efficient transport of Transmission Control Protocol (TCP) traffic over satellite ATM. First, a reference satellite ATM network architecture is presented along with an overview of the service categories available in ATM networks. A delay model for satellite networks and the major components of delay and delay variation are described. A survey of design options for TCP over Unspecified Bit Rate (UBR), Guaranteed Frame Rate (GFR) and Available Bit Rate (ABR) services in ATM is presented. The main focus is on traffic management issues. Several recommendations on the design options for efficiently carrying data services over satellite ATM networks are presented. Most of the results are based on experiments performed on Geosynchronous (GEO) latencies. Some results for Low Earth Orbits (LEO) and Medium Earth Orbit (MEO) latencies are also provided.

  17. Cooperative Learning for Distributed In-Network Traffic Classification

    Science.gov (United States)

    Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

    2017-04-01

    Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

  18. Adaptive traffic control systems for urban networks

    Directory of Open Access Journals (Sweden)

    Radivojević Danilo

    2017-01-01

    Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

  19. Routing of Internal MANET Traffic over External Networks

    Directory of Open Access Journals (Sweden)

    Vinh Pham

    2009-01-01

    Full Text Available Many have proposed to connect Mobile Ad Hoc Networks (MANETs to a wired backbone Internet access network. This paper demonstrates that a wired backbone network can be utilized for more than just providing access to the global Internet. Traffic between mobile nodes in the ad hoc network may also be routed via this backbone network to achieve higher throughput, and to reduce the load in the ad hoc network. This is referred to as transit routing. This paper proposes a cost metric algorithm that facilitates transit routing for some of the traffic flows between nodes in the MANET. The algorithm aims at carrying out transit routing for a flow only when it leads to improvements of the performance. The proposal is implemented and tested in the ns-2 network simulator, and the simulation results are promising.

  20. Uncovering transportation networks from traffic flux by compressed sensing

    Science.gov (United States)

    Tang, Si-Qi; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru

    2015-08-01

    Transportation and communication networks are ubiquitous in nature and society. Uncovering the underlying topology as well as link weights, is fundamental to understanding traffic dynamics and designing effective control strategies to facilitate transmission efficiency. We develop a general method for reconstructing transportation networks from detectable traffic flux data using the aid of a compressed sensing algorithm. Our approach enables full reconstruction of network topology and link weights for both directed and undirected networks from relatively small amounts of data compared to the network size. The limited data requirement and certain resistance to noise allows our method to achieve real-time network reconstruction. We substantiate the effectiveness of our method through systematic numerical tests with respect to several different network structures and transmission strategies. We expect our approach to be widely applicable in a variety of transportation and communication systems.

  1. Traffic networks as information systems a viability approach

    CERN Document Server

    Aubin, Jean-Pierre

    2017-01-01

    This authored monograph covers a viability to approach to traffic management by advising to vehicles circulated on the network the velocity they should follow for satisfying global traffic conditions;. It presents an investigation of three structural innovations: The objective is to broadcast at each instant and at each position the advised celerity to vehicles, which could be read by auxiliary speedometers or used by cruise control devices. Namely, 1. Construct regulation feedback providing at each time and position advised velocities (celerities) for minimizing congestion or other requirements. 2. Taking into account traffic constraints of different type, the first one being to remain on the roads, to stop at junctions, etc. 3. Use information provided by the probe vehicles equipped with GPS to the traffic regulator; 4. Use other global traffic measures of vehicles provided by different types of sensors; These results are based on convex analysis, intertemporal optimization and viability theory as mathemati...

  2. Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information.

    Science.gov (United States)

    Bao, Xu; Li, Haijian; Qin, Lingqiao; Xu, Dongwei; Ran, Bin; Rong, Jian

    2016-10-27

    To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads.

  3. A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation

    OpenAIRE

    Liang, Yunyi; Cui, Zhiyong; Tian, Yu; Chen, Huimiao; Wang, Yinhai

    2018-01-01

    This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic state estimation. The GAA is able to combine traffic flow theory with neural networks and thus improve the accuracy of traffic state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability o...

  4. Model for Predicting Traffic Signs Functional Service Life – The Republic of Croatia Case Study

    Directory of Open Access Journals (Sweden)

    Dario Babić

    2017-06-01

    Full Text Available Traffic signs are the basic elements of communication between the relevant road authorities and road users. They manage, regulate, inform and warn road users to ensure their safe movement throughout transport networks. Traffic signs must be timely visible to all traffic participants in all weather and traffic conditions in order to fulfil their function, which means they must have satisfactory retroreflection properties. This paper presents a research of the deterioration of traffic signs retroreflection. The aim of this article is to develop models that will effectively enable predicting the retroreflection of traffic signs and thus optimize the maintenance activities and replacement of road signs to increase road safety. The research included measurements of retroreflection of retroreflective material Classes I and II (white, red and blue colour and Class III (red and yellow colour. Based on the collected data from the City of Zagreb (Republic of Croatia, the authors developed the models to estimate the functional service life of certain colours and materials used to make traffic signs. Considering that the average coefficient of determination for all the models is between 0.55-0.60, they present an effective tool in the traffic sign maintenance system.

  5. Online Incremental Learning for High Bandwidth Network Traffic Classification

    Directory of Open Access Journals (Sweden)

    H. R. Loo

    2016-01-01

    Full Text Available Data stream mining techniques are able to classify evolving data streams such as network traffic in the presence of concept drift. In order to classify high bandwidth network traffic in real-time, data stream mining classifiers need to be implemented on reconfigurable high throughput platform, such as Field Programmable Gate Array (FPGA. This paper proposes an algorithm for online network traffic classification based on the concept of incremental k-means clustering to continuously learn from both labeled and unlabeled flow instances. Two distance measures for incremental k-means (Euclidean and Manhattan distance are analyzed to measure their impact on the network traffic classification in the presence of concept drift. The experimental results on real datasets show that the proposed algorithm exhibits consistency, up to 94% average accuracy for both distance measures, even in the presence of concept drifts. The proposed incremental k-means classification using Manhattan distance can classify network traffic 3 times faster than Euclidean distance at 671 thousands flow instances per second.

  6. Traffic Adaptive MAC Protocols in Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Farhan Masud

    2017-01-01

    Full Text Available In Wireless Body Area Networks (WBANs, every healthcare application that is based on physical sensors is responsible for monitoring the vital signs data of patient. WBANs applications consist of heterogeneous and dynamic traffic loads. Routine patient’s observation is described as low-load traffic while an alarming situation that is unpredictable by nature is referred to as high-load traffic. This paper offers a thematic review of traffic adaptive Medium Access Control (MAC protocols in WBANs. First, we have categorized them based on their goals, methods, and metrics of evaluation. The Zigbee standard IEEE 802.15.4 and the baseline MAC IEEE 802.15.6 are also reviewed in terms of traffic adaptive approaches. Furthermore, a comparative analysis of the protocols is made and their performances are analyzed in terms of delay, packet delivery ratio (PDR, and energy consumption. The literature shows that no review work has been done on traffic adaptive MAC protocols in WBANs. This review work, therefore, could add enhancement to traffic adaptive MAC protocols and will stimulate a better way of solving the traffic adaptivity problem.

  7. Baselining Network-Wide Traffic by Time-Frequency Constrained Stable Principal Component Pursuit

    OpenAIRE

    Hu, Kai; Wang, Zhe; Yin, Baolin

    2013-01-01

    The Internet traffic analysis is important to network management,and extracting the baseline traffic patterns is especially helpful for some significant network applications.In this paper, we study on the baseline problem of the traffic matrix satisfying a refined traffic matrix decomposition model,since this model extends the assumption of the baseline traffic component to characterize its smoothness, and is more realistic than the existing traffic matrix models. We develop a novel baseline ...

  8. Algorithm for queueing networks with multi-rate traffic

    DEFF Research Database (Denmark)

    Iversen, Villy Bæk; Ko, King-Tim

    2011-01-01

    In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... is reversibility which implies that the arrival process and departure process are identical processes, for example state-dependent Poisson processes. This property is equivalent to reversibility. Due to product form, an open network with multi-rate traffic is easy to evaluate by convolution algorithms because...... the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolution algorithm to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate...

  9. Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions.

    Directory of Open Access Journals (Sweden)

    Ed Manley

    Full Text Available The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.

  10. A model of traffic signs recognition with convolutional neural network

    Science.gov (United States)

    Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

    2016-10-01

    In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

  11. A First Look into SCADA Network Traffic

    NARCIS (Netherlands)

    Barbosa, R.R.R.; Sadre, R.; Pras, Aiko

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of critical infrastructures, such as water distribution facilities. These networks provide automated processes that ensure the correct functioning of these infrastructures, in a operation much

  12. Near real-time traffic routing

    Science.gov (United States)

    Yang, Chaowei (Inventor); Cao, Ying (Inventor); Xie, Jibo (Inventor); Zhou, Bin (Inventor)

    2012-01-01

    A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

  13. Optimization of TTEthernet Networks to Support Best-Effort Traffic

    DEFF Research Database (Denmark)

    Tamas-Selicean, Domitian; Pop, Paul

    2014-01-01

    This paper focuses on the optimization of the TTEthernet communication protocol, which offers three traffic classes: time-triggered (TT), sent according to static schedules, rate-constrained (RC) that has bounded end-to-end latency, and best-effort (BE), the classic Ethernet traffic, with no timing...... guarantees. In our earlier work we have proposed an optimization approach named DOTTS that performs the routing, scheduling and packing / fragmenting of TT and RC messages, such that the TT and RC traffic is schedulable. Although backwards compatibility with classic Ethernet networks is one of TTEthernet......’s strong points, there is little research on this topic. However, in this paper, we extend our DOTTS optimization approach to optimize TTEthernet networks, such that not only the TT and RC messages are schedulable, but we also maximize the available bandwidth for BE messages. The proposed optimization has...

  14. Energy Saving: Scaling Network Energy Efficiency Faster than Traffic Growth

    NARCIS (Netherlands)

    Chen, Y.; Blume, O.; Gati, A.; Capone, A.; Wu, C.E.; Barth, U.; Marzetta, T.; Zhang, H.; Xu, S.

    2013-01-01

    As the mobile traffic is expected to continue its exponential growth in the near future, energy efficiency has gradually become a must criterion for wireless network design. Three fundamental questions need to be answered before the detailed design could be carried out, namely what energy efficiency

  15. Towards Mining Latent Client Identifiers from Network Traffic

    Directory of Open Access Journals (Sweden)

    Jain Sakshi

    2016-04-01

    Full Text Available Websites extensively track users via identifiers that uniquely map to client machines or user accounts. Although such tracking has desirable properties like enabling personalization and website analytics, it also raises serious concerns about online user privacy, and can potentially enable illicit surveillance by adversaries who broadly monitor network traffic.

  16. Traffic Analysis for Real-Time Communication Networks onboard Ships

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Jørgensen, N.

    1998-01-01

    The paper presents a novel method for establishing worst case estimates of queue lenghts and transmission delays in networks of interconnected segments each of ring topology as defined by the ATOMOS project for marine automation. A non probalistic model for describing traffic is introduced as well...

  17. Traffic Analysis for Real-Time Communication Networks onboard Ships

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Nielsen, Jens Frederik Dalsgaard; Jørgensen, N.

    The paper presents a novel method for establishing worst case estimates of queue lenghts and transmission delays in networks of interconnected segments each of ring topology as defined by the ATOMOS project for marine automation. A non probalistic model for describing traffic is introduced as well...

  18. Simulation of traffic capacity of inland waterway network

    NARCIS (Netherlands)

    Chen, L.; Mou, J.; Ligteringen, H.

    2013-01-01

    The inland waterborne transportation is viewed as an economic, safe and environmentally friendly alternative to the congested road network. The traffic capacity are the critical indicator of the inland shipping performance. Actually, interacted under the complicated factors, it is challenging to

  19. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    Science.gov (United States)

    2014-03-27

    which represent potentially insecure mechanisms for authentication and authorization. 9 An example of a hacker penetrating network security at a...transport most application 35 traffic such as Lightweight Directory Access Protocol ( LDAP ), HTTP, HyperText Transfer Protocol Secure (HTTPS), POP3...Department of Homeland Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 DPI deep packet inspection

  20. The effects of traffic structure on application and network performance

    CERN Document Server

    Aikat, Jay; Smith, F Donelson

    2012-01-01

    Over the past three decades, the Internet's rapid growth has spurred the development of new applications in mobile computing, digital music, online video, gaming and social networks. These applications rely heavily upon various underlying network protocols and mechanisms to enable, maintain and enhance their Internet functionalityThe Effects of Traffic Structure on Application and Network Performance provides the necessary tools for maximizing the network efficiency of any Internet application, and presents ground-breaking research that will influence how these applications are built in the fu

  1. Toward an optimal convolutional neural network for traffic sign recognition

    Science.gov (United States)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  2. Routing optimization in networks based on traffic gravitational field model

    Science.gov (United States)

    Liu, Longgeng; Luo, Guangchun

    2017-04-01

    For research on the gravitational field routing mechanism on complex networks, we further analyze the gravitational effect of paths. In this study, we introduce the concept of path confidence degree to evaluate the unblocked reliability of paths that it takes the traffic state of all nodes on the path into account from the overall. On the basis of this, we propose an improved gravitational field routing protocol considering all the nodes’ gravities on the path and the path confidence degree. In order to evaluate the transmission performance of the routing strategy, an order parameter is introduced to measure the network throughput by the critical value of phase transition from a free-flow phase to a jammed phase, and the betweenness centrality is used to evaluate the transmission performance and traffic congestion of the network. Simulation results show that compared with the shortest-path routing strategy and the previous gravitational field routing strategy, the proposed algorithm improves the network throughput considerably and effectively balances the traffic load within the network, and all nodes in the network are utilized high efficiently. As long as γ ≥ α, the transmission performance can reach the maximum and remains unchanged for different α and γ, which ensures that the proposed routing protocol is high efficient and stable.

  3. On the distribution of calls in a wireless network driven by fluid traffic

    NARCIS (Netherlands)

    Ule, Aljaz; Boucherie, Richardus J.

    2003-01-01

    This note develops a modelling approach for wireless networks driven by fluid traffic models. Introducing traffic sets that follow movement of subscribers, the wireless network with time-varying rates is transformed into a stationary network at these traffic sets, which yields that the distribution

  4. On the Distribution of CAlls in a Wireless Network driven by Fluid Traffic

    NARCIS (Netherlands)

    Ule, A.; Boucherie, R.J.

    2003-01-01

    This note develops a modelling approach for wireless networks driven by fluid traffic models. Introducing traffic sets that follow movement of subscribers, the wireless network with time-varying rates is transformed into a stationary network at these traffic sets, which yields that the distribution

  5. On the distribution of customers in a wireless network driven by fluid traffic

    NARCIS (Netherlands)

    Ule, A.; Boucherie, R.J.

    2000-01-01

    This note develops a modelling approach for wireless networks driven byfluid traffic models. Introducing traffic sets that follow movement ofsubscribers, the wireless network with time-varying rates is transformedinto a stationary network at these traffic sets, which yields that thedistribution of

  6. Traffic volume estimation using network interpolation techniques.

    Science.gov (United States)

    2013-12-01

    Kriging method is a frequently used interpolation methodology in geography, which enables estimations of unknown values at : certain places with the considerations of distances among locations. When it is used in transportation field, network distanc...

  7. Generic Traffic Descriptors in Managing Service Quality in BISDN/ATM Network

    Directory of Open Access Journals (Sweden)

    Ivan Bošnjak

    2002-03-01

    Full Text Available Traffic models for multiservice broadband networks differsignificantly regarding simple analytic models applicable intelephone traffic and circuit-switch network. The paper presentsa clear analysis of standardised traffic descriptors andquality parameters of the main services in BISDNIATM. Trafficdescriptors have been associated with the basic values andconcepts developed within generic traffic theory. Part systematisationof traffic parameters has been performed as basis for formalisedgeneralised description of parameters and effectivequality management of A TM services.

  8. Efficient traffic grooming in SONET/WDM BLSR Networks

    Energy Technology Data Exchange (ETDEWEB)

    Awwal, A S; Billah, A B; Wang, B

    2004-04-02

    In this paper, we study traffic grooming in SONET/WDM BLSR networks under the uniform all-to-all traffic model with an objective to reduce total network costs (wavelength and electronic multiplexing costs), in particular, to minimize the number of ADMs while using the optimal number of wavelengths. We derive a new tighter lower bound for the number of wavelengths when the number of nodes is a multiple of 4. We show that this lower bound is achievable. All previous ADM lower bounds except perhaps that in were derived under the assumption that the magnitude of the traffic streams (r) is one unit (r = 1) with respect to the wavelength capacity granularity g. We then derive new, more general and tighter lower bounds for the number of ADMs subject to that the optimal number of wavelengths is used, and propose heuristic algorithms (circle construction algorithm and circle grooming algorithm) that try to minimize the number of ADMs while using the optimal number of wavelengths in BLSR networks. Both the bounds and algorithms are applicable to any value of r and for different wavelength granularity g. Performance evaluation shows that wherever applicable, our lower bounds are at least as good as existing bounds and are much tighter than existing ones in many cases. Our proposed heuristic grooming algorithms perform very well with traffic streams of larger magnitude. The resulting number of ADMs required is very close to the corresponding lower bounds derived in this paper.

  9. Traffic Rules in Electronic Financial Transactions (EFT Networks

    Directory of Open Access Journals (Sweden)

    Vedran Batoš

    2002-01-01

    Full Text Available This paper presents the traffic rules in the EFT (ElectronicFinancial Transactions networks, based on the implementationof the solution called Gold-Net developed and implementedby Euronet Worldwide Inc. Following the traffic rulesin EFT networks, out of its worldwide experience, Gold-Netevolved a comprehensive and expandable EFT network solutiondesigned to meet an institution's needs today and in the future.It is an ITM (Integrated Transaction Management solution,modular and expandable, and consists of a comprehensiveEFT software modules with ATM and POS driving capabilities.The combination of ATM management and the onlineconnection form the intercept processing control module. Asthe marketplace grows, this solution ensures that an ente1prisemay position itself for future growth and expanded service offerings.

  10. Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-01-01

    Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

  11. The wireshark field guide analyzing and troubleshooting network traffic

    CERN Document Server

    Shimonski, Robert

    2013-01-01

    The Wireshark Field Guide provides hackers, pen testers, and network administrators with practical guidance on capturing and interactively browsing computer network traffic. Wireshark is the world's foremost network protocol analyzer, with a rich feature set that includes deep inspection of hundreds of protocols, live capture, offline analysis and many other features. The Wireshark Field Guide covers the installation, configuration and use of this powerful multi-platform tool. The book give readers the hands-on skills to be more productive with Wireshark as they drill

  12. Stream Traffic Communication in Packet Switched Networks,

    Science.gov (United States)

    1977-08-01

    Currently, the interconnection of surh networks [ McKe 74a] and the standardization of protocols [Pouz 75), (Hove 76) are each of considerable interest in the...clear that failures do occur in practice. Long term monitoring of the ARPANET [ McKe 74) shows a mean time between failures (MTBF) of 431 hours for Lines...D. C.) [ McKe 74) McKenzie, A. A. Letter to S. D. Crocker. 16 Janu- ary 1974. [ McKe 74a] McKenzie, A. M. "Some Computer Network Intercon- nection

  13. Traffic Based Optimization of Spectrum Sensing in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Changhua Yao

    2014-01-01

    Full Text Available We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN, by considering the data traffic of second user (SU. Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the achievable throughput, based on the data traffic of SU. Our model is more general because the traditional Sensing-Throughput Tradeoff model can be seen as a special case of our model. We also prove that the throughput is a concave function of sensing time and there is only one optimal sensing time value which is determined by the data traffic. Simulation results show that the proposed approach outperforms existing methods.

  14. Emulation of realistic network traffic patterns on an eight-node data vortex interconnection network subsystem

    Science.gov (United States)

    Small, Benjamin A.; Shacham, Assaf; Bergman, Keren; Athikulwongse, Krit; Hawkins, Cory; Wills, D. Scott

    2004-11-01

    e demonstrate the feasibility of the data vortex interconnection network architecture for use in supercomputing by emulating realistic network traffic on an eight-node subnetwork. The evaluation workload uses memory accesses from the Barnes-Hut application in the SLPASH-2 parallel computing benchmark suite, which was extracted by using the M5 multiprocessor system simulator. We confirm that traffic is routed correctly and efficiently.

  15. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  16. Load characterization, overload prediction, and anomaly detection for voice over IP traffic

    NARCIS (Netherlands)

    Mandjes, Michel; Saniee, Iraj; Stolyar, Alexander; Heidelberger, P.

    2001-01-01

    We consider the problem of traffic anomaly detection in IP networks. Traffic anomalies arise when there is overload due to failures in a network. We present general formulae for the variance of the cumulative traffic over a fixed time interval and show how the derived analytical expression

  17. Prediction Models of Free-Field Vibrations from Railway Traffic

    DEFF Research Database (Denmark)

    Malmborg, Jens; Persson, Kent; Persson, Peter

    2017-01-01

    and railways close to where people work and live. Annoyance from traffic-induced vibrations and noise is expected to be a growing issue. To predict the level of vibration and noise in buildings caused by railway and road traffic, calculation models are needed. In the present paper, a simplified prediction...... model is briefly described. This prediction model is based on the assumption that the ground and railway embankment can be described in an axisymmetric model, to provide the transfer functions between the track and the free-field. In the paper, the error that arise by assuming axisymmetric response...... is studied by comparing the response in a three-dimensional finite-element model. Transfer functions at several positions in the free-field are compared....

  18. Efficiency at maximum power of motor traffic on networks

    Science.gov (United States)

    Golubeva, N.; Imparato, A.

    2014-06-01

    We study motor traffic on Bethe networks subject to hard-core exclusion for both tightly coupled one-state machines and loosely coupled two-state machines that perform work against a constant load. In both cases we find an interaction-induced enhancement of the efficiency at maximum power (EMP) as compared to noninteracting motors. The EMP enhancement occurs for a wide range of network and single-motor parameters and is due to a change in the characteristic load-velocity relation caused by phase transitions in the system. Using a quantitative measure of the trade-off between the EMP enhancement and the corresponding loss in the maximum output power we identify parameter regimes where motor traffic systems operate efficiently at maximum power without a significant decrease in the maximum power output due to jamming effects.

  19. Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method

    Directory of Open Access Journals (Sweden)

    Yanan Zhang

    2015-01-01

    Full Text Available In recent years, there is growing literature concerning the cascading failure of network characteristics. The object of this paper is to investigate the cascade failures on road traffic network, considering the aeolotropism of road traffic network topology and road congestion dissipation in traffic flow. An improved coupled map lattice (CML model is proposed. Furthermore, in order to match the congestion dissipation, a recovery mechanism is put forward in this paper. With a real urban road traffic network in Beijing, the cascading failures are tested using different attack strategies, coupling strengths, external perturbations, and attacked road segment numbers. The impacts of different aspects on road traffic network are evaluated based on the simulation results. The findings confirmed the important roles that these characteristics played in the cascading failure propagation and dissipation on road traffic network. We hope these findings are helpful to find out the optimal road network topology and avoid cascading failure on road network.

  20. New Heuristic Algorithm for Dynamic Traffic in WDM Optical Networks

    Directory of Open Access Journals (Sweden)

    Arturo Benito Rodríguez Garcia

    2015-12-01

    Full Text Available The results and comparison of the simulation of a new heuristic algorithm called Snake One are presented. The comparison is made with three heuristic algorithms, Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation was made on the WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking, but this causes a relative growth of network utilization. There are also load intervals at which its performance improves, decreasing the number of blocked requests.

  1. Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

    Science.gov (United States)

    Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

    2014-11-01

    Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

  2. Ant colony optimization algorithm for signal coordination of oversaturated traffic networks.

    Science.gov (United States)

    2010-05-01

    Traffic congestion is a daily and growing problem of the modern era in mostly all major cities in the world. : Increasing traffic demand strains the existing transportation system, leading to oversaturated network : conditions, especially at peak hou...

  3. Heterogeneous delivering capability promotes traffic efficiency in complex networks

    Science.gov (United States)

    Zhu, Yan-Bo; Guan, Xiang-Min; Zhang, Xue-Jun

    2015-12-01

    Traffic is one of the most fundamental dynamical processes in networked systems. With the homogeneous delivery capability of nodes, the global dynamic routing strategy proposed by Ling et al. [Phys. Rev. E81, 016113 (2010)] adequately uses the dynamic information during the process and thus it can reach a quite high network capacity. In this paper, based on the global dynamic routing strategy, we proposed a heterogeneous delivery allocation strategy of nodes on scale-free networks with consideration of nodes degree. It is found that the network capacity as well as some other indexes reflecting transportation efficiency are further improved. Our work may be useful for the design of more efficient routing strategies in communication or transportation systems.

  4. Predicting Severity and Duration of Road Traffic Accident

    Directory of Open Access Journals (Sweden)

    Fang Zong

    2013-01-01

    Full Text Available This paper presents a model system to predict severity and duration of traffic accidents by employing Ordered Probit model and Hazard model, respectively. The models are estimated using traffic accident data collected in Jilin province, China, in 2010. With the developed models, three severity indicators, namely, number of fatalities, number of injuries, and property damage, as well as accident duration, are predicted, and the important influences of related variables are identified. The results indicate that the goodness-of-fit of Ordered Probit model is higher than that of SVC model in severity modeling. In addition, accident severity is proven to be an important determinant of duration; that is, more fatalities and injuries in the accident lead to longer duration. Study results can be applied to predictions of accident severity and duration, which are two essential steps in accident management process. By recognizing those key influences, this study also provides suggestive results for government to take effective measures to reduce accident impacts and improve traffic safety.

  5. A model to identify urban traffic congestion hotspots in complex networks

    CERN Document Server

    Solé-Ribalta, Albert; Arenas, Alex

    2016-01-01

    The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real-traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.

  6. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Houli Duan

    2010-01-01

    Full Text Available We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  7. Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network

    Science.gov (United States)

    Houli, Duan; Zhiheng, Li; Yi, Zhang

    2010-12-01

    We propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named multi-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through our model. The simulation results indicated that our algorithm could perform more efficiently than traditional traffic light control methods.

  8. System Would Predictively Preempt Traffic Lights for Emergency Vehicles

    Science.gov (United States)

    Bachelder, Aaron; Foster, Conrad

    2004-01-01

    Two electronic communication-and-control systems have been proposed as means of modifying the switching of traffic lights to give priority to emergency vehicles. Both systems would utilize the inductive loops already installed in the streets of many municipalities to detect vehicles for timing the switching of traffic lights. The proposed systems could be used alone or to augment other automated emergency traffic-light preemption systems that are already present in some municipalities, including systems that recognize flashing lights or siren sounds or that utilize information on the positions of emergency vehicles derived from the Global Positioning System (GPS). Systems that detect flashing lights and siren sounds are limited in range, cannot "see" or "hear" well around corners, and are highly vulnerable to noise. GPS-based systems are effective in rural areas and small cities, but are often ineffective in large cities because of frequent occultation of GPS satellite signals by large structures. In contrast, the proposed traffic-loop forward prediction system would be relatively invulnerable to noise, would not be subject to significant range limitations, and would function well in large cities -- even in such places as underneath bridges and in tunnels, where GPS-based systems do not work. One proposed system has been characterized as "car-active" because each participating emergency vehicle would be equipped with a computer and a radio transceiver that would communicate with stationary transceivers at the traffic loops. The other proposed system has been characterized as "car-passive" because a passive radio transponder would be installed on the underside of a participating vehicle.

  9. Intelligent transportation system real time traffic speed prediction with minimal data

    Directory of Open Access Journals (Sweden)

    Luana Georgescu

    2012-12-01

    Full Text Available Purpose: An Intelligent Transportation System (ITS must be able to predict traffic speed for short time intervals into the future along the branches between the many nodes in a traffic network in near real time using as few observed and stored speed values as possible.  Such predictions support timely ITS reactions to changing traffic conditions such as accidents or volume-induced slowdowns and include re-routing advice and time-to-destination estimations.Design/methodology/approach: Traffic sensors are embedded in the interstate highway system in Detroit, Michigan, USA, and metropolitan area.  The set of sensors used in this project is along interstate highway 75 (I-75 southbound from the intersection with interstate highway 696 (I-696. Data from the sensors including speed, volume, and percent of sensor occupancy, were supplied in one minute intervals by the Michigan Intelligent Transportation Systems Center (MITSC.  Hierarchical linear regression was used to develop a speed prediction model that requires only the current and one previous speed value to predict speed up to 30 minutes in the future.  The model was validated by comparison to collected data with the mean relative error and the median error as the primary metrics.Findings and Originality/value: The model was a better predicator of speed than the minute by minute averages alone.  The relative error between the observed and predicted values was found to range from 5.9% for 1 minute into the future predictions to 10.9% for 30 minutes into the future predictions for the 2006 data set.  The corresponding median errors were 4.0% to 5.4%.  Thus, the predictive capability of the model was deemed sufficient for application.Research limitations/implications: The model has not yet been embedded in an ITS, so a final test of its effectiveness has not been accomplished.Social implications: Travel delays due to traffic incidents, volume induced congestion or other reasons are annoying to

  10. A Unified Monitoring Framework for Energy Consumption and Network Traffic

    Directory of Open Access Journals (Sweden)

    Florentin Clouet

    2015-08-01

    Full Text Available Providing experimenters with deep insight about the effects of their experiments is a central feature of testbeds. In this paper, we describe Kwapi, a framework designed in the context of the Grid'5000 testbed, that unifies measurements for both energy consumption and network traffic. Because all measurements are taken at the infrastructure level (using sensors in power and network equipment, using this framework has no dependencies on the experiments themselves. Initially designed for OpenStack infrastructures, the Kwapi framework allows monitoring and reporting of energy consumption of distributed platforms. In this article, we present the extension of Kwapi to network monitoring, and outline how we overcame several challenges: scaling to a testbed the size of Grid'5000 while still providing high-frequency measurements; providing long-term loss-less storage of measurements; handling operational issues when deploying such a tool on a real infrastructure.

  11. A hybrid queuing strategy for network traffic on scale-free networks

    Science.gov (United States)

    Cai, Kai-Quan; Yu, Lu; Zhu, Yan-Bo

    2017-02-01

    In this paper, a hybrid queuing strategy (HQS) is proposed in traffic dynamics model on scale-free networks, where the delivery priority of packets in the queue is related to their distance to destination and the queue length of next jump. We compare the performance of the proposed HQS with that of the traditional first-in-first-out (FIFO) queuing strategy and the shortest-remaining-path-first (SRPF) queuing strategy proposed by Du et al. It is observed that the network traffic efficiency utilizing HQS with suitable value of parameter h can be further improved in the congestion state. Our work provides new insights for the understanding of the networked-traffic systems.

  12. Effects of traffic generation patterns on the robustness of complex networks

    Science.gov (United States)

    Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

    2018-02-01

    Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

  13. Traffic Steering Framework for Mobile-Assisted Resource Management in Heterogeneous Networks

    DEFF Research Database (Denmark)

    Dogadaev, Anton Konstantinovich; Checko, Aleksandra; Popovska Avramova, Andrijana

    2013-01-01

    With the expected growth of mobile data traffic it is essential to manage the network resources efficiently. In order to undertake this challenge, we propose a framework for network-centric, mobile-assisted resource management, which facilitates traffic offloading from mobile network to Wi...

  14. EFFECTIVE BANDWIDTH FOR SELF-SIMILAR TRAFFIC IN ATM NETWORK

    Directory of Open Access Journals (Sweden)

    Linawati Linawati

    2009-05-01

    Full Text Available This paper proposes a new approach to estimate the effective bandwidth for self-similar traffic in ATM network. In this approach we use the tail distribution of queue length based on FBM model. This approach is derived from the inequalities for Mills’ ratio. Then a comparison with Norros and Trinh&Miki schemes are analysed. The results demonstrate reasonable agreement between numerical and simulation results and between all schemes. Their bandwidth estimation tends closer for CLP improvement.

  15. Discovering vesicle traffic network constraints by model checking.

    Science.gov (United States)

    Shukla, Ankit; Bhattacharyya, Arnab; Kuppusamy, Lakshmanan; Srivas, Mandayam; Thattai, Mukund

    2017-01-01

    A eukaryotic cell contains multiple membrane-bound compartments. Transport vesicles move cargo between these compartments, just as trucks move cargo between warehouses. These processes are regulated by specific molecular interactions, as summarized in the Rothman-Schekman-Sudhof model of vesicle traffic. The whole structure can be represented as a transport graph: each organelle is a node, and each vesicle route is a directed edge. What constraints must such a graph satisfy, if it is to represent a biologically realizable vesicle traffic network? Graph connectedness is an informative feature: 2-connectedness is necessary and sufficient for mass balance, but stronger conditions are required to ensure correct molecular specificity. Here we use Boolean satisfiability (SAT) and model checking as a framework to discover and verify graph constraints. The poor scalability of SAT model checkers often prevents their broad application. By exploiting the special structure of the problem, we scale our model checker to vesicle traffic systems with reasonably large numbers of molecules and compartments. This allows us to test a range of hypotheses about graph connectivity, which can later be proved in full generality by other methods.

  16. Control of Networked Traffic Flow Distribution - A Stochastic Distribution System Perspective

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Hong [Pacific Northwest National Laboratory (PNNL); Aziz, H M Abdul [ORNL; Young, Stan [National Renewable Energy Laboratory (NREL); Patil, Sagar [Pacific Northwest National Laboratory (PNNL)

    2017-10-01

    Networked traffic flow is a common scenario for urban transportation, where the distribution of vehicle queues either at controlled intersections or highway segments reflect the smoothness of the traffic flow in the network. At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic model for a one-way single-lane corridor traffic system based on theory of stochastic distribution control.. It has been shown that the developed stochastic model would provide the optimal probability density function (PDF) of the traffic queueing length as a dynamic function of the traffic signal setting parameters. Based upon such a stochastic distribution model, we have proposed a preliminary closed loop framework on stochastic distribution control for the traffic queueing system to make the traffic queueing length PDF follow a target PDF that potentially realizes the smooth traffic flow distribution in a concerned corridor.

  17. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  18. Dynamic traffic grooming for port number optimization in WDM optical mesh networks

    Science.gov (United States)

    Huang, Jun; Zeng, Qingji; Liu, Jimin; Xiao, Pengcheng; Liu, Hua; Xiao, Shilin

    2004-04-01

    In this paper, the objective was optimizing the port number with dynamic traffic grooming of SDH/SONET WDM mesh networks to give useful referenced data to networks design and the cost control of networks. The performances of different path select routing algorithms were evaluated in WDM grooming networks by considering traffic of different bandwidth requests. Finally, the results were presented and compared with in distributed-controlled WDM mesh networks.

  19. Radio resource management for mobile traffic offloading in heterogeneous cellular networks

    CERN Document Server

    Wu, Yuan; Huang, Jianwei; Shen, Xuemin (Sherman)

    2017-01-01

    This SpringerBrief offers two concrete design examples for traffic offloading. The first is an optimal resource allocation for small-cell based traffic offloading that aims at minimizing mobile users’ data cost. The second is an optimal resource allocation for device-to-device assisted traffic offloading that also minimizes the total energy consumption and cellular link usage (while providing an overview of the challenging issues). Both examples illustrate the importance of proper resource allocation to the success of traffic offloading, show the consequent performance advantages of executing optimal resource allocation, and present the methodologies to achieve the corresponding optimal offloading solution for traffic offloading in heterogeneous cellular networks. The authors also include an overview of heterogeneous cellular networks and explain different traffic offloading paradigms ranging from uplink traffic offloading through small cells to downlink traffic offloading via mobile device-to-device cooper...

  20. Intelligent Controlling Simulation of Traffic Flow in a Small City Network

    Science.gov (United States)

    Fouladvand, M. Ebrahim; Shaebani, M. Reza; Sadjadi, Zeinab

    2004-11-01

    We propose a two dimensional probabilistic cellular automata for the description of traffic flow in a small city network composed of two intersections. The traffic in the network is controlled by a set of traffic lights which can be operated both in fixed-time and a traffic responsive manner. Vehicular dynamics is simulated and the total delay experienced by the traffic is evaluated within specified time intervals. We investigate both decentralized and centralized traffic responsive schemes and in particular discuss the implementation of the green-wave strategy. Our investigations prove that the network delay strongly depends on the signalisation strategy. We show that in some traffic conditions, the application of the green-wave scheme may destructively lead to the increment of the global delay.

  1. A Comparison of Techniques for Reducing Unicast Traffic in HSR Networks

    Directory of Open Access Journals (Sweden)

    Nguyen Xuan Tien

    2015-10-01

    Full Text Available This paper investigates several existing techniques for reducing high-availability seamless redundancy (HSR unicast traffic in HSR networks for substation automation systems (SAS. HSR is a redundancy protocol for Ethernet networks that provides duplicate frames for separate physical paths with zero recovery time. This feature of HSR makes it very suited for real-time and mission-critical applications such as SAS systems. HSR is one of the redundancy protocols selected for SAS systems. However, the standard HSR protocol generates too much unnecessary redundant unicast traffic in connected-ring networks. This drawback degrades network performance and may cause congestion and delay. Several techniques have been proposed to reduce the redundant unicast traffic, resulting in the improvement of network performance in HSR networks. These HSR traffic reduction techniques are broadly classified into two categories based on their traffic reduction manner, including traffic filtering-based techniques and predefined path-based techniques. In this paper, we provide an overview and comparison of these HSR traffic reduction techniques found in the literature. The concepts, operational principles, network performance, advantages, and disadvantages of these techniques are investigated, summarized. We also provide a comparison of the traffic performance of these HSR traffic reduction techniques.

  2. Understanding characteristics in multivariate traffic flow time series from complex network structure

    Science.gov (United States)

    Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

    2017-07-01

    Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

  3. Model for Detection and Classification of DDoS Traffic Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    D. Peraković

    2017-06-01

    Full Text Available Detection of DDoS (Distributed Denial of Service traffic is of great importance for the availability protection of services and other information and communication resources. The research presented in this paper shows the application of artificial neural networks in the development of detection and classification model for three types of DDoS attacks and legitimate network traffic. Simulation results of developed model showed accuracy of 95.6% in classification of pre-defined classes of traffic.

  4. An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks

    OpenAIRE

    Qin Yu; Lyu Jibin; Lirui Jiang

    2016-01-01

    Traffic anomaly detection is emerging as a necessary component as wireless networks gain popularity. In this paper, based on the improved Autoregressive Integrated Moving Average (ARIMA) model, we propose a traffic anomaly detection algorithm for wireless sensor networks (WSNs) which considers the particular imbalanced, nonstationary properties of the WSN traffic and the limited energy and computing capacity of the wireless sensors at the same time. We systematically analyze the characteristi...

  5. On the existence of efficient solutions to vector optimization problem of traffic flow on network

    Directory of Open Access Journals (Sweden)

    T. A. Bozhanova

    2009-09-01

    Full Text Available We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also proved.

  6. On the existence of efficient solutions to vector optimization problem of traffic flow on network

    OpenAIRE

    T. A. Bozhanova

    2009-01-01

    We studied traffic flow models in vector-valued optimization statement where the flow is controlled at the nodes of network. We considered the case when an objective mapping possesses a weakened property of upper semicontinuity and made no assumptions on the interior of the ordering cone. The sufficient conditions for the existence of efficient controls of the traffic problems are derived. The existence of efficient solutions of vector optimization problem for traffic flow on network are also...

  7. Traffic Engineering of Peer-Assisted Content Delivery Network with Content-Oriented Incentive Mechanism

    National Research Council Canada - National Science Library

    MAKI, Naoya; NISHIO, Takayuki; SHINKUMA, Ryoichi; MORI, Tatsuya; KAMIYAMA, Noriaki; KAWAHARA, Ryoichi; TAKAHASHI, Tatsuro

    2012-01-01

    In content services where people purchase and download large-volume contents, minimizing network traffic is crucial for the service provider and the network operator since they want to lower the cost...

  8. Trading network predicts stock price.

    Science.gov (United States)

    Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

    2014-01-16

    Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

  9. Feasibility of Optical Packet Switched WDM Networks without Packet Synchronisation Under Bursty Traffic Conditions

    DEFF Research Database (Denmark)

    Fjelde, Tina; Hansen, Peter Bukhave; Kloch, Allan

    1999-01-01

    We show that complex packet synchronisation may be avoided in optical packetswitched networks. Detailed traffic analysis demonstrates that packet lossratios of 1e-10 are feasible under bursty traffic conditions for a highcapacity network consisting of asynchronously operated add-drop switch nodes...

  10. Time-delay neural network for audio monitoring of road traffic and vehicle classification

    Science.gov (United States)

    Nooralahiyan, Amir Y.; Lopez, Louis; Mckewon, Denis; Ahmadi, Masoud

    1997-02-01

    The aim of this research is to investigate the feasibility of developing a cost effective traffic monitoring detector for the purpose of reliable on-line vehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT recorder. The digital signal was preprocessed by LPC (Linear Predictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signature. The network was trained and tested with real data for four types of vehicles. The paper provides a description of the TDNN architecture and training algorithm and an overview of the LPC pre-processing and feature extraction technique as applied to audio monitoring of road traffic. The performance of TDNN vehicle classification, convergence and accuracy for the training patterns are fully illustrated. In generalizing to a limited number of test patterns available, 100% accuracy in classification was achieved. The net was also robust to changes in the starting position of the acoustic waveforms with 86% accuracy for the same test data set.

  11. MULTI-LEVEL NETWORK RESILIENCE: TRAFFIC ANALYSIS, ANOMALY DETECTION AND SIMULATION

    Directory of Open Access Journals (Sweden)

    Angelos Marnerides

    2011-06-01

    Full Text Available Traffic analysis and anomaly detection have been extensively used to characterize network utilization as well as to identify abnormal network traffic such as malicious attacks. However, so far, techniques for traffic analysis and anomaly detection have been carried out independently, relying on mechanisms and algorithms either in edge or in core networks alone. In this paper we propose the notion of multi-level network resilience, in order to provide a more robust traffic analysis and anomaly detection architecture, combining mechanisms and algorithms operating in a coordinated fashion both in the edge and in the core networks. This work is motivated by the potential complementarities between the research being developed at IIT Madras and Lancaster University. In this paper we describe the current work being developed at IIT Madras and Lancaster on traffic analysis and anomaly detection, and outline the principles of a multi-level resilience architecture.

  12. Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations

    KAUST Repository

    Canepa, Edward S.

    2017-06-19

    Nowadays, traffic management has become a challenge for urban areas, which are covering larger geographic spaces and facing the generation of different kinds of traffic data. This article presents a robust traffic estimation framework for highways modeled by a system of Lighthill Whitham Richards equations that is able to assimilate different sensor data available. We first present an equivalent formulation of the problem using a Hamilton–Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton–Jacobi equation are linear ones. We then pose the problem of estimating the traffic density given incomplete and inaccurate traffic data as a Mixed Integer Program. We then extend the density estimation framework to highway networks with any available data constraint and modeling junctions. Finally, we present a travel estimation application for a small network using real traffic measurements obtained obtained during Mobile Century traffic experiment, and comparing the results with ground truth data.

  13. Systematic Hybrid Network Scheduling for Multiple Traffic Classes with Host Timing and Phase Constraints

    Science.gov (United States)

    Varadarajan, Srivatsan (Inventor); Hall, Brendan (Inventor); Smithgall, William Todd (Inventor); Bonk, Ted (Inventor); DeLay, Benjamin F. (Inventor)

    2017-01-01

    Systems and methods for systematic hybrid network scheduling for multiple traffic classes with host timing and phase constraints are provided. In certain embodiments, a method of scheduling communications in a network comprises scheduling transmission of virtual links pertaining to a first traffic class on a global schedule to coordinate transmission of the virtual links pertaining to the first traffic class across all transmitting end stations on the global schedule; and scheduling transmission of each virtual link pertaining to a second traffic class on a local schedule of the respective transmitting end station from which each respective virtual link pertaining to the second traffic class is transmitted such that transmission of each virtual link pertaining to the second traffic class is coordinated only at the respective end station from which each respective virtual link pertaining to the second traffic class is transmitted.

  14. File Detection On Network Traffic Using Approximate Matching

    Directory of Open Access Journals (Sweden)

    Frank Breitinger

    2014-09-01

    Full Text Available In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial.Within this paper we present a new technique for network trac analysis based on approximate matching (a.k.a fuzzy hashing which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive conguration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10-4 and 10-5 and an algorithm throughput of over 650 Mbit/s.

  15. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  16. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Forecasting of Congestion in Traffic Neural Network Modelling Using Duffing Holmes Oscillator

    Science.gov (United States)

    Mrgole, Anamarija L.; Čelan, Marko; Mesarec, Beno

    2017-10-01

    Forecasting of congestion in traffic with Neural Network is an innovative and new process of identification and detection of chaotic features in time series analysis. With the use of Duffing Holmes Oscillator, we estimate the emergence of traffic flow congestion when the traffic load on a specific section of the road and in a specific time period is close to exceeding the capacity of the road infrastructure. The orientated model is validated in six locations with a specific requirement. The paper points out the issue of importance of traffic flow forecasting and simulations for preventing or rerouting possible short term traffic flow congestions.

  18. A computer model to predict traffic noise in urban situations under free flow and traffic light conditions

    Science.gov (United States)

    Jacobs, L. J. M.; Nijs, L.; van Willigenburg, J. J.

    1980-10-01

    A computer model is presented for predicting traffic noise indices in built-up situations for free flow traffic conditions and for a flow interrupted by a traffic light. The stream of vehicles is simulated by a given time headway distribution, and a transfer function obtained from a 1 : 100 scale model is used to simulate the specific built-up situation. Different time headway distributions result in only very small discrepancies; even the simple "equally spaced" distribution is adequate for predicting noise indices with high accuracy, unless L90 has to be predicted. In eight built-up situations along a road with freely flowing traffic only minor mutual differences are found when L1 - Leq and L10 - Leq are compared, but L50 and L90, and consequently TNI and Lnp, show discrepancies of the order of 10 dB(A). If a traffic light is introduced the value of Leq rises compared with the free flow case, and the values of L1 and L10 increase, especially at higher traffic intensities, while L50 and L90 decrease. If the noise indices are calculated as a function of the distance along the road to the traffic light increases in L1, L10 and Leq are found at about 50 m beyond the traffic light. The principal cause for this increase appears to be the differences between the peak levels of an accelerating car and the sound level at the ultimate speed. More in situ measurements are required to test the accuracy of the model, especially for accelerating vehicles.

  19. Model Predictive Control for Integrating Traffic Control Measures

    NARCIS (Netherlands)

    Hegyi, A.

    2004-01-01

    Dynamic traffic control measures, such as ramp metering and dynamic speed limits, can be used to better utilize the available road capacity. Due to the increasing traffic volumes and the increasing number of traffic jams the interaction between the control measures has increased such that local

  20. Prediction of traffic fatalities and prospects for mobility becoming ...

    Indian Academy of Sciences (India)

    road traffic fatalities for developing countries is used, based on the relationships of income level per capita with road traffic mortality. Also this model implies that at some point in time road traffic deaths will start declining for ever, also worldwide. After empirically derived corrections for missing or incomplete data and police.

  1. Impact of Bimodal Traffic on Latency in Optical Burst Switching Networks

    Directory of Open Access Journals (Sweden)

    Yuhua Chen

    2008-01-01

    Full Text Available This paper analyzes the impact of bimodal traffic composition on latency in optical burst switching networks. In particular, it studies the performance degradation to short-length packets caused by longer packets, both of which are part of a heterogeneous traffic model. The paper defines a customer satisfaction index for each of the classes of traffic, and a composite satisfaction index. The impact of higher overall utilization of the network as well as that of the ratio of the traffic mix on each of the customer satisfaction indices is specifically addressed.

  2. Comparative study about social support network among familiar physicians and traffic officers, México

    OpenAIRE

    Aranda B., Carolina; Instituto de Investigación en Salud Ocupacional, Universidad de Guadalajara, México; Torres L., Teresa; Instituto de Investigación en Salud Ocupacional, Universidad de Guadalajara, México; Salazar E., José; Instituto de Investigación en Salud Ocupacional, Universidad de Guadalajara, México; Pando M., Manuel; Instituto de Investigación en Salud Ocupacional, Universidad de Guadalajara, México; Aldrete R., María Guadalupe; Instituto de Investigación en Salud Ocupacional, Universidad de Guadalajara, México

    2014-01-01

    The social support is the process that occurs between people that make up the social network of a subject. Actions such as listening, estimate, assess, and so on, are behaviors that occur among individuals who make up the network. The aim of this study analyze the situation of social support and social support networks on family physicians and traffic agents of a city in Mexico. 197 physicians and 875 traffic agents participated voluntarily with an informed consent. The information was collec...

  3. Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

    OpenAIRE

    Diogo Santos; José Pinto; Rossetti, Rosaldo J. F.; Eugénio Oliveira

    2016-01-01

    Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particul...

  4. Next generation network based carrier ethernet test bed for IPTV traffic

    DEFF Research Database (Denmark)

    Fu, Rong; Berger, Michael Stübert; Zheng, Yu

    2009-01-01

    This paper presents a Carrier Ethernet (CE) test bed based on the Next Generation Network (NGN) framework. After the concept of CE carried out by Metro Ethernet Forum (MEF), the carrier-grade Ethernet are obtaining more and more interests and being investigated as the low cost and high performance...... services of transport network to carry the IPTV traffic. This test bed is approaching to support the research on providing a high performance carrier-grade Ethernet transport network for IPTV traffic....

  5. Traffic modeling in the integrated cellular ad hoc network system

    Science.gov (United States)

    Yamanaka, Sachiko; Shimohara, Katsunori

    2005-10-01

    We present the modeling and evaluation in the integrated cellular and ad hoc network system. The system is modeled using queueing theory and we derive some characteristic values. As regards a system model of two cells, M channels are assigned to each cell and a relay station is set in the overlapped area of two cells. New calls in cellA can be relayed to cellB if the channels in cellA are all busy and the mobile stations are in the covered area by a relay station. Handoff calls select the channels of the cell that the number of empty channels are more in the two cells. If the channels of the both cells are all busy, handoff calls can wait in a queue with the capacity Q while mobile stations are in the handoff area. However, so as not to make handoff calls possess channels prior to new calls, we manage it with the method being different from the other researches. This system is more flexible than cellular networks, the bias of traffic gets smaller and it leads to an efficient channel using. We model and evaluate our system by assuming that the dwelling time is distributed with non-exponential distribution as well as exponential one. In numerical results, we compare the characteristic values in our system with those in non-relaying system, see how the characteristic values are affected when the covered area by a relay station changes, and verify the effectiveness of our system.

  6. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    Science.gov (United States)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  7. Spatio-temporal propagation of traffic jams in urban traffic networks

    OpenAIRE

    Jiang, Yinan; Kang, Rui; Li, Daqing; Guo, Shengmin; Havlin, Shlomo

    2017-01-01

    Since the first reported traffic jam about a century ago, traffic congestion has been intensively studied with various methods ranging from macroscopic to microscopic viewpoint. However, due to the population growth and fast civilization, traffic congestion has become significantly worse not only leading to economic losses, but also causes environment damages. Without understanding of jams spatio-temporal propagation behavior in a city, it is impossible to develop efficient mitigation strateg...

  8. Robust and Agile System against Fault and Anomaly Traffic in Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Mihui Kim

    2017-03-01

    Full Text Available The main advantage of software defined networking (SDN is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three main modules in the SDN controller for a robust and agile defense (RAD system against network anomalies: a traffic analyzer, a traffic engineer, and a rule manager. RAD provides reactive flow rule generation to control traffic while detecting network failures, anomalies, high traffic volume (elephant flows, and attacks. The traffic analyzer identifies elephant flows, traffic anomalies, and attacks based on attack signatures and network monitoring. The traffic engineer module measures network utilization and delay in order to determine the best path for multi-dimensional routing and load balancing under any circumstances. Finally, the rule manager generates and installs a flow rule for the selected best path to control traffic. We implement the proposed RAD system based on Floodlight, an open source project for the SDN controller. We evaluate our system using simulation with and without the aforementioned RAD modules. Experimental results show that our approach is both practical and feasible, and can successfully augment an existing SDN controller in terms of agility, robustness, and efficiency, even in the face of link failures, attacks, and elephant flows.

  9. ENTVis: A Visual Analytic Tool for Entropy-Based Network Traffic Anomaly Detection.

    Science.gov (United States)

    Zhou, Fangfang; Huang, Wei; Zhao, Ying; Shi, Yang; Liang, Xing; Fan, Xiaoping

    2015-01-01

    Entropy-based traffic metrics have received substantial attention in network traffic anomaly detection because entropy can provide fine-grained metrics of traffic distribution characteristics. However, some practical issues--such as ambiguity, lack of detailed distribution information, and a large number of false positives--affect the application of entropy-based traffic anomaly detection. In this work, we introduce a visual analytic tool called ENTVis to help users understand entropy-based traffic metrics and achieve accurate traffic anomaly detection. ENTVis provides three coordinated views and rich interactions to support a coherent visual analysis on multiple perspectives: the timeline group view for perceiving situations and finding hints of anomalies, the Radviz view for clustering similar anomalies in a period, and the matrix view for understanding traffic distributions and diagnosing anomalies in detail. Several case studies have been performed to verify the usability and effectiveness of our method. A further evaluation was conducted via expert review.

  10. Stochastic Model of Traffic Jam and Traffic Signal Control

    Science.gov (United States)

    Shin, Ji-Sun; Cui, Cheng-You; Lee, Tae-Hong; Lee, Hee-Hyol

    Traffic signal control is an effective method to solve the traffic jam. and forecasting traffic density has been known as an important part of the Intelligent Transportation System (ITS). The several methods of the traffic signal control are known such as random walk method, Neuron Network method, Bayesian Network method, and so on. In this paper, we propose a new method of a traffic signal control using a predicted distribution of traffic jam based on a Dynamic Bayesian Network model. First, a forecasting model to predict a probabilistic distribution of the traffic jam during each period of traffic lights is built. As the forecasting model, the Dynamic Bayesian Network is used to predict the probabilistic distribution of a density of the traffic jam. According to measurement of two crossing points for each cycle, the inflow and outflow of each direction and the number of standing vehicles at former cycle are obtained. The number of standing vehicle at k-th cycle will be calculated synchronously. Next, the probabilistic distribution of the density of standing vehicle in each cycle will be predicted using the Dynamic Bayesian Network constructed for the traffic jam. And then a control rule to adjust the split and the cycle to increase the probability between a lower limit and ceiling of the standing vehicles is deduced. As the results of the simulation using the actual traffic data of Kitakyushu city, the effectiveness of the method is shown.

  11. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  12. Path sets size, model specification, or model estimation: Which one matters most in predicting stochastic user equilibrium traffic flow?

    OpenAIRE

    Haghani, Milad; Shahhoseini, Zahra; Sarvi, Majid

    2016-01-01

    This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to address is the extent to which the size of generated path sets can affect the prediction of the static flow in the path-based traffic assignment paradigm. The importance of this question arises from the fact that the need to generate a large quantity of paths may be perceived by analys...

  13. A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

    Directory of Open Access Journals (Sweden)

    Parisa Bazmi

    2016-11-01

    Full Text Available Named Data Networking (NDN is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN Based Traffic-aware Forwarding strategy (NNTF is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

  14. Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

    Directory of Open Access Journals (Sweden)

    Natalia Andrienko

    2015-04-01

    Full Text Available By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.

  15. High-speed and high-fidelity system and method for collecting network traffic

    Science.gov (United States)

    Weigle, Eric H [Los Alamos, NM

    2010-08-24

    A system is provided for the high-speed and high-fidelity collection of network traffic. The system can collect traffic at gigabit-per-second (Gbps) speeds, scale to terabit-per-second (Tbps) speeds, and support additional functions such as real-time network intrusion detection. The present system uses a dedicated operating system for traffic collection to maximize efficiency, scalability, and performance. A scalable infrastructure and apparatus for the present system is provided by splitting the work performed on one host onto multiple hosts. The present system simultaneously addresses the issues of scalability, performance, cost, and adaptability with respect to network monitoring, collection, and other network tasks. In addition to high-speed and high-fidelity network collection, the present system provides a flexible infrastructure to perform virtually any function at high speeds such as real-time network intrusion detection and wide-area network emulation for research purposes.

  16. Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS) Networks with Ensuring the Fairness for Other Traffics.

    Science.gov (United States)

    Al-Shargabi, Mohammed A; Shaikh, Asadullah; Ismail, Abdulsamad S

    2016-01-01

    Optical burst switching (OBS) networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS) for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS' QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR) scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate) ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50-60%, 30-40%, and 10-20% for high, normal, and low traffic loads respectively.

  17. Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS Networks with Ensuring the Fairness for Other Traffics.

    Directory of Open Access Journals (Sweden)

    Mohammed A Al-Shargabi

    Full Text Available Optical burst switching (OBS networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS' QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50-60%, 30-40%, and 10-20% for high, normal, and low traffic loads respectively.

  18. Traffic properties for stochastic routings on scale-free networks

    CERN Document Server

    Hayashi, Yukio

    2011-01-01

    For realistic scale-free networks, we investigate the traffic properties of stochastic routing inspired by a zero-range process known in statistical physics. By parameters $\\alpha$ and $\\delta$, this model controls degree-dependent hopping of packets and forwarding of packets with higher performance at more busy nodes. Through a theoretical analysis and numerical simulations, we derive the condition for the concentration of packets at a few hubs. In particular, we show that the optimal $\\alpha$ and $\\delta$ are involved in the trade-off between a detour path for $\\alpha 0$; In the low-performance regime at a small $\\delta$, the wandering path for $\\alpha 0$ and $\\alpha < 0$ is small, neither the wandering long path with short wait trapped at nodes ($\\alpha = -1$), nor the short hopping path with long wait trapped at hubs ($\\alpha = 1$) is advisable. A uniformly random walk ($\\alpha = 0$) yields slightly better performance. We also discuss the congestion phenomena in a more complicated situation with pack...

  19. Minimal-Intrusion Traffic Monitoring And Analysis In Mission-Critical Communication Networks

    Directory of Open Access Journals (Sweden)

    Alberto Domingo Ajenjo

    2003-10-01

    Full Text Available A good knowledge of expected and actual traffic patterns is an essential tool for network planning, design and operation in deployed, mission-critical applications. This paper describes those needs, and explains the Traffic Monitoring and Analysis Platform (TMAP concept, as developed in support of NATO deployed military headquarters Communications and Information Systems. It shows how a TMAP was deployed to a real NATO exercise, to prove the concept and baseline the traffic needs per application, per user community and per time of day. Then, it analyses the obtained results and derives conclusions on how to integrate traffic monitoring and analysis platforms in future deployments.

  20. Entropy Based Analysis of DNS Query Traffic in the Campus Network

    Directory of Open Access Journals (Sweden)

    Dennis Arturo Ludeña Romaña

    2008-10-01

    Full Text Available We carried out the entropy based study on the DNS query traffic from the campus network in a university through January 1st, 2006 to March 31st, 2007. The results are summarized, as follows: (1 The source IP addresses- and query keyword-based entropies change symmetrically in the DNS query traffic from the outside of the campus network when detecting the spam bot activity on the campus network. On the other hand (2, the source IP addresses- and query keywordbased entropies change similarly each other when detecting big DNS query traffic caused by prescanning or distributed denial of service (DDoS attack from the campus network. Therefore, we can detect the spam bot and/or DDoS attack bot by only watching DNS query access traffic.

  1. Developing a New HSR Switching Node (SwitchBox for Improving Traffic Performance in HSR Networks

    Directory of Open Access Journals (Sweden)

    Nguyen Xuan Tien

    2016-01-01

    Full Text Available High availability is crucial for industrial Ethernet networks as well as Ethernet-based control systems such as automation networks and substation automation systems (SAS. Since standard Ethernet does not support fault tolerance capability, the high availability of Ethernet networks can be increased by using redundancy protocols. Various redundancy protocols for Ethernet networks have been developed and standardized, such as rapid spanning tree protocol (RSTP, media redundancy protocol (MRP, parallel redundancy protocol (PRP, high-availability seamless redundancy (HSR and others. RSTP and MRP have switchover delay drawbacks. PRP provides zero recovery time, but requires a duplicate network infrastructure. HSR operation is similar to PRP, but HSR uses a single network. However, the standard HSR protocol is mainly applied to ring-based topologies and generates excessively unnecessary redundant traffic in the network. In this paper, we develop a new switching node for the HSR protocol, called SwitchBox, which is used in HSR networks in order to support any network topology and significantly reduce redundant network traffic, including unicast, multicast and broadcast traffic, compared with standard HSR. By using the SwitchBox, HSR not only provides seamless communications with zero switchover time in case of failure, but it is also easily applied to any network topology and significantly reduces unnecessary redundant traffic in HSR networks.

  2. COMPARISON OF TREND PROJECTION METHODS AND BACKPROPAGATION PROJECTIONS METHODS TREND IN PREDICTING THE NUMBER OF VICTIMS DIED IN TRAFFIC ACCIDENT IN TIMOR TENGAH REGENCY, NUSA TENGGARA

    Directory of Open Access Journals (Sweden)

    Aleksius Madu

    2016-10-01

    Full Text Available The purpose of this study is to predict the number of traffic accident victims who died in Timor Tengah Regency with Trend Projection method and Backpropagation method, and compare the two methods based on the degree of guilt and predict the number traffic accident victims in the Timor Tengah Regency for the coming year. This research was conducted in Timor Tengah Regency where data used in this study was obtained from Police Unit in Timor Tengah Regency. The data is on the number of traffic accidents in Timor Tengah Regency from 2000 – 2013, which is obtained by a quantitative analysis with Trend Projection and Backpropagation method. The results of the data analysis predicting the number of traffic accidents victims using Trend Projection method obtained the best model which is the quadratic trend model with equation Yk = 39.786 + (3.297 X + (0.13 X2. Whereas by using back propagation method, it is obtained the optimum network that consists of 2 inputs, 3 hidden screens, and 1 output. Based on the error rates obtained, Back propagation method is better than the Trend Projection method which means that the predicting accuracy with Back propagation method is the best method to predict the number of traffic accidents victims in Timor Tengah Regency. Thus obtained predicting the numbers of traffic accident victims for the next 5 years (Years 2014-2018 respectively - are 106 person, 115 person, 115 person, 119 person and 120 person.   Keywords: Trend Projection, Back propagation, Predicting.

  3. Traffic Control Algorithm Offering Multi-Class Fairness in PON Based Access Networks

    Science.gov (United States)

    Okumura, Yasuyuki

    This letter proposes a dynamic bandwidth allocation algorithm for access networks based PON (Passive Optical Network). It considers the mixture of transport layer protocols when responding to traffic congestion at the SNI (Service Node Interface). Simulations on a mixture of TCP (Transmission Control Protocol), and UDP (User Datagram Protocol) traffic flows show that the algorithm increases the throughput of TCP, improves the fairness between the two protocols, and solves the congestion problem at the SNI.

  4. Self-control of traffic lights and vehicle flows in urban road networks

    Science.gov (United States)

    Lämmer, Stefan; Helbing, Dirk

    2008-04-01

    Based on fluid-dynamic and many-particle (car-following) simulations of traffic flows in (urban) networks, we study the problem of coordinating incompatible traffic flows at intersections. Inspired by the observation of self-organized oscillations of pedestrian flows at bottlenecks, we propose a self-organization approach to traffic light control. The problem can be treated as a multi-agent problem with interactions between vehicles and traffic lights. Specifically, our approach assumes a priority-based control of traffic lights by the vehicle flows themselves, taking into account short-sighted anticipation of vehicle flows and platoons. The considered local interactions lead to emergent coordination patterns such as 'green waves' and achieve an efficient, decentralized traffic light control. While the proposed self-control adapts flexibly to local flow conditions and often leads to non-cyclical switching patterns with changing service sequences of different traffic flows, an almost periodic service may evolve under certain conditions and suggests the existence of a spontaneous synchronization of traffic lights despite the varying delays due to variable vehicle queues and travel times. The self-organized traffic light control is based on an optimization and a stabilization rule, each of which performs poorly at high utilizations of the road network, while their proper combination reaches a superior performance. The result is a considerable reduction not only in the average travel times, but also of their variation. Similar control approaches could be applied to the coordination of logistic and production processes.

  5. Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy.

    Science.gov (United States)

    Su, Jason G; Jerrett, Michael; Beckerman, Bernardo; Wilhelm, Michelle; Ghosh, Jo Kay; Ritz, Beate

    2009-08-01

    Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOX) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NOX concentrations. A novel model selection strategy known as "ADDRESS" (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance. Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NOX concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87-91%. Remote sensing-derived variables were significantly correlated with NOX concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., > 5000 m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region.

  6. Optimization of a Traffic Control Scheme for a Post-Disaster Urban Road Network

    Directory of Open Access Journals (Sweden)

    Zengzhen Shao

    2017-12-01

    Full Text Available Traffic control of urban road networks during emergency rescues is conducive to rapid rescue in the affected areas. However, excessive control will lead to negative impacts on the normal traffic order. We propose a novel model to optimize the traffic control scheme during the post-disaster emergency rescue period named PD-TCM (post-disaster traffic control model. In this model, the vertex and edge betweenness indexes of urban road networks are introduced to evaluate the controllability of the road sections. The gravity field model is also used to adjust the travel time function of different road sections in the control and diverging domains. Experimental results demonstrate that the proposed model can obtain the optimal traffic control scheme efficiently, which gives it the ability to meet the demand of emergency rescues as well as reducing the disturbances caused by controls.

  7. Real time network traffic monitoring for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    A wireless local area network (WLAN) is an important type of wireless networks which connotes different wireless nodes in a local area network. WLANs suffer from important problems such as network load balancing, large amount of energy, and load of sampling. This paper presents a new networking traffic approach based on Compressed Sensing (CS) for improving the quality of WLANs. The proposed architecture allows reducing Data Delay Probability (DDP) to 15%, which is a good record for WLANs. The proposed architecture is increased Data Throughput (DT) to 22 % and Signal to Noise (S/N) ratio to 17 %, which provide a good background for establishing high qualified local area networks. This architecture enables continuous data acquisition and compression of WLAN's signals that are suitable for a variety of other wireless networking applications. At the transmitter side of each wireless node, an analog-CS framework is applied at the sensing step before analog to digital converter in order to generate the compressed version of the input signal. At the receiver side of wireless node, a reconstruction algorithm is applied in order to reconstruct the original signals from the compressed signals with high probability and enough accuracy. The proposed algorithm out-performs existing algorithms by achieving a good level of Quality of Service (QoS). This ability allows reducing 15 % of Bit Error Rate (BER) at each wireless node.

  8. Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

    Directory of Open Access Journals (Sweden)

    Anamarija L. Mrgole

    2017-02-01

    Full Text Available The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.

  9. Prediction horizon effects on stochastic modelling hints for neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Drossu, R.; Obradovic, Z. [Washington State Univ., Pullman, WA (United States)

    1995-12-31

    The objective of this paper is to investigate the relationship between stochastic models and neural network (NN) approaches to time series modelling. Experiments on a complex real life prediction problem (entertainment video traffic) indicate that prior knowledge can be obtained through stochastic analysis both with respect to an appropriate NN architecture as well as to an appropriate sampling rate, in the case of a prediction horizon larger than one. An improvement of the obtained NN predictor is also proposed through a bias removal post-processing, resulting in much better performance than the best stochastic model.

  10. A Network Traffic Generator Model for Fast Network-on-Chip Simulation

    DEFF Research Database (Denmark)

    Mahadevan, Shankar; Angiolini, Frederico; Storgaard, Michael

    2005-01-01

    and effective Network-on-Chip (NoC) development and debugging environment. By capturing the type and the timestamp of communication events at the boundary of an IP core in a reference environment, the TG can subsequently emulate the core's communication behavior in different environments. Access patterns......For Systems-on-Chip (SoCs) development, a predominant part of the design time is the simulation time. Performance evaluation and design space exploration of such systems in bit- and cycle-true fashion is becoming prohibitive. We propose a traffic generation (TG) model that provides a fast...

  11. Network Traffic Generator Model for Fast Network-on-Chip Simulation

    DEFF Research Database (Denmark)

    Mahadevan, Shankar; Ang, Frederico; Olsen, Rasmus G.

    2008-01-01

    and effective Network-on-Chip (NoC) development and debugging environment. By capturing the type and the timestamp of communication events at the boundary of an IP core in a reference environment, the TG can subsequently emulate the core's communication behavior in different environments. Access patterns......For Systems-on-Chip (SoCs) development, a predominant part of the design time is the simulation time. Performance evaluation and design space exploration of such systems in bit- and cycle-true fashion is becoming prohibitive. We propose a traffic generation (TG) model that provides a fast...

  12. A Survey on Urban Traffic Management System Using Wireless Sensor Networks.

    Science.gov (United States)

    Nellore, Kapileswar; Hancke, Gerhard P

    2016-01-27

    Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the number of vehicles traveling on them. Due to this, road jamming and traffic correlated pollution have increased with the associated adverse societal and financial effect on different markets worldwide. A static control system may block emergency vehicles due to traffic jams. Wireless Sensor networks (WSNs) have gained increasing attention in traffic detection and avoiding road congestion. WSNs are very trendy due to their faster transfer of information, easy installation, less maintenance, compactness and for being less expensive compared to other network options. There has been significant research on Traffic Management Systems using WSNs to avoid congestion, ensure priority for emergency vehicles and cut the Average Waiting Time (AWT) of vehicles at intersections. In recent decades, researchers have started to monitor real-time traffic using WSNs, RFIDs, ZigBee, VANETs, Bluetooth devices, cameras and infrared signals. This paper presents a survey of current urban traffic management schemes for priority-based signalling, and reducing congestion and the AWT of vehicles. The main objective of this survey is to provide a taxonomy of different traffic management schemes used for avoiding congestion. Existing urban traffic management schemes for the avoidance of congestion and providing priority to emergency vehicles are considered and set the foundation for further research.

  13. A Survey on Urban Traffic Management System Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kapileswar Nellore

    2016-01-01

    Full Text Available Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the number of vehicles traveling on them. Due to this, road jamming and traffic correlated pollution have increased with the associated adverse societal and financial effect on different markets worldwide. A static control system may block emergency vehicles due to traffic jams. Wireless Sensor networks (WSNs have gained increasing attention in traffic detection and avoiding road congestion. WSNs are very trendy due to their faster transfer of information, easy installation, less maintenance, compactness and for being less expensive compared to other network options. There has been significant research on Traffic Management Systems using WSNs to avoid congestion, ensure priority for emergency vehicles and cut the Average Waiting Time (AWT of vehicles at intersections. In recent decades, researchers have started to monitor real-time traffic using WSNs, RFIDs, ZigBee, VANETs, Bluetooth devices, cameras and infrared signals. This paper presents a survey of current urban traffic management schemes for priority-based signalling, and reducing congestion and the AWT of vehicles. The main objective of this survey is to provide a taxonomy of different traffic management schemes used for avoiding congestion. Existing urban traffic management schemes for the avoidance of congestion and providing priority to emergency vehicles are considered and set the foundation for further research.

  14. Low Cost Wireless Network Camera Sensors for Traffic Monitoring

    Science.gov (United States)

    2012-07-01

    Many freeways and arterials in major cities in Texas are presently equipped with video detection cameras to : collect data and help in traffic/incident management. In this study, carefully controlled experiments determined : the throughput and output...

  15. Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

    Directory of Open Access Journals (Sweden)

    Chunyong Ma

    2018-01-01

    Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

  16. Energy-aware Traffic Engineering in Hybrid SDN/IP Backbone Networks

    OpenAIRE

    Wei, Yunkai; Zhang, XiaoNing; Xie, Lei; Leng, Supeng

    2016-01-01

    Software Defined Networking (SDN) can effectively improve the performance of traffic engineering and has promising application foreground in backbone networks. Therefore, new energy saving schemes must take SDN into account, which is extremely important considering the rapidly increasing energy consumption from Telecom and ISP networks. At the same time, the introduction of SDN in a current network must be incremental in most cases, for both technical and economic reasons. During this period,...

  17. Developing a stochastic traffic volume prediction model for public-private partnership projects

    Science.gov (United States)

    Phong, Nguyen Thanh; Likhitruangsilp, Veerasak; Onishi, Masamitsu

    2017-11-01

    Transportation projects require an enormous amount of capital investment resulting from their tremendous size, complexity, and risk. Due to the limitation of public finances, the private sector is invited to participate in transportation project development. The private sector can entirely or partially invest in transportation projects in the form of Public-Private Partnership (PPP) scheme, which has been an attractive option for several developing countries, including Vietnam. There are many factors affecting the success of PPP projects. The accurate prediction of traffic volume is considered one of the key success factors of PPP transportation projects. However, only few research works investigated how to predict traffic volume over a long period of time. Moreover, conventional traffic volume forecasting methods are usually based on deterministic models which predict a single value of traffic volume but do not consider risk and uncertainty. This knowledge gap makes it difficult for concessionaires to estimate PPP transportation project revenues accurately. The objective of this paper is to develop a probabilistic traffic volume prediction model. First, traffic volumes were estimated following the Geometric Brownian Motion (GBM) process. Monte Carlo technique is then applied to simulate different scenarios. The results show that this stochastic approach can systematically analyze variations in the traffic volume and yield more reliable estimates for PPP projects.

  18. Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

    OpenAIRE

    Wang, Li; Lin, Shimin; Yang, Jingfeng; Zhang, Nanfeng; Yang, Ji; Li, Yong; Zhou, Handong; Yang, Feng; Li, Zhifu

    2017-01-01

    Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of tra...

  19. Efficient traffic grooming with dynamic ONU grouping for multiple-OLT-based access network

    Science.gov (United States)

    Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Wang, Hongxiang

    2015-12-01

    Fast bandwidth growth urges large-scale high-density access scenarios, where the multiple Passive Optical Networking (PON) system clustered deployment can be adopted as an appropriate solution to fulfill the huge bandwidth demands, especially for a future 5G mobile network. However, the lack of interaction between different optical line terminals (OLTs) results in part of the bandwidth resources waste. To increase the bandwidth efficiency, as well as reduce bandwidth pressure at the edge of a network, we propose a centralized flexible PON architecture based on Time- and Wavelength-Division Multiplexing PON (TWDM PON). It can provide flexible affiliation for optical network units (ONUs) and different OLTs to support access network traffic localization. Specifically, a dynamic ONU grouping algorithm (DGA) is provided to obtain the minimal OLT outbound traffic. Simulation results show that DGA obtains an average 25.23% traffic gain increment under different OLT numbers within a small ONU number situation, and the traffic gain will increase dramatically with the increment of the ONU number. As the DGA can be deployed easily as an application running above the centralized control plane, the proposed architecture can be helpful to improve the network efficiency for future traffic-intensive access scenarios.

  20. The Influence of Traffic Networks on the Supply-Demand Balance of Tourism: A Case Study of Jiangsu Province, China

    Directory of Open Access Journals (Sweden)

    Tao Chen

    2014-01-01

    Full Text Available The purpose of this research is to address the impact of traffic networks on the supply-demand balance of tourism and to determine if it is necessary to incorporate the traffic flow data for nodes to determine the significant influences and impacts of traffic networks on tourism. For this purpose, a road network was established for Jiangsu province, and the topological parameters of this network and the tourism degree of coordination among each prefectural city were calculated as well. The results demonstrate that the inclusion of the spatial structure of the traffic network was not necessary for determining the supply-demand balance for tourism; thus, the collection of traffic flow data is required to perform further research. As a side result, it has been determined that the circuit routes are relatively absent from the Jiangsu traffic network, which might hinder tourism, and tourism resources are undersupplied to most prefectural cities in Jiangsu.

  1. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  2. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  3. A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

    Directory of Open Access Journals (Sweden)

    Xiaoling Tao

    2016-03-01

    Full Text Available Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN, and support vector machine (SVM by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.

  4. An implementation of traffic light system using multi-hop Ad hoc networks

    KAUST Repository

    Ansari, Imran Shafique

    2009-08-01

    In ad hoc networks nodes cooperate with each other to form a temporary network without the aid of any centralized administration. No wired base station or infrastructure is supported, and each host communicates via radio packets. Each host must act as a router, since routes are mostly multi-hop, due to the limited power transmission set by government agencies, (e.g. the Federal Communication Commission (FCC), which is 1 Watt in Industrial Scientific and Medical (ISM) band. The natures of wireless mobile ad hoc networks depend on batteries or other fatiguing means for their energy. A limited energy capacity may be the most significant performance constraint. Therefore, radio resource and power management is an important issue of any wireless network. In this paper, a design for traffic light system employing ad hoc networks is proposed. The traffic light system runs automatically based on signals sent through a multi-hop ad hoc network of \\'n\\' number of nodes utilizing the Token Ring protocol, which is efficient for this application from the energy prospective. The experiment consists of a graphical user interface that simulates the traffic lights and laptops (which have wireless network adapters) are used to run the graphical user interface and are responsible for setting up the ad hoc network between them. The traffic light system has been implemented utilizing A Mesh Driver (which allows for more than one wireless device to be connected simultaneously) and Java-based client-server programs. © 2009 IEEE.

  5. NETWORK TRAFFIC FORCASTING IN INFORMATION-TELECOMMUNICATION SYSTEM OF PRYDNIPROVSK RAILWAYS BASED ON NEURO-FUZZY NETWORK

    Directory of Open Access Journals (Sweden)

    V. M. Pakhomovа

    2016-12-01

    Full Text Available Purpose. Continuous increase in network traffic in the information-telecommunication system (ITS of Prydniprovsk Railways leads to the need to determine the real-time network congestion and to control the data flows. One of the possible solutions is a method of forecasting the volume of network traffic (inbound and outbound using neural network technology that will prevent from server overload and improve the quality of services. Methodology. Analysis of current network traffic in ITS of Prydniprovsk Railways and preparation of sets: learning, test and validation ones was conducted as well as creation of neuro-fuzzy network (hybrid system in Matlab program and organization of the following phases on the appropriate sets: learning, testing, forecast adequacy analysis. Findings. For the fragment (Dnipropetrovsk – Kyiv in ITS of Prydniprovsk Railways we made a forecast (day ahead for volume of network traffic based on the hybrid system created in Matlab program; MAPE values are as follows: 6.9% for volume of inbound traffic; 7.7% for volume of outbound traffic. It was found that the average learning error of the hybrid system decreases in case of increase in: the number of inputs (from 2 to 4; the number of terms (from 2 to 5 of the input variable; learning sample power (from 20 to 100. A significant impact on the average learning error of the hybrid system is caused by the number of terms of its input variable. It was determined that the lowest value of the average learning error is provided by 4-input hybrid system, it ensures more accurate learning of the neuro-fuzzy network by the hybrid method. Originality. The work resulted in the dependences for the average hybrid system error of the network traffic volume forecasting for the fragment (Dnipropetrovsk-Kyiv in ITS Prydniprovsk Railways on: the number of its inputs, the number of input variable terms, the learning sample power for different learning methods. Practical value. Forecasting of

  6. Development of a Software Based Firewall System for Computer Network Traffic Control

    Directory of Open Access Journals (Sweden)

    Ikhajamgbe OYAKHILOME

    2009-12-01

    Full Text Available The connection of an internal network to an external network such as Internet has made it vulnerable to attacks. One class of network attack is unauthorized penetration into network due to the openness of networks. It is possible for hackers to sum access to an internal network, this pose great danger to the network and network resources. Our objective and major concern of network design was to build a secured network, based on software firewall that ensured the integrity and confidentiality of information on the network. We studied several mechanisms to achieve this; one of such mechanism is the implementation of firewall system as a network defence. Our developed firewall has the ability to determine which network traffic should be allowed in or out of the network. Part of our studied work was also channelled towards a comprehensive study of hardware firewall security system with the aim of developing this software based firewall system. Our software firewall goes a long way in protecting an internal network from external unauthorized traffic penetration. We included an anti virus software which is lacking in most firewalls.

  7. Optimal Traffic Allocation for Multi-Stream Aggregation in Heterogeneous Networks

    DEFF Research Database (Denmark)

    Popovska Avramova, Andrijana; Iversen, Villy Bæk

    2015-01-01

    This paper investigates an optimal traffic rate allocation method for multi-stream aggregation over heterogeneous networks that deals with effective integration of two or more heterogeneous links for improved data throughput and enhanced quality of experience. The heterogeneity and the dynamic...... variations. Furthermore, services with different traffic characteristics in terms of quality of service requirements are considered. The simulation results show the advantages of our scheme with respect to efficient increase in data rate and delay performance compared to traditional schemes....

  8. Is CoV(t)-based Modeling Sufficient for Traffic Characterization in Network Links ?

    OpenAIRE

    Noirie, Ludovic; Post, Georg

    2008-01-01

    http://euronf.enst.fr/archive/164/EuroNFDeliverableDSEA641.pdf; International audience; For performance evaluation and dimensioning of packet-based networks, engineers need simple, efficient and realistic traffic models. The traffic volume on a packet link, observed at different time scales t, has previously been modeled as a stationary stochastic process based on the Coefficient of Variation CoV(t). In this paper we try to supply the missing information about the shape of the distribution fu...

  9. Integrating multiple networks for protein function prediction.

    Science.gov (United States)

    Yu, Guoxian; Zhu, Hailong; Domeniconi, Carlotta; Guo, Maozu

    2015-01-01

    High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.

  10. Predicting Posttraumatic Stress Symptoms in Children after Road Traffic Accidents

    Science.gov (United States)

    Landolt, Markus A.; Vollrath, Margarete; Timm, Karin; Gnehm, Hanspeter E.; Sennhauser, Felix H.

    2005-01-01

    Objective: To prospectively assess the prevalence, course, and predictors of posttraumatic stress symptoms (PTSSs) in children after road traffic accidents (RTAs). Method: Sixty-eight children (6.5-14.5 years old) were interviewed 4-6 weeks and 12 months after an RTA with the Child PTSD Reaction Index (response rate 58.6%). Their mothers (n = 60)…

  11. Predictive modelling of running and dwell times in railway traffic

    NARCIS (Netherlands)

    Kecman, P.; Goverde, R.M.P.

    2015-01-01

    Accurate estimation of running and dwell times is important for all levels of planning and control of railway traffic. The availability of historical track occupation data with a high degree of granularity inspired a data-driven approach for estimating these process times. In this paper we present

  12. A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

    Science.gov (United States)

    Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

    2017-07-01

    In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

  13. The improved degree of urban road traffic network: A case study of Xiamen, China

    Science.gov (United States)

    Wang, Shiguang; Zheng, Lili; Yu, Dexin

    2017-03-01

    The complex network theory is applied to the study of urban road traffic network topology, and we constructed a new measure to characterize an urban road network. It is inspiring to quantify the interaction more appropriately between nodes in complex networks, especially in the field of traffic. The measure takes into account properties of lanes (e.g. number of lanes, width, traffic direction). As much, it is a more comprehensive measure in comparison to previous network measures. It can be used to grasp the features of urban street network more clearly. We applied this measure to the road network in Xiamen, China. Based on a standard method from statistical physics, we examined in more detail the distribution of this new measure and found that (1) due to the limitation of space geographic attributes, traditional research conclusions acquired by using the original definition of degree to study the primal approach modeled urban street network are not very persuasive; (2) both of the direction of the network connection and the degree's odd or even classifications need to be analyzed specifically; (3) the improved degree distribution presents obvious hierarchy, and hierarchical values conform to the power-law distribution, and correlation of our new measure shows some significant segmentation of the urban road network.

  14. RESEARCH OF ENGINEERING TRAFFIC IN COMPUTER UZ NETWORK USING MPLS TE TECHNOLOGY

    Directory of Open Access Journals (Sweden)

    V. M. Pakhomovа

    2014-12-01

    Full Text Available Purpose. In railway transport of Ukraine one requires the use of computer networks of different technologies: Ethernet, Token Bus, Token Ring, FDDI and others. In combined computer networks on the railway transport it is necessary to use packet switching technology in multiprotocol networks MPLS (MultiProtocol Label Switching more effectively. They are based on the use of tags. Packet network must transmit different types of traffic with a given quality of service. The purpose of the research is development a methodology for determining the sequence of destination flows for the considered fragment of computer network of UZ. Methodology. When optimizing traffic management in MPLS networks has the important role of technology traffic engineering (Traffic Engineering, TE. The main mechanism of TE in MPLS is the use of unidirectional tunnels (MPLS TE tunnel to specify the path of the specified traffic. The mathematical model of the problem of traffic engineering in computer network of UZ technology MPLS TE was made. Computer UZ network is represented with the directed graph, their vertices are routers of computer network, and each arc simulates communication between nodes. As an optimization criterion serves the minimum value of the maximum utilization of the TE-tunnel. Findings. The six options destination flows were determined; rational sequence of flows was found, at which the maximum utilization of TE-tunnels considered a simplified fragment of a computer UZ network does not exceed 0.5. Originality. The method of solving the problem of traffic engineering in Multiprotocol network UZ technology MPLS TE was proposed; for different classes its own way is laid, depending on the bandwidth and channel loading. Practical value. Ability to determine the values of the maximum coefficient of use of TE-tunnels in computer UZ networks based on developed software model «TraffEng». The input parameters of the model: number of routers, channel capacity, the

  15. Trunk Reservation in Multi-service Networks with BPP Traffic

    DEFF Research Database (Denmark)

    Zheng, H.; Zhang, Qi; Iversen, Villy Bæk

    2006-01-01

    algorithm which allows for calculation of individual performance measures for each service, in particular the traffic congestion. The algorithm is numerically robust and requires a minimum of computer memory and computing time. The approximation is good when the services have equal mean service times....

  16. Traffic-aware Elastic Optical Networks to leverage Energy Savings

    DEFF Research Database (Denmark)

    Turus, Ioan; Fagertun, Anna Manolova; Dittmann, Lars

    2014-01-01

    Because of the static nature of the deployed optical networks, large energy wastage is experienced today in production networks such as Telecom networks . With power-adaptive optical interfaces and suitable grooming procedures, we propose the design of more energy efficient transport networks. Op...

  17. Design Issues for Traffic Management for the ATM UBR + Service for TCP Over Satellite Networks

    Science.gov (United States)

    Jain, Raj

    1999-01-01

    This project was a comprehensive research program for developing techniques for improving the performance of Internet protocols over Asynchronous Transfer Mode (ATM) based satellite networks. Among the service categories provided by ATM networks, the most commonly used category for data traffic is the unspecified bit rate (UBR) service. UBR allows sources to send data into the network without any feedback control. The project resulted in the numerous ATM Forum contributions and papers.

  18. Bandwidth Impacts of Localizing Peer-to-Peer IP Video Traffic in Access and Aggregation Networks

    Directory of Open Access Journals (Sweden)

    Kerpez Kenneth

    2008-01-01

    Full Text Available Abstract This paper examines the burgeoning impact of peer-to-peer (P2P traffic IP video traffic. High-quality IPTV or Internet TV has high-bandwidth requirements, and P2P IP video could severely strain broadband networks. A model for the popularity of video titles is given, showing that some titles are very popular and will often be available locally; making localized P2P attractive for video titles. The bandwidth impacts of localizing P2P video to try and keep traffic within a broadband access network area or within a broadband access aggregation network area are examined. Results indicate that such highly localized P2P video can greatly lower core bandwidth usage.

  19. Local control of traffic flows in networks: Self-organisation of phase synchronised dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Laemmer, Stefan; Donner, Reik [TU Dresden, Andreas-Schubert-Str. 23, 01062 Dresden (Germany); Helbing, Dirk [ETH Zuerich, Universitaetstr. 41, 8092 Zuerich (Switzerland)

    2008-07-01

    The effective control of flows in urban traffic networks is a subject of broad economic interest. During the last years, efforts have been made to develop decentralised control strategies that take only the actual state of present traffic conditions into account. In this contribution, we introduce a permeability model for the local control of conflicting material flows on networks, which incorporates a self-organisation of the flows. The dynamics of our model is studied under different situations, with a special emphasis on the development of a phase synchronised switching behaviour at the nodes of the traffic network. In order to improve the potential applicability of our concept, we discuss how a proper demand anticipation and the definition of a priority function can be used to further optimise the performance of the presented strategy.

  20. Bandwidth Impacts of Localizing Peer-to-Peer IP Video Traffic in Access and Aggregation Networks

    Directory of Open Access Journals (Sweden)

    Kenneth Kerpez

    2008-10-01

    Full Text Available This paper examines the burgeoning impact of peer-to-peer (P2P traffic IP video traffic. High-quality IPTV or Internet TV has high-bandwidth requirements, and P2P IP video could severely strain broadband networks. A model for the popularity of video titles is given, showing that some titles are very popular and will often be available locally; making localized P2P attractive for video titles. The bandwidth impacts of localizing P2P video to try and keep traffic within a broadband access network area or within a broadband access aggregation network area are examined. Results indicate that such highly localized P2P video can greatly lower core bandwidth usage.

  1. A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

    Science.gov (United States)

    Cai, Kaiquan; Jia, Yaoguang; Zhu, Yanbo; Xiao, Mingming

    2015-01-01

    Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

  2. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015.

    Science.gov (United States)

    Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

    2016-01-01

    Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. It was a cross-sectional study. All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents.

  3. Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones

    Directory of Open Access Journals (Sweden)

    Cuiping Zhang

    2014-01-01

    Full Text Available Traffic safety evaluation for traffic analysis zones (TAZs plays an important role in transportation safety planning and long-range transportation plan development. This paper aims to present a comprehensive analysis of zonal safety evaluation. First, several criteria are proposed to measure the crash risk at zonal level. Then these criteria are integrated into one measure-average hazard index (AHI, which is used to identify unsafe zones. In addition, the study develops a negative binomial regression model to statistically estimate significant factors for the unsafe zones. The model results indicate that the zonal crash frequency can be associated with several social-economic, demographic, and transportation system factors. The impact of these significant factors on zonal crash is also discussed. The finding of this study suggests that safety evaluation and estimation might benefit engineers and decision makers in identifying high crash locations for potential safety improvements.

  4. Energy savings in mobile broadband network based on load predictions

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pedersen, Torben Bach; Sørensen, Troels Bundgaard

    2012-01-01

    in wireless networks. To save energy in MBNs, one of the options is to turn off parts of the network equipment in areas where traffic falls below a specific predefined threshold. This paper looks at a methodology for identifying periods of the day when cells or sites carrying low traffic are candidates...... for being totally or partly switched off, given that the decrease in service quality can be controlled gracefully when the sites are switched off. Based on traffic data from an operational network, potential average energy savings of approximately 30% with some few low traffic cells/sites reaching up to 99......Abstract—The deployment of new network equipment is resulting in increasing energy consumption in mobile broadband networks (MBNs). This contributes to higher CO2 emissions. Over the last 10 years MBNs have grown considerably, and are still growing to meet the evolution in traffic volume carried...

  5. Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

    Directory of Open Access Journals (Sweden)

    Diogo Santos

    2016-06-01

    Full Text Available Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.

  6. PLUS highway network analysis: Case of in-coming traffic burden in 2013

    Science.gov (United States)

    Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman; Mohamad, Ismail

    2017-05-01

    PLUS highway is the largest concessionary in Malaysia. The study on PLUS highway development, in order to overcome the demand for efficient road transportation, is crucial. If the highways have better interconnected network, it will help the economic activities such as trade to increase. If economic activities are increasing, the benefit will come to the people and state. In its turn, it will help the leaders to plan and conduct national development program. In this paper, network analysis approach will be used to study the in-coming traffic burden during the year of 2013. The highway network linking all the toll plazas is a dynamic network. The objective of this study is to learn and understand about highway network in terms of the in-coming traffic burden entering to each toll plazas along PLUS highway. For this purpose, the filtered network topology based on the forest of all possible minimum spanning trees is used. The in-coming traffic burden of a city is represented by the number of cars passing through the corresponding toll plaza. To interpret the filtered network, centrality measures such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality are used. An overall centrality will be proposed if those four measures are assumed to have the same role. Based on the results, some suggestions and recommendations for PLUS highway network development will be delivered to PLUS highway management.

  7. Analysis and Classification of Traffic in Wireless Sensor Network

    Science.gov (United States)

    2007-03-01

    length can be directly extracted from the Xsniffer output under the column “Len.” In the self-similarity discussion in Chapter V, two Mathcad scripts...processed through a Mathcad script as described in Chapter III to determine if the traffic is self-similar. 1. Direct Connection to Base Setup...analyzed using two Mathcad scripts for self- similarity characteristics. Variance time plots were constructed for both packet length and interarrival

  8. Communication and Networking Techniques for Traffic Safety Systems

    OpenAIRE

    Chisalita, Ioan

    2006-01-01

    Accident statistics indicate that every year a significant number of casualties and extensive property losses occur due to traffic accidents. Consequently, efforts are directed towards developing passive and active safety systems that help reduce the severity of crashes, or prevent vehicles from colliding with one another. To develop these systems, technologies such as sensor systems, computer vision and vehicular communication have been proposed. Safety vehicular communication is defined as ...

  9. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  10. Effect and Analysis of Sustainable Cell Rate using MPEG video Traffic in ATM Networks

    Directory of Open Access Journals (Sweden)

    Sakshi Kaushal

    2006-04-01

    Full Text Available The broadband networks inhibit the capability to carry multiple types of traffic – voice, video and data, but these services need to be controlled according to the traffic contract negotiated at the time of the connection to maintain desired Quality of service. Such control techniques use traffic descriptors to evaluate its performance and effectiveness. In case of Variable Bit Rate (VBR services, Peak Cell Rate (PCR and its Cell Delay Variation Tolerance (CDVTPCR are mandatory descriptors. In addition to these, ATM Forum proposed Sustainable Cell Rate (SCR and its Cell delay variation tolerance (CDVTSCR. In this paper, we evaluated the impact of specific SCR and CDVTSCR values on the Usage Parameter Control (UPC performance in case of measured MPEG traffic for improving the efficiency

  11. Application of growing hierarchical SOM for visualisation of network forensics traffic data.

    Science.gov (United States)

    Palomo, E J; North, J; Elizondo, D; Luque, R M; Watson, T

    2012-08-01

    Digital investigation methods are becoming more and more important due to the proliferation of digital crimes and crimes involving digital evidence. Network forensics is a research area that gathers evidence by collecting and analysing network traffic data logs. This analysis can be a difficult process, especially because of the high variability of these attacks and large amount of data. Therefore, software tools that can help with these digital investigations are in great demand. In this paper, a novel approach to analysing and visualising network traffic data based on growing hierarchical self-organising maps (GHSOM) is presented. The self-organising map (SOM) has been shown to be successful for the analysis of highly-dimensional input data in data mining applications as well as for data visualisation in a more intuitive and understandable manner. However, the SOM has some problems related to its static topology and its inability to represent hierarchical relationships in the input data. The GHSOM tries to overcome these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reflects the inherent hierarchical relationships among them. Moreover, the proposed GHSOM has been modified to correctly treat the qualitative features that are present in the traffic data in addition to the quantitative features. Experimental results show that this approach can be very useful for a better understanding of network traffic data, making it easier to search for evidence of attacks or anomalous behaviour in a network environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Best response game of traffic on road network of non-signalized intersections

    Science.gov (United States)

    Yao, Wang; Jia, Ning; Zhong, Shiquan; Li, Liying

    2018-01-01

    This paper studies the traffic flow in a grid road network with non-signalized intersections. The nature of the drivers in the network is simulated such that they play an iterative snowdrift game with other drivers. A cellular automata model is applied to study the characteristics of the traffic flow and the evolution of the behaviour of the drivers during the game. The drivers use best-response as their strategy to update rules. Three major findings are revealed. First, the cooperation rate in simulation experiences staircase-shaped drop as cost to benefit ratio r increases, and cooperation rate can be derived analytically as a function of cost to benefit ratio r. Second, we find that higher cooperation rate corresponds to higher average speed, lower density and higher flow. This reveals that defectors deteriorate the efficiency of traffic on non-signalized intersections. Third, the system experiences more randomness when the density is low because the drivers will not have much opportunity to update strategy when the density is low. These findings help to show how the strategy of drivers in a traffic network evolves and how their interactions influence the overall performance of the traffic system.

  13. Link prediction in weighted networks

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Mørup, Morten

    2012-01-01

    Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network...

  14. Estimation and Control of Networked Distributed Parameter Systems: Application to Traffic Flow

    KAUST Repository

    Canepa, Edward

    2016-11-01

    The management of large-scale transportation infrastructure is becoming a very complex task for the urban areas of this century which are covering bigger geographic spaces and facing the inclusion of connected and self-controlled vehicles. This new system paradigm can leverage many forms of sensing and interaction, including a high-scale mobile sensing approach. To obtain a high penetration sensing system on urban areas more practical and scalable platforms are needed, combined with estimation algorithms suitable to the computational capabilities of these platforms. The purpose of this work was to develop a transportation framework that is able to handle different kinds of sensing data (e.g., connected vehicles, loop detectors) and optimize the traffic state on a defined traffic network. The framework estimates the traffic on road networks modeled by a family of Lighthill-Whitham-Richards equations. Based on an equivalent formulation of the problem using a Hamilton-Jacobi equation and using a semi-analytic formula, I will show that the model constraints resulting from the Hamilton-Jacobi equation are linear, albeit with unknown integer variables. This general framework solve exactly a variety of problems arising in transportation networks: traffic estimation, traffic control (including robust control), cybersecurity and sensor fault detection, or privacy analysis of users in probe-based traffic monitoring systems. This framework is very flexible, fast, and yields exact results. The recent advances in sensors (GPS, inertial measurement units) and microprocessors enable the development low-cost dedicated devices for traffic sensing in cities, 5 which are highly scalable, providing a feasible solution to cover large urban areas. However, one of the main problems to address is the privacy of the users of the transportation system, the framework presented here is a viable option to guarantee the privacy of the users by design.

  15. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

    Science.gov (United States)

    Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y; Kantelhardt, Jan W

    2015-01-01

    Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

  16. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks.

    Directory of Open Access Journals (Sweden)

    Mirko Kämpf

    Full Text Available Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic's importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.

  17. The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

    Science.gov (United States)

    Kämpf, Mirko; Tessenow, Eric; Kenett, Dror Y.; Kantelhardt, Jan W.

    2015-01-01

    Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic’s importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends. PMID:26720074

  18. Predictive control strategies for energy saving of hybrid electric vehicles based on traffic light information

    Directory of Open Access Journals (Sweden)

    Kaijiang YU

    2015-10-01

    Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.

  19. 3D Markov Process for Traffic Flow Prediction in Real-Time

    Directory of Open Access Journals (Sweden)

    Eunjeong Ko

    2016-01-01

    Full Text Available Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1 a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2 the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further.

  20. 3D Markov Process for Traffic Flow Prediction in Real-Time

    Science.gov (United States)

    Ko, Eunjeong; Ahn, Jinyoung; Kim, Eun Yi

    2016-01-01

    Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further. PMID:26821025

  1. Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

    DEFF Research Database (Denmark)

    Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

    2011-01-01

    This article provides a broad picture of fatal traffic accidents in Israel to answer an increasing need of addressing compelling problems, designing preventive measures, and targeting specific population groups with the objective of reducing the number of traffic fatalities. The analysis focuses...... on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns......: (1) single-vehicle accidents of young drivers, (2) multiple-vehicle accidents between young drivers, (3) accidents involving motorcyclists or cyclists, (4) accidents where elderly pedestrians crossed in urban areas, and (5) accidents where children and teenagers cross major roads in small urban areas...

  2. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  3. Link prediction in multiplex online social networks.

    Science.gov (United States)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  4. Link prediction in multiplex online social networks

    Science.gov (United States)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  5. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-01

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels. PMID:26784204

  6. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety

    Directory of Open Access Journals (Sweden)

    Angelica Reyes-Muñoz

    2016-01-01

    Full Text Available The emergence of Body Sensor Networks (BSNs constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1 an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving that may cause traffic accidents is presented; (2 A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3 as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  7. Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety.

    Science.gov (United States)

    Reyes-Muñoz, Angelica; Domingo, Mari Carmen; López-Trinidad, Marco Antonio; Delgado, José Luis

    2016-01-15

    The emergence of Body Sensor Networks (BSNs) constitutes a new and fast growing trend for the development of daily routine applications. However, in the case of heterogeneous BSNs integration with Vehicular ad hoc Networks (VANETs) a large number of difficulties remain, that must be solved, especially when talking about the detection of human state factors that impair the driving of motor vehicles. The main contributions of this investigation are principally three: (1) an exhaustive review of the current mechanisms to detect four basic physiological behavior states (drowsy, drunk, driving under emotional state disorders and distracted driving) that may cause traffic accidents is presented; (2) A middleware architecture is proposed. This architecture can communicate with the car dashboard, emergency services, vehicles belonging to the VANET and road or street facilities. This architecture seeks on the one hand to improve the car driving experience of the driver and on the other hand to extend security mechanisms for the surrounding individuals; and (3) as a proof of concept, an Android real-time attention low level detection application that runs in a next-generation smartphone is developed. The application features mechanisms that allow one to measure the degree of attention of a driver on the base of her/his EEG signals, establish wireless communication links via various standard wireless means, GPRS, Bluetooth and WiFi and issue alarms of critical low driver attention levels.

  8. Software defined multi-OLT passive optical network for flexible traffic allocation

    Science.gov (United States)

    Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Zhang, Jiawei; Li, Hui

    2016-10-01

    With the rapid growth of 4G mobile network and vehicular network services mobile terminal users have increasing demand on data sharing among different radio remote units (RRUs) and roadside units (RSUs). Meanwhile, commercial video-streaming, video/voice conference applications delivered through peer-to-peer (P2P) technology are still keep on stimulating the sharp increment of bandwidth demand in both business and residential subscribers. However, a significant issue is that, although wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) technology have been proposed to fulfil the ever-increasing bandwidth demand in access network, the bandwidth of optical fiber is not unlimited due to the restriction of optical component properties and modulation/demodulation technology, and blindly increase the wavelength cannot meet the cost-sensitive characteristic of the access network. In this paper, we propose a software defined multi-OLT PON architecture to support efficient scheduling of access network traffic. By introducing software defined networking technology and wavelength selective switch into TWDM PON system in central office, multiple OLTs can be considered as a bandwidth resource pool and support flexible traffic allocation for optical network units (ONUs). Moreover, under the configuration of the control plane, ONUs have the capability of changing affiliation between different OLTs under different traffic situations, thus the inter-OLT traffic can be localized and the data exchange pressure of the core network can be released. Considering this architecture is designed to be maximum following the TWDM PON specification, the existing optical distribution network (ODN) investment can be saved and conventional EPON/GPON equipment can be compatible with the proposed architecture. What's more, based on this architecture, we propose a dynamic wavelength scheduling algorithm, which can be deployed as an application on control plane

  9. Multi Service Proxy: Mobile Web Traffic Entitlement Point in 4G Core Network

    Directory of Open Access Journals (Sweden)

    Dalibor Uhlir

    2015-05-01

    Full Text Available Core part of state-of-the-art mobile networks is composed of several standard elements like GGSN (Gateway General Packet Radio Service Support Node, SGSN (Serving GPRS Support Node, F5 or MSP (Multi Service Proxy. Each node handles network traffic from a slightly different perspective, and with various goals. In this article we will focus only on the MSP, its key features and especially on related security issues. MSP handles all HTTP traffic in the mobile network and therefore it is a suitable point for the implementation of different optimization functions, e.g. to reduce the volume of data generated by YouTube or similar HTTP-based service. This article will introduce basic features and functions of MSP as well as ways of remote access and security mechanisms of this key element in state-of-the-art mobile networks.

  10. Evaluation Study of a Wireless Multimedia Traffic-Oriented Network Model

    Science.gov (United States)

    Vasiliadis, D. C.; Rizos, G. E.; Vassilakis, C.

    2008-11-01

    In this paper, a wireless multimedia traffic-oriented network scheme over a fourth generation system (4-G) is presented and analyzed. We conducted an extensive evaluation study for various mobility configurations in order to incorporate the behavior of the IEEE 802.11b standard over a test-bed wireless multimedia network model. In this context, the Quality of Services (QoS) over this network is vital for providing a reliable high-bandwidth platform for data-intensive sources like video streaming. Therefore, the main issues concerned in terms of QoS were the metrics for bandwidth of both dropped and lost packets and their mean packet delay under various traffic conditions. Finally, we used a generic distance-vector routing protocol which was based on an implementation of Distributed Bellman-Ford algorithm. The performance of the test-bed network model has been evaluated by using the simulation environment of NS-2.

  11. Network traffic intelligence using a low interaction honeypot

    Science.gov (United States)

    Nyamugudza, Tendai; Rajasekar, Venkatesh; Sen, Prasad; Nirmala, M.; Madhu Viswanatham, V.

    2017-11-01

    Advancements in networking technology have seen more and more devices becoming connected day by day. This has given organizations capacity to extend their networks beyond their boundaries to remote offices and remote employees. However as the network grows security becomes a major challenge since the attack surface also increases. There is need to guard the network against different types of attacks like intrusion and malware through using different tools at different networking levels. This paper describes how network intelligence can be acquired through implementing a low-interaction honeypot which detects and track network intrusion. Honeypot allows an organization to interact and gather information about an attack earlier before it compromises the network. This process is important because it allows the organization to learn about future attacks of the same nature and allows them to develop counter measures. The paper further shows how honeypot-honey net based model for interruption detection system (IDS) can be used to get the best valuable information about the attacker and prevent unexpected harm to the network.

  12. Understanding the vulnerability of traffic networks by means of structured expert judgment elicitation

    NARCIS (Netherlands)

    Nogal, M.; Morales Napoles, O.; O'Connor, Alan

    2016-01-01

    There is a lack of consensus in relation to the operationality of important concepts and descriptors of traffic networks such as resilience and vulnerability. With the aim of determining a framework with mathematical sound to objectively define and delimit these concepts, the expert judgment

  13. Road safety and bicycle usage impacts of unbundling vehicular and cycle traffic in Dutch urban networks

    NARCIS (Netherlands)

    Schepers, Paul; Heinen, Eva; Methorst, Rob; Wegman, Fred

    2013-01-01

    Bicycle-motor vehicle crashes are concentrated along distributor roads where cyclists are exposed to greater volumes of high-speed motorists than they would experience on access roads. This study examined the road safety impact of network-level separation of vehicular and cycle traffic in Dutch

  14. Routing of guaranteed throughput traffic in a network-on-chip

    NARCIS (Netherlands)

    Kavaldjiev, N.K.; Smit, Gerardus Johannes Maria; Wolkotte, P.T.; Jansen, P.G.

    This paper examines the possibilities of providing throughput guarantees in a network-on-chip by appropriate traffic routing. A source routing function is used to find routes with specified throughput for the data streams in a streaming multiprocessor system-on-chip. The influence of the routing

  15. Dynamic Flow Migration for Delay Constrained Traffic in Software-Defined Networks

    NARCIS (Netherlands)

    Berger, Andre; Gross, James; Danielis, Peter; Dán, György

    2017-01-01

    Various industrial control applications have stringent end-to-end latency requirements in the order of a few milliseconds. Software-defined networking (SDN) is a promising solution in order to meet these stringent requirements under varying traffic patterns, as it enables the flexible management of

  16. Downlink Performance of a Multi-Carrier MIMO System in a Bursty Traffic Cellular Network

    DEFF Research Database (Denmark)

    Nguyen, Hung Tuan; Kovacs, Istvan; Wang, Yuanye

    2011-01-01

    In this paper we analyse the downlink performance of a rank adaptive multiple input multiple output (MIMO) system in a busty traffic cellular network. A LTE-Advanced system with multiple component carriers was selected as a study case. To highlight the advantage of using MIMO techniques, we used...

  17. Report on the Dagstuhl Seminar on Visualization and Monitoring of Network Traffic

    NARCIS (Netherlands)

    Keim, Daniel A.; Pras, Aiko; Schönwälder, Jürgen; Wong, Pak Chung; Mansmann, Florian

    The Dagstuhl Seminar on Visualization and Monitoring of Network Traffic took place May 17-20, 2009 in Dagstuhl, Germany. Dagstuhl seminars promote personal interaction and open discussion of results as well as new ideas. Unlike at most conferences, the focus is not solely on the presentation of

  18. Traffic characteristics analysis in optical burst switching networks with optical label processing

    Directory of Open Access Journals (Sweden)

    Edson Moschim

    2007-03-01

    Full Text Available An analysis is carried out with burst-switching optical networks which use label processing consisting of orthogonal optical codes (OOC, considering traffic characteristics such as length/duration and arrival rate of bursts. Main results show that the use of OOC label processing influences on the decrease of burst loss probability, especially for short-lived bursts. Therefore, short bursts that would be blocked in conventional electronic processing networks are transmitted when the OOC label processing is used. Thus, an increase in the network use occurs as well as a decrease in the burst transmission latency, reaching a granularity close to packets networks.

  19. Control Policies Approaching HGI Performance in Heavy Traffic for Resource Sharing Networks

    OpenAIRE

    Budhiraja, Amarjit; Johnson, Dane

    2017-01-01

    We consider resource sharing networks of the form introduced in the work of Massouli\\'{e} and Roberts(2000) as models for Internet flows. The goal is to study the open problem, formulated in Harrison et al. (2014), of constructing simple form rate allocation policies for broad families of resource sharing networks with associated costs converging to the Hierarchical Greedy Ideal performance in the heavy traffic limit. We consider two types of cost criteria, an infinite horizon discounted cost...

  20. Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

    National Research Council Canada - National Science Library

    Wan, Jiafu; Liu, Jianqi; Shao, Zehui; Vasilakos, Athanasios V; Imran, Muhammad; Zhou, Keliang

    2016-01-01

    The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm...

  1. Optimal traffic control in highway transportation networks using linear programming

    KAUST Repository

    Li, Yanning

    2014-06-01

    This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.

  2. Hyper-Spectral Networking Concept of Operations and Future Air Traffic Management Simulations

    Science.gov (United States)

    Davis, Paul; Boisvert, Benjamin

    2017-01-01

    The NASA sponsored Hyper-Spectral Communications and Networking for Air Traffic Management (ATM) (HSCNA) project is conducting research to improve the operational efficiency of the future National Airspace System (NAS) through diverse and secure multi-band, multi-mode, and millimeter-wave (mmWave) wireless links. Worldwide growth of air transportation and the coming of unmanned aircraft systems (UAS) will increase air traffic density and complexity. Safe coordination of aircraft will require more capable technologies for communications, navigation, and surveillance (CNS). The HSCNA project will provide a foundation for technology and operational concepts to accommodate a significantly greater number of networked aircraft. This paper describes two of the HSCNA projects technical challenges. The first technical challenge is to develop a multi-band networking concept of operations (ConOps) for use in multiple phases of flight and all communication link types. This ConOps will integrate the advanced technologies explored by the HSCNA project and future operational concepts into a harmonized vision of future NAS communications and networking. The second technical challenge discussed is to conduct simulations of future ATM operations using multi-bandmulti-mode networking and technologies. Large-scale simulations will assess the impact, compared to todays system, of the new and integrated networks and technologies under future air traffic demand.

  3. Dynamic routing control in heterogeneous tactical networks with multiple traffic priorities

    Science.gov (United States)

    Fecko, Mariusz A.; Wong, Larry; Kang, Jaewong; Cichocki, Andrzej; Kaul, Vikram; Samtani, Sunil

    2012-05-01

    To efficiently use alternate paths during periods of congestion, we have devised prioritized Dynamic Routing Control Agent (pDRCA) that (1) selects best links to meet the bandwidth and delay requirements of traffic, (2) provides load-balancing and traffic prioritization when multiple topologies are available, and (3) handles changes in link quality and traffic demand, and link outages. pDRCA provides multiplatform load balancing to maximize SATCOM (both P2P and multi-point) and airborne links utilization. It influences link selection by configuring the cost metrics on a router's interface, which does not require any changes to the routing protocol itself. It supports service differentiation of multiple traffic priorities by providing more network resources to the highest priority flows. pDRCA does so by solving an optimization problem to find optimal links weights that increase throughput and decrease E2E delay; avoid congested, low quality, and long delay links; and exploit path diversity in the network. These optimal link weights are sent to the local agents to be configured on individual routers per traffic priority. The pDRCA optimization algorithm has been proven effective in improving application performance. We created a variety of different test scenarios by varying traffic profile and link behavior (stable links, varying capacity, and link outages). In the scenarios where high priority traffic experienced significant loss without pDRCA, the average loss was reduced from 49.5% to 13% and in some cases dropped to 0%. Currently, pDRCA is integrated with an open-source software router and priority queues on Linux as a component of Open Tactical Router (OTR), which is being developed by ONR DTCN program.

  4. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  5. Characterization of Background Traffic in Hybrid Network Simulation

    National Research Council Canada - National Science Library

    Lauwens, Ben; Scheers, Bart; Van de Capelle, Antoine

    2006-01-01

    .... Two approaches are common: discrete event simulation and fluid approximation. A discrete event simulation generates a huge amount of events for a full-blown battlefield communication network resulting in a very long runtime...

  6. Anticipation of Traffic Demands to Guarantee QoS in IP/Optical Networks

    Directory of Open Access Journals (Sweden)

    Carolina Pinart

    2010-09-01

    Full Text Available Traffic in the Internet backbone is expected to grow above a few Tbit/s in 2020. To cope with this, operators are moving to IP/optical network architectures, where IP is the convergence layer for all services. On the other hand, the quality of service (QoS requirements of future applications encompasses the individualization of services and the assurance of stricter quality parameters such as latency, jitter or capacity. In other words, future optical networks will not only transport more IP data, but they will also have to offer differentiated QoS requirements to services. Finally, some emerging applications, e.g., grid computing, need greater flexibility in the usage of network resources, which involves establishing and releasing connections as if they were virtualized resources controlled by other elements or layers. In this context, traffic-driven lightpath provisioning and service-plane approaches arise as very interesting candidate solutions to solve the main challenges described above. This work reviews the concepts of service-oriented and self-managed networks and relates them to propose an integrated approach to assure QoS by offering flow-aware networking in the sense that traffic demands will be anticipated in a suitable way, lightpaths will be established taking into account QoS information (i.e., impairments and complex services will be decomposed into optical connections so that the above techniques can be employed to assure QoS for any service.

  7. Threshold based AntNet algorithm for dynamic traffic routing of road networks

    Directory of Open Access Journals (Sweden)

    Ayman M. Ghazy

    2012-07-01

    Full Text Available Dynamic routing algorithms play an important role in road traffic routing to avoid congestion and to direct vehicles to better routes. AntNet routing algorithms have been applied, extensively and successfully, in data communication network. However, its application for dynamic routing on road networks is still considerably limited. This paper presents a modified version of the AntNet routing algorithm, called “Threshold based AntNet”, that has the ability to efficiently utilize a priori information of dynamic traffic routing, especially, for road networks. The modification exploits the practical and pre-known information for most road traffic networks, namely, the good travel times between sources and destinations. The values of those good travel times are manipulated as threshold values. This approach has proven to conserve tracking of good routes. According to the dynamic nature of the problem, the presented approach guards the agility of rediscovering a good route. Attaining the thresholds (good reported travel times, of a given source to destination route, permits for a better utilization of the computational resources, that, leads to better accommodation for the network changes. The presented algorithm introduces a new type of ants called “check ants”. It assists in preserving good routes and, better yet, exposes and discards the degraded ones. The threshold AntNet algorithm presents a new strategy for updating the routing information, supported by the backward ants.

  8. DynaMIT2.0: architecture design and preliminary results on real-time data fusion for traffic prediction and crisis management

    DEFF Research Database (Denmark)

    Lu, Yang; Pereira, Francisco Camara; Seshadri, Ravi

    2015-01-01

    The ability to monitor and predict in real-time the state of the transportation network is a valuable tool for both transportation administrators and travellers. While many solutions exist for this task, they are generally much more successful in recurrent scenarios than in non-recurrent ones....... Paradoxically, it is in the latter case that such tools can make the difference. Therefore, the dynamic traffic assignment and simulation based prediction system such as DynaMIT (1) demonstrates high effectiveness in the context of sudden network disturbance or demand pattern changes. This paper presents...... expressway to show the actual benefit of the system....

  9. Method for predicting future developments of traffic noise in urban areas in Europe

    NARCIS (Netherlands)

    Salomons, E.; Hout, D. van den; Janssen, S.; Kugler, U.; MacA, V.

    2010-01-01

    Traffic noise in urban areas in Europe is a major environmental stressor. In this study we present a method for predicting how environmental noise can be expected to develop in the future. In the project HEIMTSA scenarios were developed for all relevant environmental stressors to health, for all

  10. Heterogeneous Cellular Networks with Spatio-Temporal Traffic: Delay Analysis and Scheduling

    OpenAIRE

    Zhong, Yi; Quek, Tony Q. S.; Ge, Xiaohu

    2016-01-01

    Emergence of new types of services has led to various traffic and diverse delay requirements in fifth generation (5G) wireless networks. Meeting diverse delay requirements is one of the most critical goals for the design of 5G wireless networks. Though the delay of point-to-point communications has been well investigated, the delay of multi-point to multi-point communications has not been thoroughly studied since it is a complicated function of all links in the network. In this work, we propo...

  11. USER EQUILIBRIUM AND SYSTEM OPTIMUM TRAFFIC ASSIGNMENTS; ISTANBUL ROAD NETWORK EXAMPLE

    Directory of Open Access Journals (Sweden)

    Banihan GÜNAY

    1996-03-01

    Full Text Available The concept of road networks and traffic flow equilibrium conditions are briefly reviewed and discussed. In order to see whether some benefits for the society (e.g. whole network by employing a System Optimum assignment approach can be achieved or not, an assessment study was carried out on the Ystanbul road network using the actual data gathered. As a result of the system optimising simulation, queuing times on the Bosphorus Bridge dropped by 12% and speed of an average car increased by 16%, compared to the results produced by the User Equilibrium assignment. Besides, the total system journey time was also reduced by about 4%.

  12. Influence of traffic on build-up of polycyclic aromatic hydrocarbons on urban road surfaces: A Bayesian network modelling approach.

    Science.gov (United States)

    Li, Yingxia; Jia, Ziliang; Wijesiri, Buddhi; Song, Ningning; Goonetilleke, Ashantha

    2017-12-04

    Due to their carcinogenic effects, Polycyclic Aromatic Hydrocarbons (PAHs) deposited on urban surfaces are a major concern in the context of stormwater pollution. However, the design of effective pollution mitigation strategies is challenging due to the lack of reliability in stormwater quality modelling outcomes. Current modelling approaches do not adequately replicate the interdependencies between pollutant processes and their influential factors. Using Bayesian Network modelling, this research study characterised the influence of vehicular traffic on the build-up of the sixteen US EPA classified priority PAHs. The predictive analysis was conditional on the structure of the proposed BN, which can be further improved by including more variables. This novel modelling approach facilitated the characterisation of the influence of traffic as a source of origin and also as a key factor that influences the re-distribution of PAHs, with positive or negative relationship between traffic volume and PAH build-up. It was evident that the re-distribution of particle-bound PAHs is determined by the particle size rather than the chemical characteristics such as volatility. Moreover, compared to commercial and residential land uses, mostly industrial land use contributes to the PAHs load released to the environment. Carcinogenic PAHs in industrial areas are likely to be associated with finer particles, while PAHs, which are not classified as human carcinogens, are likely to be found in the coarser particle fraction. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Noise-Assisted Concurrent Multipath Traffic Distribution in Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Narun Asvarujanon

    2013-01-01

    Full Text Available The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP, which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account.

  14. Accelerating Network Traffic Analytics Using Query-DrivenVisualization

    Energy Technology Data Exchange (ETDEWEB)

    Bethel, E. Wes; Campbell, Scott; Dart, Eli; Stockinger, Kurt; Wu,Kesheng

    2006-07-29

    Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysis technologies targeted at reducing the time required for data filtering, querying, hypothesis testing and knowledge discovery in the domain of network connection data analysis. We show that use of compressed bitmap indexing can quickly answer queries in an interactive visual data analysis application, and compare its performance with two alternatives for serial and parallel filtering/querying on 2.5 billion records worth of network connection data collected over a period of 42 weeks. Our approach to visual network connection data exploration centers on two primary factors: interactive ad-hoc and multiresolution query formulation and execution over n dimensions and visual display of then-dimensional histogram results. This combination is applied in a case study to detect a distributed network scan and to then identify the set of remote hosts participating in the attack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools.

  15. A network traffic reduction method for cooperative positioning

    NARCIS (Netherlands)

    Das, Kallol; Wymeersch, Henk

    Cooperative positioning is suitable for applications where conventional positioning fails due to lack of connectivity with a sufficient number of reference nodes. In a dense network, as the number of cooperating devices increases, the number of packet exchanges also increases proportionally. This

  16. A latency analysis for M2M and OG-like traffic patterns in different HSPA core network configurations

    Directory of Open Access Journals (Sweden)

    M. V. Popović

    2014-11-01

    Full Text Available In this paper we present an analysis intended to reveal possible impacts of core network features on latency for modelled M2M and Online Gaming traffic. Simulations were performed in a live 3G/HSPA network. Test traffic simulating multiplayer real-time games and M2M applications was generated on 10 mobile phones in parallel, sending data to a remote server. APNs with different combinations of hardware and features (proxy server, different GGSNs and firewalls, usage of Service Awareness feature were chosen. The traffic was recorded on the Gn interface in the mobile core. The goal of experiments was to evaluate any eventually significant variation of average recorded RTTs in the core part of mobile network that would clearly indicate either the impact of used APN on delay for a specific traffic pattern, or selectivity of the APN towards different traffic patterns.

  17. Diamond Networks with Bursty Traffic: Bounds on the Minimum Energy-Per-Bit

    CERN Document Server

    Shomorony, Ilan; Parvaresh, Farzad; Avestimehr, A Salman

    2012-01-01

    When data traffic in a wireless network is bursty, small amounts of data sporadically become available for transmission, at times that are unknown at the receivers, and an extra amount of energy must be spent at the transmitters to overcome this lack of synchronization between the network nodes. In practice, pre-defined header sequences are used with the purpose of synchronizing the different network nodes. However, in networks where relays must be used for communication, the overhead required for synchronizing the entire network may be very significant. In this work, we study the fundamental limits of energy-efficient communication in an asynchronous diamond network with two relays. We formalize the notion of relay synchronization by saying that a relay is synchronized if the conditional entropy of the arrival time of the source message given the received signals at the relay is small. We show that the minimum energy-per-bit for bursty traffic in diamond networks is achieved with a coding scheme where each r...

  18. Integrated coding-aware intra-ONU scheduling for passive optical networks with inter-ONU traffic

    Science.gov (United States)

    Li, Yan; Dai, Shifang; Wu, Weiwei

    2016-12-01

    Recently, with the soaring of traffic among optical network units (ONUs), network coding (NC) is becoming an appealing technique for improving the performance of passive optical networks (PONs) with such inter-ONU traffic. However, in the existed NC-based PONs, NC can only be implemented by buffering inter-ONU traffic at the optical line terminal (OLT) to wait for the establishment of coding condition, such passive uncertain waiting severely limits the effect of NC technique. In this paper, we will study integrated coding-aware intra-ONU scheduling in which the scheduling of inter-ONU traffic within each ONU will be undertaken by the OLT to actively facilitate the forming of coding inter-ONU traffic based on the global inter-ONU traffic distribution, and then the performance of PONs with inter-ONU traffic can be significantly improved. We firstly design two report message patterns and an inter-ONU traffic transmission framework as the basis for the integrated coding-aware intra-ONU scheduling. Three specific scheduling strategies are then proposed for adapting diverse global inter-ONU traffic distributions. The effectiveness of the work is finally evaluated by both theoretical analysis and simulations.

  19. Prediction of traffic fatalities and prospects for mobility becoming ...

    Indian Academy of Sciences (India)

    Since in developing countries long series of traffic volume data are absent, another model for the fit and prediction of road traffic fatalities for developing countries is used, ... It might be assumed that the developing countries could learn faster to increase their road safety by knowledge transfer from developed countries.

  20. Integration of a network aware traffic generation device into a computer network emulation platform

    CSIR Research Space (South Africa)

    Von Solms, S

    2014-07-01

    Full Text Available Flexible, open source network emulation tools can provide network researchers with significant benefits regarding network behaviour and performance. The evaluation of these networks can benefit greatly from the integration of realistic, network...

  1. Contribution to the Management of Traffic in Networks

    Directory of Open Access Journals (Sweden)

    Filip Chamraz

    2014-01-01

    Full Text Available The paper deals with Admission control methods (AC in IMS networks (IP multimedia subsystem as one of the elements that help ensure QoS (Quality of service. In the paper we are trying to choose the best AC method for selected IMS network to allow access to the greatest number of users. Of the large number of methods that were tested and considered good we chose two. The paper compares diffusion method and one of the measurement based method, specifically „Simple Sum“. Both methods estimate effective bandwidth to allow access for the greatest number of users/devices and allow them access to prepaid services or multimedia content.

  2. Traffic Dimensioning and Performance Modeling of 4G LTE Networks

    Science.gov (United States)

    Ouyang, Ye

    2011-01-01

    Rapid changes in mobile techniques have always been evolutionary, and the deployment of 4G Long Term Evolution (LTE) networks will be the same. It will be another transition from Third Generation (3G) to Fourth Generation (4G) over a period of several years, as is the case still with the transition from Second Generation (2G) to 3G. As a result,…

  3. Prediction of traffic convective instability with spectral analysis of the Aw–Rascle–Zhang model

    Energy Technology Data Exchange (ETDEWEB)

    Belletti, Francois, E-mail: francois.belletti@berkeley.edu [Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (United States); Huo, Mandy, E-mail: mhuo@berkeley.edu [Department of Physics, University of California, Berkeley (United States); Department of Mathematics, University of California, Berkeley (United States); Litrico, Xavier, E-mail: xavier.litrico@lyonnaise-des-eaux.fr [LyRE, R& D center of SUEZ environnement, Bordeaux (France); Bayen, Alexandre M., E-mail: bayen@berkeley.edu [Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (United States); Department of Civil and Environmental Engineering, University of California, Berkeley (United States); Institute of Transportation Studies, University of California, Berkeley (United States)

    2015-10-09

    Highlights: • We linearize and diagonalize the ARZ model. We give a Froude number for traffic. • Spectral domain transfer functions are derived and decompose the model. • The linearized system is convectively unstable in the free-flow regime. • We conduct experiments with the linearized model on the NGSIM dataset. • We show that the linearization does not destroy the accuracy of the model. - Abstract: This article starts from the classical Aw–Rascle–Zhang (ARZ) model for freeway traffic and develops a spectral analysis of its linearized version. A counterpart to the Froude number in hydrodynamics is defined that enables a classification of the nature of vehicle traffic flow using the explicit solution resulting from the analysis. We prove that our linearization about an equilibrium is stable for congested regimes and unstable otherwise. NGSIM data for congested traffic trajectories is used so as to confront the linearized model's predictions to actual macroscopic behavior of traffic. The model is shown to achieve good accuracy for speed and flow. In particular, it accounts for the advection of oscillations on boundaries into the interior domain where the PDE under study is solved.

  4. Comparison between genetic algorithm and self organizing map to detect botnet network traffic

    Science.gov (United States)

    Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

    2017-11-01

    In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

  5. Game theoretic analysis of congestion, safety and security networks, air traffic and emergency departments

    CERN Document Server

    Zhuang, Jun

    2015-01-01

    Maximizing reader insights into the roles of intelligent agents in networks, air traffic and emergency departments, this volume focuses on congestion in systems where safety and security are at stake, devoting special attention to applying game theoretic analysis of congestion to: protocols in wired and wireless networks; power generation, air transportation and emergency department overcrowding. Reviewing exhaustively the key recent research into the interactions between game theory, excessive crowding, and safety and security elements, this book establishes a new research angle by illustrating linkages between the different research approaches and serves to lay the foundations for subsequent analysis. Congestion (excessive crowding) is defined in this work as all kinds of flows; e.g., road/sea/air traffic, people, data, information, water, electricity, and organisms. Analyzing systems where congestion occurs – which may be in parallel, series, interlinked, or interdependent, with flows one way or both way...

  6. CIPP: a versatile analytical model for VBR traffic in ATM networks

    Science.gov (United States)

    Manivasakan, R.; Desai, U. B.; Karandikar, Abhay

    1999-08-01

    Correlated Interarrival time Process (CIPP) has been proposed, for modeling both the composite arrival process of packets in broadband networks and the individual source modeling. The CIPP--a generalization of the Poisson process- - is a stationary counting process and is parameterized by a correlation parameter `p' which represents the degree of correlation in adjacent interarrivals in addition to `(lambda) ' the intensity of the process. In this paper, we present the performance modeling of VBR video traffic in ATM networks, using CIPP/M/1 queue. We first give the expressions for stationary distributions for CIPP/M/1 queue. The, we derive the queuing measures of interest. We simulate a queue with smoothed VBR video trace data as input (with exponential services) to compare with the theoretical measures derived above. Experimental results show that the CIPP/M/1 queue, models well with ATM multiplexer performance with the real world VBR video traffic input.

  7. INCITE: Edge-based Traffic Processing and Inference for High-Performance Networks

    Energy Technology Data Exchange (ETDEWEB)

    Baraniuk, Richard G.; Feng, Wu-chun; Cottrell, Les; Knightly, Edward; Nowak, Robert; Riedi, Rolf

    2005-06-20

    The INCITE (InterNet Control and Inference Tools at the Edge) Project developed on-line tools to characterize and map host and network performance as a function of space, time, application, protocol, and service. In addition to their utility for trouble-shooting problems, these tools will enable a new breed of applications and operating systems that are network aware and resource aware. Launching from the foundation provided our recent leading-edge research on network measurement, multifractal signal analysis, multiscale random fields, and quality of service, our effort consisted of three closely integrated research thrusts that directly attack several key networking challenges of DOE's SciDAC program. These are: Thrust 1, Multiscale traffic analysis and modeling techniques; Thrust 2, Inference and control algorithms for network paths, links, and routers, and Thrust 3, Data collection tools.

  8. An Efficient Tabu Search DSA Algorithm for Heterogeneous Traffic in Cellular Networks

    OpenAIRE

    Kamal, Hany; Coupechoux, Marceau; Godlewski, Philippe

    2010-01-01

    International audience; In this paper, we propose and analyze a TS (Tabu Search) algorithm for DSA (Dynamic Spectrum Access) in cellular networks. We consider a scenario where cellular operators share a common access band, and we focus on the strategy of one operator providing packet services to the end-users. We consider a soft interference requirement for the algorithm's design that suits the packet traffic context. The operator's objective is to maximize its reward while taking into accoun...

  9. A study of Time-varying Cost Parameter Estimation Methods in Traffic Networks for Mobile Robots

    OpenAIRE

    Das, Pragna; Xirgo, Lluís Ribas

    2015-01-01

    Industrial robust controlling systems built using automated guided vehicles (AGVs) requires planning which depends on cost parameters like time and energy of the mobile robots functioning in the system. This work addresses the problem of on-line traversal time identification and estimation for proper mobility of mobile robots on systems' traffic networks. Several filtering and estimation methods have been investigated with respect to proper identification of traversal time of arcs of systems'...

  10. Prediction Based Energy Balancing Forwarding in Cellular Networks

    Directory of Open Access Journals (Sweden)

    Yang Jian-Jun

    2017-01-01

    Full Text Available In the recent cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. Energy is a very important factor in the forwarding of cellular network since mobile users(cell phones in hot cells often suffer from low throughput due to energy lack problems. In many situations, the energy lack problems take place because the energy loading is not balanced. In this paper, we present a prediction based forwarding algorithm to let a mobile node dynamically select the next relay station with highest potential energy capacity to resume communication. Key to this strategy is that a relay station only maintains three past status, and then it is able to predict the potential energy capacity. Then, the node selects the next hop with potential maximal energy. Moreover, a location based algorithm is developed to let the mobile node figure out the target region in order to avoid flooding. Simulations demonstrate that our approach significantly increase the aggregate throughput and decrease the delay in cellular network environment.

  11. Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria.

    Science.gov (United States)

    Ihueze, Chukwutoo C; Onwurah, Uchendu O

    2018-01-04

    One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. 77 FR 3544 - Meeting and Webinar on the Active Traffic and Demand Management and Intelligent Network Flow...

    Science.gov (United States)

    2012-01-24

    ... Management and Intelligent Network Flow Optimization Operational Concepts; Notice of Public Meeting AGENCY... Traffic and Demand Management (ADTM) and Intelligent Network Flow Optimization (INFLO) operational... transportation network. Issued in Washington, DC, on the 18th day of January 2012. John Augustine, Managing...

  13. SmartCop: Enabling Smart Traffic Violations Ticketing in Vehicular Named Data Networks

    Directory of Open Access Journals (Sweden)

    Syed Hassan Ahmed

    2016-01-01

    Full Text Available Recently, various applications for Vehicular Ad hoc Networks (VANETs have been proposed and smart traffic violation ticketing is one of them. On the other hand, the new Information-Centric Networking (ICN architectures have emerged and been investigated into VANETs, such as Vehicular Named Data Networking (VNDN. However, the existing applications in VANETs are not suitable for VNDN paradigm due to the dependency on a “named content” instead of a current “host-centric” approach. Thus, we need to design the emerging and new architectures for VNDN applications. In this paper, we propose a smart traffic violation ticketing (TVT system for VNDN, named as SmartCop, that enables a cop vehicle (CV to issue tickets for traffic violation(s to the offender(s autonomously, once they are in the transmission range of that CV. The ticket issuing delay, messaging cost, and percentage of violations detected for varying number of vehicles, violators, CVs, and vehicles speeds are estimated through simulations. In addition, we provide a road map of future research directions for enabling safe driving experience in future cars aided with VNDN technology.

  14. Admission Control for Multiservices Traffic in Hierarchical Mobile IPv6 Networks by Using Fuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Jung-Shyr Wu

    2012-01-01

    Full Text Available CAC (Call Admission Control plays a significant role in providing QoS (Quality of Service in mobile wireless networks. In addition to much research that focuses on modified Mobile IP to get better efficient handover performance, CAC should be introduced to Mobile IP-based network to guarantee the QoS for users. In this paper, we propose a CAC scheme which incorporates multiple traffic types and adjusts the admission threshold dynamically using fuzzy control logic to achieve better usage of resources. The method can provide QoS in Mobile IPv6 networks with few modifications on MAP (Mobility Anchor Point functionality and slight change in BU (Binding Update message formats. According to the simulation results, the proposed scheme presents good performance of voice and video traffic at the expenses of poor performance on data traffic. It is evident that these CAC schemes can reduce the probability of the handoff dropping and the cell overload and limit the probability of the new call blocking.

  15. Model establishing and performance analysis of service stratum traffic in the integrated sensing network

    Science.gov (United States)

    Ge, Zhiqun; Wang, Ying; Zhang, Xiaolu; Zheng, Yu; Zhao, Xinqun; Sun, Xiaohan

    2017-01-01

    We propose a time-division hybrid-user data flow model scheme based on semi-Markov state-transition algorithm for multiclass business and service in Integrated Sensing Network (ISN). Two typical flow models, visual sense and auditory sense service models, are set up due to the real situation of service stratum traffic, respectively. The experimental system based on the Asynchronous Optical Packet Switching (AOPS) network simulation platform is established for the feasibility of the proposed data flow model. The results show that the proposed models achieve reasonable packet loss rate and delay time in the case of different business and service levels.

  16. A multiclass vehicular dynamic traffic flow model for main roads and dedicated lanes/roads of multimodal transport network

    Energy Technology Data Exchange (ETDEWEB)

    Sossoe, K.S., E-mail: kwami.sossoe@irt-systemx.fr [TECHNOLOGICAL RESEARCH INSTITUTE SYSTEMX (France); Lebacque, J-P., E-mail: jean-patrick.lebacque@ifsttar.fr [UPE/IFSTTAR-COSYS-GRETTIA (France)

    2015-03-10

    We present in this paper a model of vehicular traffic flow for a multimodal transportation road network. We introduce the notion of class of vehicles to refer to vehicles of different transport modes. Our model describes the traffic on highways (which may contain several lanes) and network transit for pubic transportation. The model is drafted with Eulerian and Lagrangian coordinates and uses a Logit model to describe the traffic assignment of our multiclass vehicular flow description on shared roads. The paper also discusses traffic streams on dedicated lanes for specific class of vehicles with event-based traffic laws. An Euler-Lagrangian-remap scheme is introduced to numerically approximate the model’s flow equations.

  17. Link Label Prediction in Signed Citation Network

    KAUST Repository

    Akujuobi, Uchenna

    2016-04-12

    Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the

  18. An empirical model to predict road dust emissions based on pavement and traffic characteristics.

    Science.gov (United States)

    Padoan, Elio; Ajmone-Marsan, Franco; Querol, Xavier; Amato, Fulvio

    2017-11-08

    The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables. We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions

  19. Report on the Dagstuhl Seminar on Visualization and Monitoring of Network Traffic

    Energy Technology Data Exchange (ETDEWEB)

    Keim, Daniel; Pras, Aiko; Schonwalder, Jurgen; Wong, Pak C.; Mansmann, Florian

    2011-01-26

    The Dagstuhl Seminar on Visualization and Monitoring of Network Traffic [1] took place May 17-20, 2009 in Dagstuhl, Germany. Dagstuhl seminars promote personal interaction and open discussion of results as well as new ideas. Unlike at most conferences, the focus is not solely on the presentation of established results but to equal parts on results, ideas, sketches, and open problems. The aim of this particular seminar was to bring together experts from the information visualization community and the networking community in order to discuss the state of the art of monitoring and visualization of network traffic. People from the different research communities involved jointly organized the seminar. The co-chairs of the seminar from the networking community were Aiko Pras (University of Twente) and Jürgen Schönwälder (Jacobs University Bremen). The co-chairs from the visualization community were Daniel A. Keim (University of Konstanz) and Pak Chung Wong (Pacific Northwest National Lab). Florian Mansmann (University of Konstanz) helped with producing this report. The seminar was organized and supported by Schloss Dagstuhl and the EC IST-EMANICS Network of Excellence [1].

  20. Predicting network instabilities in mobile directional wireless networks

    Science.gov (United States)

    Coleman, David M.; Milner, Stuart D.; Davis, Christopher C.

    2013-09-01

    We have been investigating the dynamics of molecular systems as analogies for directional wireless networks. This has provided significant insight into reconfigurations of mobile wireless networks using directional point-to-point links (e.g. free-space optics or radio frequency). In this effort, we conceptualize the network as a giant molecule comprised of atoms that exert forces (attraction and repulsion) that stretch and relax the corresponding links. We monitor second-order variations of a potential energy function to gain an improved understanding of the large dimensionality of the optimized reconfiguration for network topology management. Ultimately, we envision this approach will allow for the prediction of two distinct events: 1) localized link failures and 2) catastrophic network events such as a partition. Our results show the detection of localized link failures and the availability for resource allocation more than one minute ahead of the failure (due to known events such as range and antenna blockage) with <80% accuracy.

  1. Determination of network origin-destination matrices using partial link traffic counts and virtual sensor information in an integrated corridor management framework.

    Science.gov (United States)

    2014-04-01

    Trip origin-destination (O-D) demand matrices are critical components in transportation network : modeling, and provide essential information on trip distributions and corresponding spatiotemporal : traffic patterns in traffic zones in vehicular netw...

  2. A wireless sensor network for urban traffic characterization and trend monitoring.

    Science.gov (United States)

    Fernández-Lozano, J J; Martín-Guzmán, Miguel; Martín-Ávila, Juan; García-Cerezo, A

    2015-10-15

    Sustainable mobility requires a better management of the available infrastructure resources. To achieve this goal, it is necessary to obtain accurate data about road usage, in particular in urban areas. Although a variety of sensor alternates for urban traffic exist, they usually require extensive investments in the form of construction works for installation, processing means, etc. Wireless Sensor Networks (WSN) are an alternative to acquire urban traffic data, allowing for flexible, easy deployment. Together with the use of the appropriate sensors, like Bluetooth identification, and associate processing, WSN can provide the means to obtain in real time data like the origin-destination matrix, a key tool for trend monitoring which previously required weeks or months to be completed. This paper presents a system based on WSN designed to characterize urban traffic, particularly traffic trend monitoring through the calculation of the origin-destination matrix in real time by using Bluetooth identification. Additional sensors are also available integrated in different types of nodes. Experiments in real conditions have been performed, both for separate sensors (Bluetooth, ultrasound and laser), and for the whole system, showing the feasibility of this approach.

  3. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  4. Internet Traffic Prediction Using Artificial Neural Network | Abubakar ...

    African Journals Online (AJOL)

    Journal of Computer Science and Its Application. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 17, No 2 (2010) >. Log in or Register to get access to full text downloads.

  5. Internet Traffic Prediction Using Artificial Neural Network | Abubakar ...

    African Journals Online (AJOL)

    Journal of Computer Science and Its Application. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 17, No 2 (2010) >. Log in or Register to get access to full text downloads.

  6. Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks

    Directory of Open Access Journals (Sweden)

    Tao Chen

    2016-01-01

    Full Text Available Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs among a large pool of servers greatly affects the performance of data center networks (DCNs. As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB and then proved it to be NP-hard and designed a Longest Processing Time Based Placement algorithm (LPTBP algorithm to solve it. To take advantage of the communication locality, we proposed Locality-Aware Virtual Machine Placement Problem with Traffic-Aware Balancing (LVMPPTB, which is a multiobjective optimization problem of simultaneously minimizing the maximum number of VM partitions of requests and minimizing the maximum bandwidth occupancy on uplinks of Top of Rack (ToR switches. We also proved it to be NP-hard and designed a heuristic algorithm (Least-Load First Based Placement algorithm, LLBP algorithm to solve it. Through extensive simulations, the proposed heuristic algorithm is proven to significantly balance the bandwidth occupancy on uplinks of ToR switches, while keeping the number of VM partitions of each request small enough.

  7. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  8. Spectrum Hole Prediction And White Space Ranking For Cognitive Radio Network Using An Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Sunday Iliya

    2015-08-01

    Full Text Available Abstract With spectrum becoming an ever scarcer resource it is critical that new communication systems utilize all the available frequency bands as efficiently as possible in time frequency and spatial domain. rHowever spectrum allocation policies most of the licensed spectrums grossly underutilized while the unlicensed spectrums are overcrowded. Hence all future wireless communication devices beequipped with cognitive capability to maximize quality of service QoS require a lot of time and energartificial intelligence and machine learning in cognitive radio deliver optimum performance. In this paper we proposed a novel way of spectrum holes prediction using artificial neural network ANN. The ANN was trained to adapt to the radio spectrum traffic of 20 channels and the trained network was used for prediction of future spectrum holes. The input of the neural network consist of a time domain vector of length six i.e. minute hour date day week and month. The output is a vector of length 20 each representing the probability of the channel being idle. The channels are ranked in order of decreasing probability of being idleminimizing We assumed that all the channels have the same noise and quality of service and only one vacant channel is needed for communication. The result of the spectrum holes search using ANN was compared with that of blind linear and blind stochastic search and was found to be superior. The performance of the ANN that was trained to predict the probability of the channels being idle outperformed the ANN that will predict the exact channel states busy or idle. In the ANN that was trained to predict the exact channels states all channels predicted to be idle are randomly searched until the first spectrum hole was found no information about search direction regarding which channel should be sensed first.

  9. Big data analytics : predicting traffic flow regimes from simulated connected vehicle messages using data analytics and machine learning.

    Science.gov (United States)

    2016-12-25

    The key objectives of this study were to: 1. Develop advanced analytical techniques that make use of a dynamically configurable connected vehicle message protocol to predict traffic flow regimes in near-real time in a virtual environment and examine ...

  10. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  11. Structural network efficiency predicts conversion to dementia

    NARCIS (Netherlands)

    Tuladhar, A.; van Uden, I.W.M.; Rutten-Jacobs, L.C.A.; van der Holst, H.; van Norden, A.; de Laat, K.; Dijk, E.; Claassen, J.A.H.R.; Kessels, R.P.C.; Markus, H.S.; Norris, David Gordon; de Leeuw, F.E.

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen

  12. Performance Evaluation of IEEE 802.11ah Networks With High-Throughput Bidirectional Traffic.

    Science.gov (United States)

    Šljivo, Amina; Kerkhove, Dwight; Tian, Le; Famaey, Jeroen; Munteanu, Adrian; Moerman, Ingrid; Hoebeke, Jeroen; De Poorter, Eli

    2018-01-23

    So far, existing sub-GHz wireless communication technologies focused on low-bandwidth, long-range communication with large numbers of constrained devices. Although these characteristics are fine for many Internet of Things (IoT) applications, more demanding application requirements could not be met and legacy Internet technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP) could not be used. This has changed with the advent of the new IEEE 802.11ah Wi-Fi standard, which is much more suitable for reliable bidirectional communication and high-throughput applications over a wide area (up to 1 km). The standard offers great possibilities for network performance optimization through a number of physical- and link-layer configurable features. However, given that the optimal configuration parameters depend on traffic patterns, the standard does not dictate how to determine them. Such a large number of configuration options can lead to sub-optimal or even incorrect configurations. Therefore, we investigated how two key mechanisms, Restricted Access Window (RAW) grouping and Traffic Indication Map (TIM) segmentation, influence scalability, throughput, latency and energy efficiency in the presence of bidirectional TCP/IP traffic. We considered both high-throughput video streaming traffic and large-scale reliable sensing traffic and investigated TCP behavior in both scenarios when the link layer introduces long delays. This article presents the relations between attainable throughput per station and attainable number of stations, as well as the influence of RAW, TIM and TCP parameters on both. We found that up to 20 continuously streaming IP-cameras can be reliably connected via IEEE 802.11ah with a maximum average data rate of 160 kbps, whereas 10 IP-cameras can achieve average data rates of up to 255 kbps over 200 m. Up to 6960 stations transmitting every 60 s can be connected over 1 km with no lost packets. The presented results enable the fine tuning

  13. A Novel Architecture for Adaptive Traffic Control in Network on Chip using Code Division Multiple Access Technique

    OpenAIRE

    Fatemeh. Dehghani; Shahram. Darooei

    2016-01-01

    Network on chip has emerged as a long-term and effective method in Multiprocessor System-on-Chip communications in order to overcome the bottleneck in bus based communication architectures. Efficiency and performance of network on chip is so dependent on the architecture and structure of the network. In this paper a new structure and architecture for adaptive traffic control in network on chip using Code Division Multiple Access technique is presented. To solve the problem of synchronous acce...

  14. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  15. Container traffic prediction in a seaport stockyard implementing artificial bee colony algorithm compared with the genetic algorithm

    OpenAIRE

    Gökkuş, Ümit; Akoğlu, Kıvanç; Yıldırım, Mehmet

    2017-01-01

    Container traffic prediction in a seaport stockyard implementing artificial bee colony algorithm compared with the genetic algorithmAfter finishing the construction stage of a container terminal, it is very difficult to modify the current port configuration and properties. For this instance, to avoid of the sunk costs, it is crucial for the seaport designers to predict the container traffic at any stage of the design phase. One of the important facilities of a seaport is the container stockya...

  16. Blood glucose prediction using neural network

    Science.gov (United States)

    Soh, Chit Siang; Zhang, Xiqin; Chen, Jianhong; Raveendran, P.; Soh, Phey Hong; Yeo, Joon Hock

    2008-02-01

    We used neural network for blood glucose level determination in this study. The data set used in this study was collected using a non-invasive blood glucose monitoring system with six laser diodes, each laser diode operating at distinct near infrared wavelength between 1500nm and 1800nm. The neural network is specifically used to determine blood glucose level of one individual who participated in an oral glucose tolerance test (OGTT) session. Partial least squares regression is also used for blood glucose level determination for the purpose of comparison with the neural network model. The neural network model performs better in the prediction of blood glucose level as compared with the partial least squares model.

  17. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    Directory of Open Access Journals (Sweden)

    Feng Zhong-xiang

    2014-01-01

    Full Text Available In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  18. International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies.

    Science.gov (United States)

    Morley, D W; de Hoogh, K; Fecht, D; Fabbri, F; Bell, M; Goodman, P S; Elliott, P; Hodgson, S; Hansell, A L; Gulliver, J

    2015-11-01

    The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)). Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    Science.gov (United States)

    Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

    2014-01-01

    In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

  20. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

    Directory of Open Access Journals (Sweden)

    Jisheng Zhang

    2015-06-01

    Full Text Available It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks.

  1. The Model of Severity Prediction of Traffic Crash on the Curve

    Directory of Open Access Journals (Sweden)

    Jian-feng Xi

    2014-01-01

    Full Text Available With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors. Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure. Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.

  2. Problems in air traffic management. VII., Job training performance of air traffic control specialists - measurement, structure, and prediction.

    Science.gov (United States)

    1965-07-01

    A statistical study of training- and job-performance measures of several hundred Air Traffic Control Specialists (ATCS) representing Enroute, Terminal, and Flight Service Station specialties revealed that training-performance measures reflected: : 1....

  3. TCP with source traffic shaping (TCP-STS): an approach for network congestion reduction

    Science.gov (United States)

    Elaywe, Ali H.; Kamal, Ahmed E.

    2002-07-01

    The Transmission Control Protocol (TCP), provides flow control functions which are based on the window mechanism. Packet losses are detected by various mechanisms, such as timeouts and duplicate acknowledgements, and are then recovered from using different techniques. A problem that arises with the use of window based mechanisms is that the availability of a large number of credits at the source may cause a source to flood the network with back-to-back packets, which may drive the network into congestion, especially if multiple sources become active at the same time. In this paper we propose a new approach for congestion reduction. The approach works by shaping the traffic at the TCP source, such that the basic TCP flow control mechanism is still preserved, but the packet transmissions are spaced in time in order to prevent a sudden surge of traffic from overflowing the routers' buffers. Simulation results show that this technique can result in an improved network performance, in terms of reduced mean delay, delay variance, and packet dropping ratio.

  4. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    Full Text Available Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS and Internet of Things (IoT, transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  5. Large-scale transportation network congestion evolution prediction using deep learning theory.

    Science.gov (United States)

    Ma, Xiaolei; Yu, Haiyang; Wang, Yunpeng; Wang, Yinhai

    2015-01-01

    Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

  6. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  7. Temporal Classification Error Compensation of Convolutional Neural Network for Traffic Sign Recognition

    Science.gov (United States)

    Yoon, Seungjong; Kim, Eungtae

    2017-02-01

    In this paper, we propose the method that classifies the traffic signs by using Convolutional Neural Network(CNN) and compensates the error rate of CNN using the temporal correlation between adjacent successive frames. Instead of applying a conventional CNN architecture with more layers, Temporal Classification Error Compensation(TCEC) is proposed to improve the error rate in the architecture which has less nodes and layers than a conventional CNN. Experimental results show that the complexity of the proposed method could be reduced by 50% compared with that of the conventional CNN with same layers, and the error rate could be improved by about 3%.

  8. A Practical Method for Multilevel Classification and Accounting of Traffic in Computer Networks

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Pedersen, Jens Myrup

    the service provider (as Facebook, YouTube, or Google). Furthermore, Deep Packet Inspection (DPI), which seems to be the most accurate technique, in addition to the extensive needs for resources, often cannot be used by ISPs in their networks due to privacy or legal reasons. Techniques based on Machine......, SSH, and Telnet. Within each application group we identify a number of behaviors -- for example, for HTTP, we selected file transfer, web browsing, web radio, and unknown. Our system built based on the method provides also traffic accounting and it was tested on 2 datasets. The classification results...

  9. Predicting biological networks from genomic data

    DEFF Research Database (Denmark)

    Harrington, Eoghan D; Jensen, Lars J; Bork, Peer

    2008-01-01

    Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks...... provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate...

  10. Traffic forecaster for MPLS multimedia data streams

    Science.gov (United States)

    Ambrose, Barry; Lin, Freddie

    2006-05-01

    Contemporary high performance data networks carry a wide range of multimedia services (voice, video, audio, text, sensor data, etc.) that require an outstanding Quality of Service (QoS) to provide performance guarantees in priority delivery, latency, bandwidth utilization, load balancing, etc. With the advent of recent Multi-Protocol Label Switching (MPLS) network standards, the QoS has made significant progress in performance to provide these performance guarantees. Right now, attention has turned to the task of managing these QoS networks more efficiently through the handling of network traffic. We have investigated a novel Network Traffic Forecasting Assisted QoS Planner technology that will provide constantly updated forecasts of data traffic and server loads to any application that needs this information. Using source models of voice, video and data traffic based on empirical studies of TCP/IP data traffic carried out by Paxson and Floyd in the 1990's, our studies have shown that the system may provide up to 43% bandwidth savings for MPLS data streams, by predicting future traffic flows and reserving network resources to accommodate the predicted traffic. The system additionally provides a means to submit bandwidth reservation requests for those applications that need assured service guarantees for data delivery. The technology essentially increases the efficiency and effectiveness of multimedia information and communication network infrastructure that supports multiple or adaptive QoS levels for multimedia data networking and information system applications.

  11. Adaptive EWMA Method Based on Abnormal Network Traffic for LDoS Attacks

    Directory of Open Access Journals (Sweden)

    Dan Tang

    2014-01-01

    Full Text Available The low-rate denial of service (LDoS attacks reduce network services capabilities by periodically sending high intensity pulse data flows. For their concealed performance, it is more difficult for traditional DoS detection methods to detect LDoS attacks; at the same time the accuracy of the current detection methods for LDoS attacks is relatively low. As the fact that LDoS attacks led to abnormal distribution of the ACK traffic, LDoS attacks can be detected by analyzing the distribution characteristics of ACK traffic. Then traditional EWMA algorithm which can smooth the accidental error while being the same as the exceptional mutation may cause some misjudgment; therefore a new LDoS detection method based on adaptive EWMA (AEWMA algorithm is proposed. The AEWMA algorithm which uses an adaptive weighting function instead of the constant weighting of EWMA algorithm can smooth the accidental error and retain the exceptional mutation. So AEWMA method is more beneficial than EWMA method for analyzing and measuring the abnormal distribution of ACK traffic. The NS2 simulations show that AEWMA method can detect LDoS attacks effectively and has a low false negative rate and a false positive rate. Based on DARPA99 datasets, experiment results show that AEWMA method is more efficient than EWMA method.

  12. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qiang Niu

    2016-10-01

    Full Text Available Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS, which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.

  14. Combination Adaptive Traffic Algorithm and Coordinated Sleeping in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    M. Udin Harun Al Rasyid

    2014-12-01

    Full Text Available Wireless sensor network (WSN uses a battery as its primary power source, so that WSN will be limited to battery power for long operations. The WSN should be able to save the energy consumption in order to operate in a long time.WSN has the potential to be the future of wireless communications solutions. WSN are small but has a variety of functions that can help human life. WSN has the wide variety of sensors and can communicate quickly making it easier for people to obtain information accurately and quickly. In this study, we combine adaptive traffic algorithms and coordinated sleeping as power‐efficient WSN solution. We compared the performance of our proposed ideas combination adaptive traffic and coordinated sleeping algorithm with non‐adaptive scheme. From the simulation results, our proposed idea has good‐quality data transmission and more efficient in energy consumption, but it has higher delay than that of non‐adaptive scheme. Keywords:WSN,adaptive traffic,coordinated sleeping,beacon order,superframe order.

  15. Visualizing Network Traffic to Understand the Performance of Massively Parallel Simulations

    KAUST Repository

    Landge, A. G.

    2012-12-01

    The performance of massively parallel applications is often heavily impacted by the cost of communication among compute nodes. However, determining how to best use the network is a formidable task, made challenging by the ever increasing size and complexity of modern supercomputers. This paper applies visualization techniques to aid parallel application developers in understanding the network activity by enabling a detailed exploration of the flow of packets through the hardware interconnect. In order to visualize this large and complex data, we employ two linked views of the hardware network. The first is a 2D view, that represents the network structure as one of several simplified planar projections. This view is designed to allow a user to easily identify trends and patterns in the network traffic. The second is a 3D view that augments the 2D view by preserving the physical network topology and providing a context that is familiar to the application developers. Using the massively parallel multi-physics code pF3D as a case study, we demonstrate that our tool provides valuable insight that we use to explain and optimize pF3D-s performance on an IBM Blue Gene/P system. © 1995-2012 IEEE.

  16. Network-Wide Traffic Anomaly Detection and Localization Based on Robust Multivariate Probabilistic Calibration Model

    Directory of Open Access Journals (Sweden)

    Yuchong Li

    2015-01-01

    Full Text Available Network anomaly detection and localization are of great significance to network security. Compared with the traditional methods of host computer, single link and single path, the network-wide anomaly detection approaches have distinctive advantages with respect to detection precision and range. However, when facing the actual problems of noise interference or data loss, the network-wide anomaly detection approaches also suffer significant performance reduction or may even become unavailable. Besides, researches on anomaly localization are rare. In order to solve the mentioned problems, this paper presents a robust multivariate probabilistic calibration model for network-wide anomaly detection and localization. It applies the latent variable probability theory with multivariate t-distribution to establish the normal traffic model. Not only does the algorithm implement network anomaly detection by judging whether the sample’s Mahalanobis distance exceeds the threshold, but also it locates anomalies by contribution analysis. Both theoretical analysis and experimental results demonstrate its robustness and wider use. The algorithm is applicable when dealing with both data integrity and loss. It also has a stronger resistance over noise interference and lower sensitivity to the change of parameters, all of which indicate its performance stability.

  17. Cybersecurity and Network Forensics: Analysis of Malicious Traffic towards a Honeynet with Deep Packet Inspection

    Directory of Open Access Journals (Sweden)

    Gabriel Arquelau Pimenta Rodrigues

    2017-10-01

    Full Text Available Any network connected to the Internet is subject to cyber attacks. Strong security measures, forensic tools, and investigators contribute together to detect and mitigate those attacks, reducing the damages and enabling reestablishing the network to its normal operation, thus increasing the cybersecurity of the networked environment. This paper addresses the use of a forensic approach with Deep Packet Inspection to detect anomalies in the network traffic. As cyber attacks may occur on any layer of the TCP/IP networking model, Deep Packet Inspection is an effective way to reveal suspicious content in the headers or the payloads in any packet processing layer, excepting of course situations where the payload is encrypted. Although being efficient, this technique still faces big challenges. The contributions of this paper rely on the association of Deep Packet Inspection with forensics analysis to evaluate different attacks towards a Honeynet operating in a network laboratory at the University of Brasilia. In this perspective, this work could identify and map the content and behavior of attacks such as the Mirai botnet and brute-force attacks targeting various different network services. Obtained results demonstrate the behavior of automated attacks (such as worms and bots and non-automated attacks (brute-force conducted with different tools. The data collected and analyzed is then used to generate statistics of used usernames and passwords, IP and services distribution, among other elements. This paper also discusses the importance of network forensics and Chain of Custody procedures to conduct investigations and shows the effectiveness of the mentioned techniques in evaluating different attacks in networks.

  18. Defending Tor from Network Adversaries: A Case Study of Network Path Prediction

    Directory of Open Access Journals (Sweden)

    Juen Joshua

    2015-06-01

    Full Text Available The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems (ASes and Internet exchanges (IXes, which can observe different overlay hops belonging to the same circuit. We evaluate whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by collecting 17.2 million traceroutes from Tor relays to destinations around the Internet. We compare the collected traceroute paths to predicted paths using state-of-the-art path inference techniques. We find that traceroutes present a very different picture, with the set of ASes seen in the traceroute path differing from the predicted path 80% of the time. We also consider the impact that prediction errors have on Tor security. Using a simulator to choose paths over a week, our traceroutes indicate a user has nearly a 100% chance of at least one compromise in a week with 11% of total paths containing an AS compromise and less than 1% containing an IX compromise when using default Tor selection. We find modifying the path selection to choose paths predicted to be safe lowers total paths with an AS compromise to 0.14% but still presents a 5–11% chance of at least one compromise in a week while making 5% of paths fail, with 96% of failures due to false positives in path inferences. Our results demonstrate more measurement and better path prediction is necessary to mitigate the risk of AS and IX adversaries to Tor.

  19. Advancing interconnect density for spiking neural network hardware implementations using traffic-aware adaptive network-on-chip routers.

    Science.gov (United States)

    Carrillo, Snaider; Harkin, Jim; McDaid, Liam; Pande, Sandeep; Cawley, Seamus; McGinley, Brian; Morgan, Fearghal

    2012-09-01

    The brain is highly efficient in how it processes information and tolerates faults. Arguably, the basic processing units are neurons and synapses that are interconnected in a complex pattern. Computer scientists and engineers aim to harness this efficiency and build artificial neural systems that can emulate the key information processing principles of the brain. However, existing approaches cannot provide the dense interconnect for the billions of neurons and synapses that are required. Recently a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an efficient, robust computing platform. However, the use of the NoC as an interconnection fabric for large-scale SNNs demands a good trade-off between scalability, throughput, neuron/synapse ratio and power consumption. This paper presents a novel traffic-aware, adaptive NoC router, which forms part of a proposed embedded mixed-signal SNN architecture called EMBRACE (EMulating Biologically-inspiRed ArChitectures in hardwarE). The proposed adaptive NoC router provides the inter-neuron connectivity for EMBRACE, maintaining router communication and avoiding dropped router packets by adapting to router traffic congestion. Results are presented on throughput, power and area performance analysis of the adaptive router using a 90 nm CMOS technology which outperforms existing NoCs in this domain. The adaptive behaviour of the router is also verified on a Stratix II FPGA implementation of a 4 × 2 router array with real-time traffic congestion. The presented results demonstrate the feasibility of using the proposed adaptive NoC router within the EMBRACE architecture to realise large-scale SNNs on embedded hardware. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  1. Person Movement Prediction Using Neural Networks

    OpenAIRE

    Vintan, Lucian; Gellert, Arpad; Petzold, Jan; Ungerer, Theo

    2006-01-01

    Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user's desires and acts proactively. This paper proposes neural prediction techniques to anticipate a person's next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The optimal configuration of the neural network is determined by evaluating movement sequences of real p...

  2. Predicting economic growth with stock networks

    Science.gov (United States)

    Heiberger, Raphael H.

    2018-01-01

    Networks derived from stock prices are often used to model developments on financial markets and are tightly intertwined with crises. Yet, the influence of changing market topologies on the broader economy (i.e. GDP) is unclear. In this paper, we propose a Bayesian approach that utilizes individual-level network measures of companies as lagged probabilistic features to predict national economic growth. We use a comprehensive data set consisting of Standard and Poor's 500 corporations from January 1988 until October 2016. The final model forecasts correctly all major recession and prosperity phases of the U.S. economy up to one year ahead. By employing different network measures on the level of corporations, we can also identify which companies' stocks possess a key role in a changing economic environment and may be used as indication of critical (and prosperous) developments. More generally, the proposed approach allows to predict probabilities for different overall states of social entities by using local network positions and could be applied on various phenomena.

  3. Traffic Incident Clearance Time and Arrival Time Prediction Based on Hazard Models

    Directory of Open Access Journals (Sweden)

    Yang beibei Ji

    2014-01-01

    Full Text Available Accurate prediction of incident duration is not only important information of Traffic Incident Management System, but also an effective input for travel time prediction. In this paper, the hazard based prediction models are developed for both incident clearance time and arrival time. The data are obtained from the Queensland Department of Transport and Main Roads’ STREAMS Incident Management System (SIMS for one year ending in November 2010. The best fitting distributions are drawn for both clearance and arrival time for 3 types of incident: crash, stationary vehicle, and hazard. The results show that Gamma, Log-logistic, and Weibull are the best fit for crash, stationary vehicle, and hazard incident, respectively. The obvious impact factors are given for crash clearance time and arrival time. The quantitative influences for crash and hazard incident are presented for both clearance and arrival. The model accuracy is analyzed at the end.

  4. Reliable Path Selection Problem in Uncertain Traffic Network after Natural Disaster

    Directory of Open Access Journals (Sweden)

    Jing Wang

    2013-01-01

    Full Text Available After natural disaster, especially for large-scale disasters and affected areas, vast relief materials are often needed. In the meantime, the traffic networks are always of uncertainty because of the disaster. In this paper, we assume that the edges in the network are either connected or blocked, and the connection probability of each edge is known. In order to ensure the arrival of these supplies at the affected areas, it is important to select a reliable path. A reliable path selection model is formulated, and two algorithms for solving this model are presented. Then, adjustable reliable path selection model is proposed when the edge of the selected reliable path is broken. And the corresponding algorithms are shown to be efficient both theoretically and numerically.

  5. Improved network convergence and quality of service by strict priority queuing of routing traffic

    Science.gov (United States)

    Balandin, Sergey; Heiner, Andreas P.

    2002-07-01

    During the transient period after a link failure the network cannot guarantee the agreed service levels to user data. This is due to the fact that forwarding tables in the network are inconsistent. Moreover, link states can inadvertently be advertised wrong due to protocol time outs, which may result in persistent route flaps. Reducing the probability of wrongly advertised link states, and the time during which the forwarding tables are inconsistent, is therefore of eminent importance to provide consistent and high level QoS to user data. By queuing routing traffic in a queue with strict priority over all other (data) queues, i.e. assigning the highest priority in a Differentiated Services model, we were able to reduce the probability of routing data loss to almost zero, and reduce flooding times almost to their theoretical limit. The quality of service provided to user traffic was considerable higher than without the proposed modification. The scheme is independent of the routing protocol, and can be used with most differentiated service models. It is compatible with the current OSPF standard, and can be used in conjunction with other improvements in the protocol with similar objectives.

  6. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks.

    Science.gov (United States)

    Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

    2017-09-15

    Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller's direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20-40% while ensuring feasible data delay.

  7. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yunkai Wei

    2017-09-01

    Full Text Available Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs are an inexorable trend for Wireless Sensor Networks (WSNs, including Wireless Rechargeable Sensor Network (WRSNs. However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay.

  8. Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Rechargeable Sensor Networks

    Science.gov (United States)

    Wei, Yunkai; Ma, Xiaohui; Yang, Ning; Chen, Yijin

    2017-01-01

    Software Defined Wireless Rechargeable Sensor Networks (SDWRSNs) are an inexorable trend for Wireless Sensor Networks (WSNs), including Wireless Rechargeable Sensor Network (WRSNs). However, the traditional network devices cannot be completely substituted in the short term. Hybrid SDWRSNs, where software defined devices and traditional devices coexist, will last for a long time. Hybrid SDWRSNs bring new challenges as well as opportunities for energy saving issues, which is still a key problem considering that the wireless chargers are also exhaustible, especially in some rigid environment out of the main supply. Numerous energy saving schemes for WSNs, or even some works for WRSNs, are no longer suitable for the new features of hybrid SDWRSNs. To solve this problem, this paper puts forward an Energy-saving Traffic Scheduling (ETS) algorithm. The ETS algorithm adequately considers the new characters in hybrid SDWRSNs, and takes advantage of the Software Defined Networking (SDN) controller’s direct control ability on SDN nodes and indirect control ability on normal nodes. The simulation results show that, comparing with traditional Minimum Transmission Energy (MTE) protocol, ETS can substantially improve the energy efficiency in hybrid SDWRSNs for up to 20–40% while ensuring feasible data delay. PMID:28914816

  9. Modelling and control of broadband traffic using multiplicative ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Modelling of broadband network traffic has emerged as an active area in the last decade. This has been primarily ... that broadband network traffic exhibits statistical self-similarity triggered off an immense volume of ...... with CAC control techniques and adaptive prediction of delay for bandwidth management in broadband ...

  10. A semi-empirical model for predicting the effect of changes in traffic flow patterns on carbon monoxide concentrations

    Science.gov (United States)

    Dirks, Kim N.; Johns, Murray D.; Hay, John E.; Sturman, Andrew P.

    A simple semi-empirical model for predicting the effect of changes in traffic flow patterns on carbon monoxide concentrations is presented. The traffic component of the model requires average vehicle emission rate estimates for a range of driving conditions, as well as traffic flow data for the site of interest. The dispersion component of the model is based on a modified empirically optimised box model requiring only wind speed and direction information. The model is evaluated at a suburban site in Hamilton, New Zealand. Despite the simplicity of the model, produces reliable concentration predictions when tested on days with significantly different traffic flow patterns from those days with which the optimum model parameters were evaluated.

  11. Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

    Science.gov (United States)

    Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

    2018-01-31

    This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Time-Predictable Communication on a Time-Division Multiplexing Network-on-Chip Multicore

    DEFF Research Database (Denmark)

    Sørensen, Rasmus Bo

    This thesis presents time-predictable inter-core communication on a multicore platform with a time-division multiplexing (TDM) network-on-chip (NoC) for hard real-time systems. The thesis is structured as a collection of papers that contribute within the areas of: reconfigurable TDM NoCs, static...... of the Argo NoC network interface (NI) that supports instantaneous reconfiguration, a TDM traffic scheduler that generates virtual circuit (VC) configurations for the Argo NoC, and software functions for two types of intercore communication. The new generation of the Argo NoC adds the capability...

  13. Prediction of surface distress using neural networks

    Science.gov (United States)

    Hamdi, Hadiwardoyo, Sigit P.; Correia, A. Gomes; Pereira, Paulo; Cortez, Paulo

    2017-06-01

    Road infrastructures contribute to a healthy economy throughout a sustainable distribution of goods and services. A road network requires appropriately programmed maintenance treatments in order to keep roads assets in good condition, providing maximum safety for road users under a cost-effective approach. Surface Distress is the key element to identify road condition and may be generated by many different factors. In this paper, a new approach is aimed to predict Surface Distress Index (SDI) values following a data-driven approach. Later this model will be accordingly applied by using data obtained from the Integrated Road Management System (IRMS) database. Artificial Neural Networks (ANNs) are used to predict SDI index using input variables related to the surface of distress, i.e., crack area and width, pothole, rutting, patching and depression. The achieved results show that ANN is able to predict SDI with high correlation factor (R2 = 0.996%). Moreover, a sensitivity analysis was applied to the ANN model, revealing the influence of the most relevant input parameters for SDI prediction, namely rutting (59.8%), crack width (29.9%) and crack area (5.0%), patching (3.0%), pothole (1.7%) and depression (0.3%).

  14. Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

    Science.gov (United States)

    Roy, Sandip; Sridhar, Banavar

    2016-01-01

    Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure

  15. Energy prediction using spatiotemporal pattern networks

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

    2017-11-01

    This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

  16. Multi-agent model predictive control with applications to power networks

    NARCIS (Netherlands)

    Negenborn, R.R.

    2007-01-01

    Transportation networks, such as power networks, road traffic networks, water distribution networks, railway networks, etc., are the corner stones of our modern society. As transportation networks have to operate closer and closer to their capacity limits and as the dynamics of these networks become

  17. Energy-Saving Mechanism in WDM/TDM-PON Based on Upstream Network Traffic

    Directory of Open Access Journals (Sweden)

    Paola Garfias

    2014-08-01

    Full Text Available One of the main challenges of Passive Optical Networks (PONs is the resource (bandwidth and wavelength management. Since it has been shown that access networks consume a significant part of the overall energy of the telecom networks, the resource management schemes should also consider energy minimization strategies. To sustain the increased bandwidth demand of emerging applications in the access section of the network, it is expected that next generation optical access networks will adopt the wavelength division/time division multiplexing (WDM/TDM technique to increase PONs capacity. Compared with traditional PONs, the architecture of a WDM/TDM-PON requires more transceivers/receivers, hence they are expected to consume more energy. In this paper, we focus on the energy minimization in WDM/TDM-PONs and we propose an energy-efficient Dynamic Bandwidth and Wavelength Allocation mechanism whose objective is to turn off, whenever possible, the unnecessary upstream traffic receivers at the Optical Line Terminal (OLT. We evaluate our mechanism in different scenarios and show that the proper use of upstream channels leads to relevant energy savings. Our proposed energy-saving mechanism is able to save energy at the OLT while maintaining the introduced penalties in terms of packet delay and cycle time within an acceptable range. We might highlight the benefits of our proposal as a mechanism that maximizes the channel utilization. Detailed implementation of the proposed algorithm is presented, and simulation results are reported to quantify energy savings and effects on network performance on different network scenarios.

  18. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  19. A Prediction System Using a P2P Overlay Network for a Bus Arrival System

    Directory of Open Access Journals (Sweden)

    Ssu-Hsuan Lu

    2014-01-01

    Full Text Available Along with the evolution of times and the surge of metropolitan populations, government agencies often promote the construction of public transport. Unlike rail transportation or rapid transit systems, it is often difficult to estimate the vehicle arrival times at each station in a bus transportation system due to metropolitan transportation congestion. Traffic status is often monitored using wireless sensor networks (WSNs. However, WSNs are always separated from one another spatially. Recent studies have considered the connection of multiple sensor networks. This study considers a combination view of peer-to-peer (P2P overlay networks and WSN architecture to predict bus arrival times. Each bus station, which is also a P2P overlay peer, is connected in a P2P overlay network. A sensor installed in each bus can receive data via peers to obtain the moving speed of a bus. Then, each peer can exchange its data to predict bus arrival times at bus stations. This method can considerably increase the accuracy with which bus arrival times can be predicted and can provide traffic status with high precision. Furthermore, these data can also be used to plan new bus routes according to the information gathered.

  20. An Interference-Aware Traffic-Priority-Based Link Scheduling Algorithm for Interference Mitigation in Multiple Wireless Body Area Networks

    Directory of Open Access Journals (Sweden)

    Thien T. T. Le

    2016-12-01

    Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.

  1. The predictive value of the NICE "red traffic lights" in acutely ill children.

    Science.gov (United States)

    Kerkhof, Evelien; Lakhanpaul, Monica; Ray, Samiran; Verbakel, Jan Y; Van den Bruel, Ann; Thompson, Matthew; Berger, Marjolein Y; Moll, Henriette A; Oostenbrink, Rianne

    2014-01-01

    Early recognition and treatment of febrile children with serious infections (SI) improves prognosis, however, early detection can be difficult. We aimed to validate the predictive rule-in value of the National Institute for Health and Clinical Excellence (NICE) most severe alarming signs or symptoms to identify SI in children. The 16 most severe ("red") features of the NICE traffic light system were validated in seven different primary care and emergency department settings, including 6,260 children presenting with acute illness. We focussed on the individual predictive value of single red features for SI and their combinations. Results were presented as positive likelihood ratios, sensitivities and specificities. We categorised "general" and "disease-specific" red features. Changes in pre-test probability versus post-test probability for SI were visualised in Fagan nomograms. Almost all red features had rule-in value for SI, but only four individual red features substantially raised the probability of SI in more than one dataset: "does not wake/stay awake", "reduced skin turgor", "non-blanching rash", and "focal neurological signs". The presence of ≥ 3 red features improved prediction of SI but still lacked strong rule-in value as likelihood ratios were below 5. The rule-in value of the most severe alarming signs or symptoms of the NICE traffic light system for identifying children with SI was limited, even when multiple red features were present. Our study highlights the importance of assessing the predictive value of alarming signs in clinical guidelines prior to widespread implementation in routine practice.

  2. The predictive value of the NICE "red traffic lights" in acutely ill children.

    Directory of Open Access Journals (Sweden)

    Evelien Kerkhof

    Full Text Available OBJECTIVE: Early recognition and treatment of febrile children with serious infections (SI improves prognosis, however, early detection can be difficult. We aimed to validate the predictive rule-in value of the National Institute for Health and Clinical Excellence (NICE most severe alarming signs or symptoms to identify SI in children. DESIGN, SETTING AND PARTICIPANTS: The 16 most severe ("red" features of the NICE traffic light system were validated in seven different primary care and emergency department settings, including 6,260 children presenting with acute illness. MAIN OUTCOME MEASURES: We focussed on the individual predictive value of single red features for SI and their combinations. Results were presented as positive likelihood ratios, sensitivities and specificities. We categorised "general" and "disease-specific" red features. Changes in pre-test probability versus post-test probability for SI were visualised in Fagan nomograms. RESULTS: Almost all red features had rule-in value for SI, but only four individual red features substantially raised the probability of SI in more than one dataset: "does not wake/stay awake", "reduced skin turgor", "non-blanching rash", and "focal neurological signs". The presence of ≥ 3 red features improved prediction of SI but still lacked strong rule-in value as likelihood ratios were below 5. CONCLUSIONS: The rule-in value of the most severe alarming signs or symptoms of the NICE traffic light system for identifying children with SI was limited, even when multiple red features were present. Our study highlights the importance of assessing the predictive value of alarming signs in clinical guidelines prior to widespread implementation in routine practice.

  3. Market driven network neutrality and the fallacies of internet traffic quality regulation

    OpenAIRE

    Knieps, Günter

    2011-01-01

    In the U.S. paying for priority arrangements between Internet access service providers and Internet application providers to favor some traffic over other traf-fic is considered unreasonable discrimination. In Europe the focus is on mini-mum traffic quality requirements. It can be shown that neither market power nor universal service arguments can justify traffic quality regulation. In particular, heterogeneous demand for traffic quality for delay sensitive versus delay insen-sitive applicati...

  4. Receiver Based Traffic Control Mechanism to Protect Low Capacity Network in Infrastructure Based Wireless Mesh Network

    Science.gov (United States)

    Gilani, Syed Sherjeel Ahmad; Zubair, Muhammad; Khan, Zeeshan Shafi

    Infrastructure-based Wireless Mesh Networks are emerging as an affordable, robust, flexible and scalable technology. With the advent of Wireless Mesh Networks (WMNs) the dream of connecting multiple technology based networks seems to come true. A fully secure WMN is still a challenge for the researchers. In infrastructure-based WMNs almost all types of existing Wireless Networks like Wi-Fi, Cellular, WiMAX, and Sensor etc can be connected through Wireless Mesh Routers (WMRs). This situation can lead to a security problem. Some nodes can be part of the network with high processing power, large memory and least energy issues while others may belong to a network having low processing power, small memory and serious energy limitations. The later type of the nodes is very much vulnerable to targeted attacks. In our research we have suggested to set some rules on the WMR to mitigate these kinds of targeted flooding attacks. The WMR will then share those set of rules with other WMRs for Effective Utilization of Resources.

  5. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  6. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Science.gov (United States)

    Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

    2016-01-01

    Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  7. An efficient mechanism for dynamic multicast traffic grooming in overlay IP/MPLS over WDM networks

    Science.gov (United States)

    Yu, Xiaojun; Xiao, Gaoxi; Cheng, Tee Hiang

    2014-08-01

    This paper proposes an efficient overlay multicast provisioning (OMP) mechanism for dynamic multicast traffic grooming in overlay IP/MPLS over WDM networks. To facilitate request provisioning, OMP jointly utilizes a data learning (DL) scheme on the IP/MPLS layer for logical link cost estimation, and a lightpath fragmentation (LPF) based method on the WDM layer for improving resource sharing in grooming process. Extensive simulations are carried out to evaluate the performance of OMP mechanism under different traffic loads, with either limited or unlimited port resources. Simulation results demonstrate that OMP significantly outperforms the existing methods. To evaluate the respective influences of the DL scheme and the LPF method on OMP performance, provisioning mechanisms only utilizing either the IP/MPLS layer DL scheme or the WDM layer LPF method are also devised. Comparison results show that both DL and LPF methods help improve OMP blocking performance, and contribution from the DL scheme is more significant when the fixed routing and first-fit wavelength assignment (RWA) strategy is adopted on the WDM layer. Effects of a few other factors, including definition of connection cost to be reported by the WDM layer to the IP/MPLS layer and WDM-layer routing method, on OMP performance are also evaluated.

  8. Wave transmission prediction of multilayer floating breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Hegde, A.V.

    in unison to solve a specific problem. The network learns through examples, so it requires good examples to train properly and further a trained network model can be used for prediction purpose. Proceedings of ICOE 2009 Wave transmission... prediction of multilayer floating breakwater using neural network 577 In order to allow the network to learn both non-linear and linear relationships between input nodes and output nodes, multiple-layer neural networks are often used...

  9. On-Chip SDM Switching for Unicast, Multicast and Traffic Grooming in Data Center Networks

    DEFF Research Database (Denmark)

    Kamchevska, Valerija; Ding, Yunhong; Dalgaard, Kjeld

    2017-01-01

    This paper reports on the use of a novel photonic integrated circuit that facilitates multicast and grooming in an optical data center architecture. The circuit allows for on-chip spatial multiplexing and demultiplexing as well as fiber core switching. Using this device, we experimentally verify...... that multicast and/or grooming can be successfully performed along the full range of output ports, for different group size and different power ratio. Moreover, we experimentally demonstrate SDM transmission and 5 Tbit/s switching using the on-chip fiber switch with integrated fan-in/fan-out devices and achieve...... errorfree performance (BER≤10-9) for a network scenario including simultaneous unicast/multicast switching and traffic grooming....

  10. Modeling Air Traffic Management Technologies with a Queuing Network Model of the National Airspace System

    Science.gov (United States)

    Long, Dou; Lee, David; Johnson, Jesse; Gaier, Eric; Kostiuk, Peter

    1999-01-01

    This report describes an integrated model of air traffic management (ATM) tools under development in two National Aeronautics and Space Administration (NASA) programs -Terminal Area Productivity (TAP) and Advanced Air Transport Technologies (AATT). The model is made by adjusting parameters of LMINET, a queuing network model of the National Airspace System (NAS), which the Logistics Management Institute (LMI) developed for NASA. Operating LMINET with models of various combinations of TAP and AATT will give quantitative information about the effects of the tools on operations of the NAS. The costs of delays under different scenarios are calculated. An extension of Air Carrier Investment Model (ACIM) under ASAC developed by the Institute for NASA maps the technologies' impacts on NASA operations into cross-comparable benefits estimates for technologies and sets of technologies.

  11. Impact of the traffic load on performance of an alternative LTE railway communication network

    DEFF Research Database (Denmark)

    Sniady, Aleksander; Soler, José

    2013-01-01

    obstructing railway operations at big train stations and junctions. Hence, other technologies, such as Long Term Evolution (LTE), need to be considered as an alternative to GSM-R. The goal of this paper is to demonstrate the capacity increase that railways can expect, from the introduction of LTE as internal...... communication infrastructure supporting railway signaling. This work is based on OPNET realistic network simulations, which show the relation between the traffic load (the number of trains transmitting and receiving data in an LTE cell) and the delay performance of the European Train Control System (ETCS......) signaling, which is one of the essential railway communication services. Results of the simulations demonstrate that LTE can solve the urgent capacity problem faced by railways currently deploying GSM-R....

  12. A cost-effective traffic data collection system based on the iDEN mobile telecommunication network.

    Science.gov (United States)

    2008-10-01

    This report describes a cost-effective data collection system for Caltrans 170 traffic signal : controller. The data collection system is based on TCP/IP communication over existing : low-cost mobile communication networks and Motorola iDEN1 mobile...

  13. Predicting local field potentials with recurrent neural networks.

    Science.gov (United States)

    Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

    2016-08-01

    We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

  14. Evolving networks-Using past structure to predict the future

    Science.gov (United States)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  15. Forex Market Prediction Using NARX Neural Network with Bagging

    Directory of Open Access Journals (Sweden)

    Shahbazi Nima

    2016-01-01

    Full Text Available We propose a new methodfor predicting movements in Forex market based on NARX neural network withtime shifting bagging techniqueand financial indicators, such as relative strength index and stochastic indicators. Neural networks have prominent learning ability but they often exhibit bad and unpredictable performance for noisy data. When compared with the static neural networks, our method significantly reducesthe error rate of the responseandimproves the performance of the prediction. We tested three different types ofarchitecture for predicting the response and determined the best network approach. We applied our method to prediction the hourly foreign exchange rates and found remarkable predictability in comprehensive experiments with 2 different foreign exchange rates (GBPUSD and EURUSD.

  16. Emergency Situation Prediction Mechanism: A Novel Approach for Intelligent Transportation System Using Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    P. Ganeshkumar

    2015-01-01

    Full Text Available In Indian four-lane express highway, millions of vehicles are travelling every day. Accidents are unfortunate and frequently occurring in these highways causing deaths, increase in death toll, and damage to infrastructure. A mechanism is required to avoid such road accidents at the maximum to reduce the death toll. An Emergency Situation Prediction Mechanism, a novel and proactive approach, is proposed in this paper for achieving the best of Intelligent Transportation System using Vehicular Ad Hoc Network. ESPM intends to predict the possibility of occurrence of an accident in an Indian four-lane express highway. In ESPM, the emergency situation prediction is done by the Road Side Unit based on (i the Status Report sent by the vehicles in the range of RSU and (ii the road traffic flow analysis done by the RSU. Once the emergency situation or accident is predicted in advance, an Emergency Warning Message is constructed and disseminated to all vehicles in the area of RSU to alert and prevent the vehicles from accidents. ESPM performs well in emergency situation prediction in advance to the occurrence of an accident. ESPM predicts the emergency situation within 0.20 seconds which is comparatively less than the statistical value. The prediction accuracy of ESPM against vehicle density is found better in different traffic scenarios.

  17. A Study of Video Teleconferencing Traffic on a TCP/IP Network

    National Research Council Canada - National Science Library

    Carvey, Harlan

    1997-01-01

    ... of traffic generated by a video teleconferencing application. This information is useful in formulating accurate models to support the various classes of traffic that will dominate the broadband ISDN (B-ISDN or ATM...

  18. Traffic data collection and anonymous vehicle detection using wireless sensor networks : research summary.

    Science.gov (United States)

    2012-05-01

    Problem: : Most Intelligent Transportation System (ITS) applications require distributed : acquisition of various traffic metrics such as traffic speed, volume, and density. : The existing measurement technologies, such as inductive loops, infrared, ...

  19. Traffic data collection and anonymous vehicle detection using wireless sensor networks.

    Science.gov (United States)

    2012-05-01

    New traffic sensing devices based on wireless sensing technologies were designed and tested. Such devices encompass a cost-effective, battery-free, and energy self-sustained architecture for real-time traffic measurement over distributed points in a ...

  20. An Intelligent Traffic Flow Control System Based on Radio Frequency Identification and Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Chao, Kuei-Hsiang; Chen, Pi-Yun

    2014-01-01

    This study primarily focuses on the use of radio frequency identification (RFID) as a form of traffic flow detection, which transmits collected information related to traffic flow directly to a control system through an RS232 interface...

  1. Timing analysis of rate-constrained traffic in TTEthernet using network calculus

    DEFF Research Database (Denmark)

    Zhao, Luxi; Pop, Paul; Li, Qiao

    2017-01-01

    calculus (NC) to determine the worst-case end-to-end delays of RC traffic in TTEthernet. The main contribution of this paper is capturing the effects of all the integration policies on the latency bounds of RC traffic using NC, and the consideration of relative frame offsets of TT traffic to reduce...

  2. Predicting disease associations via biological network analysis.

    Science.gov (United States)

    Sun, Kai; Gonçalves, Joana P; Larminie, Chris; Przulj, Nataša

    2014-09-17

    Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment. We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships. We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

  3. Defending Tor from Network Adversaries: A Case Study of Network Path Prediction

    Science.gov (United States)

    2015-01-01

    routing policy (e.g., using OSPF [29], IS-IS [9], RIP [27], or iBGP [32]) that routes traffic to and from other networks. BGP is a path-vector routing...Applications, Tech- nologies, Architectures, and Protocols for Computer Commu- nications, 2003. [29] J. Moy. RFC 2328: OSPF Version 2, April 1998. http

  4. Modelling and Implementation of QoS in Wireless Sensor Networks: A Multiconstrained Traffic Engineering Model

    Directory of Open Access Journals (Sweden)

    Bagula AntoineB

    2010-01-01

    Full Text Available This paper revisits the problem of Quality of Service (QoS provisioning to assess the relevance of using multipath routing to improve the reliability and packet delivery in wireless sensor networks while maintaining lower power consumption levels. Building upon a previous benchmark, we propose a traffic engineering model that relies on delay, reliability, and energy-constrained paths to achieve faster, reliable, and energy-efficient transmission of the information routed by a wireless sensor network. As a step forward into the implementation of the proposed QoS model, we describe the initial steps of its packet forwarding protocol and highlight the tradeoff between the complexity of the model and the ease of implementation. Using simulation, we demonstrate the relative efficiency of our proposed model compared to single path routing, disjoint path routing, and the previously proposed benchmarks. The results reveal that by achieving a good tradeoff between delay minimization, reliability maximization, and path set selection, our model outperforms the other models in terms of energy consumption and quality of paths used to route the information.

  5. Traffic Adaptive Synchronized Cluster Based MAC Protocol for Cognitive Radio Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Sultana Sahelee

    2017-01-01

    Full Text Available In wireless communication, Cognitive Radio Network (CRN is the contemporary research area to improve efficiency and spectrum utilization. It is structured with both licensed users and unlicensed users. In CRN, unlicensed users also called Cognitive Radio (CR users are permitted to utilize the free/idle of licensed channels without harmful interference to licensed users. However, accessing idle channels is the big challenging issue due to licensed users’ activities. A large number of cluster based MAC protocol have been proposed to solve this issue. In this paper, we have come up with a Traffic Adaptive Synchronized Cluster Based MAC Protocol for Cognitive Radio Ad Hoc Network, with the target of creating cluster structure more vigorous to the licensed users’ channel re-occupancy actions, maximize throughput, and minimize switching delay, so that CR users be able to use the idle spectrum more efficiently. In our protocol, clusters are formed according to Cluster Identification Channel (CIC and inter-communication is completed without gateway nodes. Finally, we have analysed and implemented our protocol through simulation and it provides better performance in terms of different performance metrics.

  6. CROSS LAYERED HYBRID TRANSPORT LAYER PROTOCOL APPROACH TO ENHANCE NETWORK UTILISATION FOR VIDEO TRAFFIC

    Directory of Open Access Journals (Sweden)

    Matilda.S

    2010-03-01

    Full Text Available Video data transfer is the major traffic in today’s Internet. With the emerging need for anytime anywhere communication, applications transmitting video is gaining momentum. Real Time Protocol is the primary standard for transfer of video data, as; it requires timely delivery and can tolerate loss of packets. Streaming is the method used for delivering video content from the source server to the user. But this has many drawbacks: a It sends only the amount of data equivalent to the streaming encoded rate to the client irrespective of the available bandwidth in the path. Hence the links are underutilized; b It utilizes the link for the entire period of transfer and hence the link is not available to service other new clients. Thus as the number of clients increases, the network performance decreases. In this work, the advantages and disadvantages of the combination of different protocols in the application layer and transport layer are analyzed. The significant characteristics of each of these protocols are utilized and a combination of protocols for improving the network performance is arrived at, while retaining the QoS of video transmission.

  7. Selfish routing equilibrium in stochastic traffic network: A probability-dominant description.

    Science.gov (United States)

    Zhang, Wenyi; He, Zhengbing; Guan, Wei; Ma, Rui

    2017-01-01

    This paper suggests a probability-dominant user equilibrium (PdUE) model to describe the selfish routing equilibrium in a stochastic traffic network. At PdUE, travel demands are only assigned to the most dominant routes in the same origin-destination pair. A probability-dominant rerouting dynamic model is proposed to explain the behavioral mechanism of PdUE. To facilitate applications, the logit formula of PdUE is developed, of which a well-designed route set is not indispensable and the equivalent varitional inequality formation is simple. Two routing strategies, i.e., the probability-dominant strategy (PDS) and the dominant probability strategy (DPS), are discussed through a hypothetical experiment. It is found that, whether out of insurance or striving for perfection, PDS is a better choice than DPS. For more general cases, the conducted numerical tests lead to the same conclusion. These imply that PdUE (rather than the conventional stochastic user equilibrium) is a desirable selfish routing equilibrium for a stochastic network, given that the probability distributions of travel time are available to travelers.

  8. Selfish routing equilibrium in stochastic traffic network: A probability-dominant description

    Science.gov (United States)

    Zhang, Wenyi; Guan, Wei; Ma, Rui

    2017-01-01

    This paper suggests a probability-dominant user equilibrium (PdUE) model to describe the selfish routing equilibrium in a stochastic traffic network. At PdUE, travel demands are only assigned to the most dominant routes in the same origin-destination pair. A probability-dominant rerouting dynamic model is proposed to explain the behavioral mechanism of PdUE. To facilitate applications, the logit formula of PdUE is developed, of which a well-designed route set is not indispensable and the equivalent varitional inequality formation is simple. Two routing strategies, i.e., the probability-dominant strategy (PDS) and the dominant probability strategy (DPS), are discussed through a hypothetical experiment. It is found that, whether out of insurance or striving for perfection, PDS is a better choice than DPS. For more general cases, the conducted numerical tests lead to the same conclusion. These imply that PdUE (rather than the conventional stochastic user equilibrium) is a desirable selfish routing equilibrium for a stochastic network, given that the probability distributions of travel time are available to travelers. PMID:28829834

  9. Feasibility of a Networked Air Traffic Infrastructure Validation Environment for Advanced NextGen Concepts

    Science.gov (United States)

    McCormack, Michael J.; Gibson, Alec K.; Dennis, Noah E.; Underwood, Matthew C.; Miller,Lana B.; Ballin, Mark G.

    2013-01-01

    Abstract-Next Generation Air Transportation System (NextGen) applications reliant upon aircraft data links such as Automatic Dependent Surveillance-Broadcast (ADS-B) offer a sweeping modernization of the National Airspace System (NAS), but the aviation stakeholder community has not yet established a positive business case for equipage and message content standards remain in flux. It is necessary to transition promising Air Traffic Management (ATM) Concepts of Operations (ConOps) from simulation environments to full-scale flight tests in order to validate user benefits and solidify message standards. However, flight tests are prohibitively expensive and message standards for Commercial-off-the-Shelf (COTS) systems cannot support many advanced ConOps. It is therefore proposed to simulate future aircraft surveillance and communications equipage and employ an existing commercial data link to exchange data during dedicated flight tests. This capability, referred to as the Networked Air Traffic Infrastructure Validation Environment (NATIVE), would emulate aircraft data links such as ADS-B using in-flight Internet and easily-installed test equipment. By utilizing low-cost equipment that is easy to install and certify for testing, advanced ATM ConOps can be validated, message content standards can be solidified, and new standards can be established through full-scale flight trials without necessary or expensive equipage or extensive flight test preparation. This paper presents results of a feasibility study of the NATIVE concept. To determine requirements, six NATIVE design configurations were developed for two NASA ConOps that rely on ADS-B. The performance characteristics of three existing in-flight Internet services were investigated to determine whether performance is adequate to support the concept. Next, a study of requisite hardware and software was conducted to examine whether and how the NATIVE concept might be realized. Finally, to determine a business case

  10. Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences

    OpenAIRE

    Kastens, Terry L.; Featherstone, Allen M.

    1996-01-01

    An out-of-sample prediction of Kansas farmers' responses to five surveyed questions involving risk is used to compare ordered multinomial logistic regression models with feedforward backpropagation neural network models. Although the logistic models often predict more accurately than the neural network models in a mean-squared error sense, the neural network models are shown to be more accommodating of loss functions associated with a desire to predict certain combinations of categorical resp...

  11. Predicting long-term average concentrations of traffic-related air pollutants using GIS-based information

    Science.gov (United States)

    Hochadel, Matthias; Heinrich, Joachim; Gehring, Ulrike; Morgenstern, Verena; Kuhlbusch, Thomas; Link, Elke; Wichmann, H.-Erich; Krämer, Ursula

    Global regression models were developed to estimate individual levels of long-term exposure to traffic-related air pollutants. The models are based on data of a one-year measurement programme including geographic data on traffic and population densities. This investigation is part of a cohort study on the impact of traffic-related air pollution on respiratory health, conducted at the westerly end of the Ruhr-area in North-Rhine Westphalia, Germany. Concentrations of NO 2, fine particle mass (PM 2.5) and filter absorbance of PM 2.5 as a marker for soot were measured at 40 sites spread throughout the study region. Fourteen-day samples were taken between March 2002 and March 2003 for each season and site. Annual average concentrations for the sites were determined after adjustment for temporal variation. Information on traffic counts in major roads, building densities and community population figures were collected in a geographical information system (GIS). This information was used to calculate different potential traffic-based predictors: (a) daily traffic flow and maximum traffic intensity of buffers with radii from 50 to 10 000 m and (b) distances to main roads and highways. NO 2 concentration and PM 2.5 absorbance were strongly correlated with the traffic-based variables. Linear regression prediction models, which involved predictors with radii of 50 to 1000 m, were developed for the Wesel region where most of the cohort members lived. They reached a model fit ( R2) of 0.81 and 0.65 for NO 2 and PM 2.5 absorbance, respectively. Regression models for the whole area required larger spatial scales and reached R2=0.90 and 0.82. Comparison of predicted values with NO 2 measurements at independent public monitoring stations showed a satisfactory association ( r=0.66). PM 2.5 concentration, however, was only slightly correlated and thus poorly predictable by traffic-based variables ( rtraffic-related pollutants, and that GIS-based regression models offer a promising

  12. Predicting cryptic links in host-parasite networks.

    Directory of Open Access Journals (Sweden)

    Tad Dallas

    2017-05-01

    Full Text Available Networks are a way to represent interactions among one (e.g., social networks or more (e.g., plant-pollinator networks classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

  13. Predictive Control of Networked Multiagent Systems via Cloud Computing.

    Science.gov (United States)

    Liu, Guo-Ping

    2017-01-18

    This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

  14. Track wear-and-tear cost by traffic class: Functional form, zero output levels and marginal cost pricing recovery on the French rail network

    OpenAIRE

    Gaudry, Marc; Quinet, Emile

    2009-01-01

    We address the issue of the allocation of railway track maintenance (wear-and-tear) costs to traffic output classes and consider a very general function relating maintenance cost C to a set of technical production characteristics K used to produce traffic output vector T. We neglect other rail cost categories, such as traffic control and track renewal. The data base pertains to over 1500 sections of the French rail infrastructure in 1999, representing about 90% of the total network of 30000 k...

  15. A Mobility and Traffic Generation Framework for Modeling and Simulating Ad Hoc Communication Networks

    Directory of Open Access Journals (Sweden)

    Chris Barrett

    2004-01-01

    Full Text Available We present a generic mobility and traffic generation framework that can be incorporated into a tool for modeling and simulating large scale ad~hoc networks. Three components of this framework, namely a mobility data generator (MDG, a graph structure generator (GSG and an occlusion modification tool (OMT allow a variety of mobility models to be incorporated into the tool. The MDG module generates positions of transceivers at specified time instants. The GSG module constructs the graph corresponding to the ad hoc network from the mobility data provided by MDG. The OMT module modifies the connectivity of the graph produced by GSG to allow for occlusion effects. With two other modules, namely an activity data generator (ADG which generates packet transmission activities for transceivers and a packet activity simulator (PAS which simulates the movement and interaction of packets among the transceivers, the framework allows the modeling and simulation of ad hoc communication networks. The design of the framework allows a user to incorporate various realistic parameters crucial in the simulation. We illustrate the utility of our framework through a comparative study of three mobility models. Two of these are synthetic models (random waypoint and exponentially correlated mobility proposed in the literature. The third model is based on an urban population mobility modeling tool (TRANSIMS developed at the Los Alamos National Laboratory. This tool is capable of providing comprehensive information about the demographics, mobility and interactions of members of a large urban population. A comparison of these models is carried out by computing a variety of parameters associated with the graph structures generated by the models. There has recently been interest in the structural properties of graphs that arise in real world systems. We examine two aspects of this for the graphs created by the mobility models: change associated with power control (range of

  16. Prediction of Modal Shift Using Artificial Neural Networks

    OpenAIRE

    Kadir Akgöl; Metin Mutlu Aydin; Özcan Asilkan; Banihan Günay

    2014-01-01

    Various public transport concepts have been developed to provide solutions to the ever growing problem of traffic in modern times. For instance, intelligent subscription bus service is one of them. This concept aims to provide a means of transport at near private car comfort as well as at near public transport cost. By this means, a shift from other modes of transport, especially private car, to public transport is aimed to encourage use of public transport. An artificial neural network model...

  17. A study of modelling simplifications in ground vibration predictions for railway traffic at grade

    Science.gov (United States)

    Germonpré, M.; Degrande, G.; Lombaert, G.

    2017-10-01

    Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

  18. Social network models predict movement and connectivity in ecological landscapes

    Science.gov (United States)

    Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

  19. Predicting Expressive Dynamics in Piano Performances using Neural Networks

    NARCIS (Netherlands)

    van Herwaarden, Sam; Grachten, Maarten; de Haas, W. Bas

    2014-01-01

    This paper presents a model for predicting expressive accentuation in piano performances with neural networks. Using Restricted Boltzmann Machines (RBMs), features are learned from performance data, after which these features are used to predict performed loudness. During feature learning, data

  20. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

    Directory of Open Access Journals (Sweden)

    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  1. POLLING AND DUAL-LEVEL TRAFFIC ANALYSIS FOR IMPROVED DOS DETECTION IN IEEE 802.21 NETWORKS

    Directory of Open Access Journals (Sweden)

    Nygil Alex Vadakkan

    2014-06-01

    Full Text Available The IEEE 802.21 standard was developed for communication of devices in a heterogeneous environment which included greater support for handoffs. This paper focuses on the denial of service (DoS vulnerabilities faced by such Media Independent Handover (MIH networks & various effective countermeasures that can be deployed to prevent their impact on such heterogeneous networks. The use of polling mechanism coupled with real time as well as offline traffic analysis can keep a good number of attacks at bay. The use of offline traffic analysis is to use the model and compare it with a lighter model and see if any of the excluded features in the lighter model have had suspicious variations which could be a varied form of DoS attack or an attack that is completely new. The countermeasures that have been developed also allows for the increase in efficiency of data transfer as well as higher rates of success in handoffs.

  2. High-Speed Network Traffic Management Analysis and Optimization Models and Methods

    CERN Document Server

    Zaborovski, V; Podgurski, Y; Shemanin, Y

    1997-01-01

    The main steps of automatic control methodology include the hierarchical representation of management system and the formal definitions of input variables, object and goal of control of each management level. A Petri net model of individual traffic source is presented. It is noted that the current set of traffic parameters recommended by ATM-forum is not enough to synthesize optimal traffic control system. The feature of traffic self-similarity can be used to effectively solve optimal control task. An example of an optimal control scheme for cell discarding algorithm is presented.

  3. p53 as the main traffic controller of the cell signaling network.

    Science.gov (United States)

    Sebastian, Sinto; Azzariti, Amalia; Silvestris, Nicola; Porcelli, Letizia; Russo, Antonio; Paradiso, Angelo

    2010-06-01

    Among different pathological conditions that affect human beings, cancer has received a great deal of attention primarily because it leads to significant morbidity and mortality. This is essentially due to increasing world-wide incidence of this disease and the inability to discover the cause and molecular mechanisms by which normal human cells acquire the characteristics that define cancer cells. Since the discovery of p53 over a quarter of a century ago, it is now recognized that virtually all cell fate pathways of live cells and the decision to die are under the control of p53. Such extensive involvement indicates that p53 protein is acting as a major traffic controller in the cell signaling network. In cancer cells, many cell signaling pathways of normal human cells are rerouted towards immortalization and this is accomplished by the corruption of the main controllers of cell signaling pathways such as p53. This review highlights how p53 signaling activity is altered in cancer cells so that cells acquire the hallmarks of cancer including deregulated infinite self replicative potential.

  4. Time series prediction with simple recurrent neural networks ...

    African Journals Online (AJOL)

    Simple recurrent neural networks are widely used in time series prediction. Most researchers and application developers often choose arbitrarily between Elman or Jordan simple recurrent neural networks for their applications. A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used.

  5. Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

    DEFF Research Database (Denmark)

    Míguez González, M; López Peña, F.; Díaz Casás, V.

    2011-01-01

    acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network...

  6. Using Neural Networks to Predict MBA Student Success

    Science.gov (United States)

    Naik, Bijayananda; Ragothaman, Srinivasan

    2004-01-01

    Predicting MBA student performance for admission decisions is crucial for educational institutions. This paper evaluates the ability of three different models--neural networks, logit, and probit to predict MBA student performance in graduate programs. The neural network technique was used to classify applicants into successful and marginal student…

  7. Artificial neural networks for prediction of percentage of water ...

    Indian Academy of Sciences (India)

    According to these input parameters, in the neural networks model, the percentage of water absorption of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the percentage of water absorption of the geopolymer specimens.

  8. Centrality Robustness and Link Prediction in Complex Social Networks

    DEFF Research Database (Denmark)

    Davidsen, Søren Atmakuri; Ortiz-Arroyo, Daniel

    2012-01-01

    This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present am analysis on the robustness of centrality measures that extend the work presented in Borgati et al. using three types of complex network structures and one real social network....... Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved...

  9. Characterizing and Predicting the Robustness of Power-law Networks

    CERN Document Server

    LaRocca, Sarah

    2013-01-01

    Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2,000 power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing...

  10. An interaction switch predicts the nested architecture of mutualistic networks.

    Science.gov (United States)

    Zhang, Feng; Hui, Cang; Terblanche, John S

    2011-08-01

    Nested architecture is distinctive in plant-animal mutualistic networks. However, to date an integrative and quantitative explanation has been lacking. It is evident that species often switch their interactive partners in real-world mutualistic networks such as pollination and seed-dispersal networks. By incorporating an interaction switch into a novel multi-population model, we show that the nested architecture rapidly emerges from an initially random network. The model allowing interaction switches between partner species produced predictions which fit remarkably well with observations from 81 empirical networks. Thus, the nested architecture in mutualistic networks could be an intrinsic physical structure of dynamic networks and the interaction switch is likely a key ecological process that results in nestedness of real-world networks. Identifying the biological processes responsible for network structures is thus crucial for understanding the architecture of ecological networks. © 2011 Blackwell Publishing Ltd/CNRS.

  11. How social network heterogeneity facilitates lexical access and lexical prediction.

    Science.gov (United States)

    Lev-Ari, Shiri; Shao, Zeshu

    2017-04-01

    People learn language from their social environment. As individuals differ in their social networks, they might be exposed to input with different lexical distributions, and these might influence their linguistic representations and lexical choices. In this article we test the relation between linguistic performance and 3 social network properties that should influence input variability, namely, network size, network heterogeneity, and network density. In particular, we examine how these social network properties influence lexical prediction, lexical access, and lexical use. To do so, in Study 1, participants predicted how people of different ages would name pictures, and in Study 2 participants named the pictures themselves. In both studies, we examined how participants' social network properties related to their performance. In Study 3, we ran simulations on norms we collected to see how age variability in one's network influences the distribution of different names in the input. In all studies, network age heterogeneity influenced performance leading to better prediction, faster response times for difficult-to-name items, and less entropy in input distribution. These results suggest that individual differences in social network properties can influence linguistic behavior. Specifically, they show that having a more heterogeneous network is associated with better performance. These results also show that the same factors influence lexical prediction and lexical production, suggesting the two might be related.

  12. Stock Price Prediction Based on Procedural Neural Networks

    OpenAIRE

    Jiuzhen Liang; Wei Song; Mei Wang

    2011-01-01

    We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two differen...

  13. Congestion prediction modeling for quality of service improvement in wireless sensor networks.

    Science.gov (United States)

    Lee, Ga-Won; Lee, Sung-Young; Huh, Eui-Nam

    2014-04-30

    Information technology (IT) is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute "Information Networks" through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs). Information observed from sensor networks in real-time is used to increase quality of life (QoL) in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS) requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.

  14. Permeability prediction in shale gas reservoirs using Neural Network

    Science.gov (United States)

    Aliouane, Leila; Ouadfeul, Sid-Ali

    2017-04-01

    Here, we suggest the use of the artificial neural network for permeability prediction in shale gas reservoirs using artificial neural network. Prediction of Permeability in shale gas reservoirs is a complicated task that requires new models where Darcy's fluid flow model is not suitable. Proposed idea is based on the training of neural network machine using the set of well-logs data as an input and the measured permeability as an output. In this case the Multilayer Perceptron neural network machines is used with Levenberg Marquardt algorithm. Application to two horizontal wells drilled in the Barnett shale formation exhibit the power of neural network model to resolve such as problem. Keywords: Artificial neural network, permeability, prediction , shale gas.

  15. Cold-start link prediction in multi-relational networks

    Science.gov (United States)

    Wu, Shun-yao; Zhang, Qi; Wu, Mei

    2017-10-01

    During the last decade, interaction data have accumulated exponentially in many fields and provide a new opportunity for cold start link prediction. It seems necessarily to take full advantages of diversified information. However, correlation between different interactions has to be pre-tested. Therefore, this paper abstracts complex systems as multi-relational networks, and employs latent space network model to extract low-dimensional factors of sub-networks and adopts likelihood ratio test to examine correlation between factors. Then, regression between target sub-networks and correlated auxiliary sub-networks could be established for cold start link prediction. Experiments on 8 bioinformatic data sets validate the effectiveness and potential of our strategy for network correlation analysis and cold-start link prediction.

  16. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    Science.gov (United States)

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

  17. Financial time series prediction using spiking neural networks.

    Directory of Open Access Journals (Sweden)

    David Reid

    Full Text Available In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  18. Chronic Traffic-Related Air Pollution and Stress Interact to Predict Biologic and Clinical Outcomes in Asthma

    OpenAIRE

    Chen, Edith; Schreier, Hannah M. C.; Strunk, Robert C.; Brauer, Michael

    2008-01-01

    Background Previous research has documented effects of both physical and social environmental exposures on childhood asthma. However, few studies have considered how these two environments might interact to affect asthma. Objective This study aimed to test interactions between chronic exposure to traffic-related air pollution and chronic family stress in predicting biologic and clinical outcomes in children with asthma. Method Children with asthma (n = 73, 9–18 years of age) were interviewed ...

  19. Prediction of secondary crash frequency on highway networks.

    Science.gov (United States)

    Sarker, Afrid A; Paleti, Rajesh; Mishra, Sabyasachee; Golias, Mihalis M; Freeze, Philip B

    2017-01-01

    Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Online traffic grooming using timing information in WDM–TDM networks

    Directory of Open Access Journals (Sweden)

    Tabarak allah Ali Mohamed

    2013-03-01

    Full Text Available In this paper, the effect of holding time awareness on the process of time slot assignment in WDM–TDM is considered. Use has been made of Markov model in order to predict the wavelength congestion. A routing algorithm is developed based on the Markov modeling. The results are compared with existing algorithms—ASP, WSP and OTGA. Validation results have shown that the performance of the system is significantly improved in terms of bandwidth blocking ratio, network utilization and fairness.

  1. The predictive value of the NICE "red traffic lights" in acutely ill children

    National Research Council Canada - National Science Library

    Kerkhof, Evelien; Lakhanpaul, Monica; Ray, Samiran; Verbakel, Jan; Bruel, Ann; Thompson, Matthew; Berger, Marjolein; Moll, Henriëtte; Oostenbrink, Rianne

    2014-01-01

    ...: The 16 most severe ("red") features of the NICE traffic light system were validated in seven different primary care and emergency department settings, including 6,260 children presenting with acute illness...

  2. Assembly and offset assignment scheme for self-similar traffic in optical burst switched networks

    CSIR Research Space (South Africa)

    Muwonge, KB

    2007-10-01

    Full Text Available are modelled as the burst duration, while the OFF periods are modelled as the offset times. Modelling OBS core traffic in this manner allows for a per channel flow modelling in DWDM wavelengths. Figure 1 shows the overall proposed LER traffic set...-up. Modelling OBS traffic as ON/OFF periods in DWDM conforms to the same distribution theory in Jackson queues. C. The burst queue In this section we develop several rules for the burst queue. Rule 1: After a BHP corresponding to a lower priority burst...

  3. Design and implementation of priority and time-window based traffic scheduling and routing-spectrum allocation mechanism in elastic optical networks

    Science.gov (United States)

    Wang, Honghuan; Xing, Fangyuan; Yin, Hongxi; Zhao, Nan; Lian, Bizhan

    2016-02-01

    With the explosive growth of network services, the reasonable traffic scheduling and efficient configuration of network resources have an important significance to increase the efficiency of the network. In this paper, an adaptive traffic scheduling policy based on the priority and time window is proposed and the performance of this algorithm is evaluated in terms of scheduling ratio. The routing and spectrum allocation are achieved by using the Floyd shortest path algorithm and establishing a node spectrum resource allocation model based on greedy algorithm, which is proposed by us. The fairness index is introduced to improve the capability of spectrum configuration. The results show that the designed traffic scheduling strategy can be applied to networks with multicast and broadcast functionalities, and makes them get real-time and efficient response. The scheme of node spectrum configuration improves the frequency resource utilization and gives play to the efficiency of the network.

  4. A Best Effort Traffic Management Solution for Server and Agent-Based Active Network Management (SAAM)

    Science.gov (United States)

    2002-03-01

    14 c. Link State Protocols ............................................................... 14 2. Interdomain Routing...Shortest Widest Least Congested Path (SWLCP) ................. 38 4. Traffic Splitting...c. Reversion Interval .................................................................. 43 d. Congestion Bypass Time

  5. Analysis of traffic state variation patterns for urban road network based on spectral clustering

    National Research Council Canada - National Science Library

    Yang, Senyan; Wu, Jianping; Qi, Geqi; Tian, Kun

    2017-01-01

    ... on section-based traffic speed dataset. The proposed method transforms traditional clustering problems into graph partition problems, which is suitable for the clustering problems with multiple attributes by dimension reduction...

  6. Agent-based traffic management and reinforcement learning in congested intersection network.

    Science.gov (United States)

    2012-08-01

    This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

  7. Development of Network Synchronization Predicts Language Abilities.

    Science.gov (United States)

    Doesburg, Sam M; Tingling, Keriann; MacDonald, Matt J; Pang, Elizabeth W

    2016-01-01

    Synchronization of oscillations among brain areas is understood to mediate network communication supporting cognition, perception, and language. How task-dependent synchronization during word production develops throughout childhood and adolescence, as well as how such network coherence is related to the development of language abilities, remains poorly understood. To address this, we recorded magnetoencephalography while 73 participants aged 4-18 years performed a verb generation task. Atlas-guided source reconstruction was performed, and phase synchronization among regions was calculated. Task-dependent increases in synchronization were observed in the theta, alpha, and beta frequency ranges, and network synchronization differences were observed between age groups. Task-dependent synchronization was strongest in the theta band, as were differences between age groups. Network topologies were calculated for brain regions associated with verb generation and were significantly associated with both age and language abilities. These findings establish the maturational trajectory of network synchronization underlying expressive language abilities throughout childhood and adolescence and provide the first evidence for an association between large-scale neurophysiological network synchronization and individual differences in the development of language abilities.

  8. Using neural networks to predict the functionality of reconfigurable nano-material networks

    NARCIS (Netherlands)

    Greff, Klaus; van Damme, Rudolf M.J.; Koutnik, Jan; Broersma, Haitze J.; Mikhal, Julia Olegivna; Lawrence, Celestine Preetham; van der Wiel, Wilfred Gerard; Schmidhuber, Jürgen

    2017-01-01

    This paper demonstrates how neural networks can be applied to model and predict the functional behaviour of disordered nano-particle and nano-tube networks. In recently published experimental work, nano-particle and nano-tube networks show promising functionality for future reconfigurable devices,

  9. Context-sensitive data integration and prediction of biological networks

    National Research Council Canada - National Science Library

    Myers, Chad L; Troyanskaya, Olga G

    2007-01-01

    Motivation: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically...

  10. The predictive power of local properties of financial networks

    Science.gov (United States)

    Caraiani, Petre

    2017-01-01

    The literature on analyzing the dynamics of financial networks has focused so far on the predictive power of global measures of networks like entropy or index cohesive force. In this paper, I show that the local network properties have similar predictive power. I focus on key network measures like average path length, average degree or cluster coefficient, and also consider the diameter and the s-metric. Using Granger causality tests, I show that some of these measures have statistically significant prediction power with respect to the dynamics of aggregate stock market. Average path length is most robust relative to the frequency of data used or specification (index or growth rate). Most measures are found to have predictive power only for monthly frequency. Further evidences that support this view are provided through a simple regression model.

  11. Using machine learning, neural networks and statistics to predict bankruptcy

    NARCIS (Netherlands)

    Pompe, P.P.M.; Feelders, A.J.; Feelders, A.J.

    1997-01-01

    Recent literature strongly suggests that machine learning approaches to classification outperform "classical" statistical methods. We make a comparison between the performance of linear discriminant analysis, classification trees, and neural networks in predicting corporate bankruptcy. Linear

  12. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  13. Neural Network Algorithm for Prediction of Secondary Protein Structure

    National Research Council Canada - National Science Library

    Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic

    2009-01-01

    .... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...

  14. Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

    Directory of Open Access Journals (Sweden)

    Lun Zhang

    2015-01-01

    Full Text Available This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN. Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

  15. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    Directory of Open Access Journals (Sweden)

    Jie Wang

    2016-01-01

    (ERNN, the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

  16. Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks

    NARCIS (Netherlands)

    Zhao, Zongliang; Karimzadeh Motallebi Azar, Morteza; Braun, Torsten; Pras, Aiko; van den Berg, Hans Leo

    2017-01-01

    Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to

  17. Artificial neural networks in predicting current in electric arc furnaces

    Science.gov (United States)

    Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

    2014-03-01

    The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

  18. Congestion Prediction Modeling for Quality of Service Improvement in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ga-Won Lee

    2014-04-01

    Full Text Available Information technology (IT is pushing ahead with drastic reforms of modern life for improvement of human welfare. Objects constitute “Information Networks” through smart, self-regulated information gathering that also recognizes and controls current information states in Wireless Sensor Networks (WSNs. Information observed from sensor networks in real-time is used to increase quality of life (QoL in various industries and daily life. One of the key challenges of the WSNs is how to achieve lossless data transmission. Although nowadays sensor nodes have enhanced capacities, it is hard to assure lossless and reliable end-to-end data transmission in WSNs due to the unstable wireless links and low hard ware resources to satisfy high quality of service (QoS requirements. We propose a node and path traffic prediction model to predict and minimize the congestion. This solution includes prediction of packet generation due to network congestion from both periodic and event data generation. Simulation using NS-2 and Matlab is used to demonstrate the effectiveness of the proposed solution.

  19. Predicting the survival of diabetes using neural network

    Science.gov (United States)

    Mamuda, Mamman; Sathasivam, Saratha

    2017-08-01

    Data mining techniques at the present time are used in predicting diseases of health care industries. Neural Network is one among the prevailing method in data mining techniques of an intelligent field for predicting diseases in health care industries. This paper presents a study on the prediction of the survival of diabetes diseases using different learning algorithms from the supervised learning algorithms of neural network. Three learning algorithms are considered in this study: (i) The levenberg-marquardt learning algorithm (ii) The Bayesian regulation learning algorithm and (iii) The scaled conjugate gradient learning algorithm. The network is trained using the Pima Indian Diabetes Dataset with the help of MATLAB R2014(a) software. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is further computed to validate the accurate prediction

  20. Limits of Friendship Networks in Predicting Epidemic Risk

    CERN Document Server

    Coviello, Lorenzo; Rahwan, Iyad

    2015-01-01

    The spread of an infection on a real-world social network is determined by the interplay of two processes - the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about said encounters is often unavailable because expensive, unpractical to collect or privacy sensitive. The present work asks whether the friendship ties between the individuals in a social network successfully predict who is at risk. Using a dataset from a popular online review service, we build a time-varying network that is a proxy of physical encounter between users and a static network based on their reported friendship. Through computer simulation, we compare infection processes on the resulting networks and show that friendship provides a poor identification of the individuals at risk if th...

  1. water demand prediction using artificial neural network

    African Journals Online (AJOL)

    user

    2017-01-01

    Jan 1, 2017 ... estimate water quantity and to make decisions that can prevent water scarcity. Timely implementation of such decisions lead to the improvement of network reliability and to the reduced occurrence of pipe burst and plant breakdown. On the other hand long- term forecasting helps to know the water demand ...

  2. OCP: Opportunistic Carrier Prediction for Wireless Networks

    Science.gov (United States)

    2008-08-01

    Many protocols have been proposed for medium access control in wireless networks. MACA [13], MACAW [3], and FAMA [8] are the earlier proposals for...world performance of carrier sense. In Proceedings of ACM SIGCOMM E-WIND Workshop, 2005. [13] P. Karn. MACA : A new channel access method for packet radio

  3. Energy-efficient multicast traffic grooming strategy based on light-tree splitting for elastic optical networks

    Science.gov (United States)

    Liu, Huanlin; Yin, Yarui; Chen, Yong

    2017-07-01

    In order to address the problem of optimizing the spectrum resources and power consumption in elastic optical networks (EONs), we investigate the potential gains by jointly employing the light-tree splitting and traffic grooming for multicast requests. An energy-efficient multicast traffic grooming strategy based on light-tree splitting (EED-MTGS-LS) is proposed in this paper. Firstly, we design a traffic pre-processing mechanism to decide the multicast requests' routing order, which considers the request's bandwidth requirement and physical hops synthetically. Then, by dividing a light-tree to some sub-light-trees and grooming the request to these sub-light-trees, the light-tree sharing ratios of multicast requests can be improved. What's more, a priority scheduling vector is constructed, which aims to improve the success rate of spectrum assignment for grooming requests. Finally, a grooming strategy is designed to optimize the total power consumption by reducing the use of transponders and IP routers during routing. Simulation results show that the proposed strategy can significantly improve the spectrum utilization and save the power consumption.

  4. Predicting drunk driving: contribution of alcohol use and related problems, traffic behaviour, personality and platelet monoamine oxidase (MAO) activity.

    Science.gov (United States)

    Eensoo, Diva; Paaver, Marika; Harro, Maarike; Harro, Jaanus

    2005-01-01

    The aim of the study was to characterize the predictive value of socio-economic data, alcohol consumption measures, smoking, platelet monoamine oxidase (MAO) activity, traffic behaviour habits and impulsivity measures for actual drunk driving. Data were collected from 203 male drunk driving offenders and 211 control subjects using self-reported questionnaires, and blood samples were obtained from the two groups. We identified the combination of variables, which predicted correctly, approximately 80% of the subjects' belonging to the drunk driving and control groups. Significant independent discriminators in the final model were, among the health-behaviour measures, alcohol-related problems, frequency of using alcohol, the amount of alcohol consumed and smoking. Predictive traffic behaviour measures were seat belt use and paying for parking. Among the impulsivity measures, dysfunctional impulsivity was the best predictor; platelet MAO activity and age also had an independent predictive value. Our results support the notion that drunk driving is the result of a combination of various behavioural, biological and personality-related risk factors.

  5. Exploring function prediction in protein interaction networks via clustering methods.

    Science.gov (United States)

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.

  6. Exploring function prediction in protein interaction networks via clustering methods.

    Directory of Open Access Journals (Sweden)

    Kire Trivodaliev

    Full Text Available Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.

  7. Echo state network prediction method and its application in flue gas turbine condition prediction

    Science.gov (United States)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  8. Stock market price prediction using artificial neural network: an ...

    African Journals Online (AJOL)

    This paper looks at the application of the artificial neural networks (ANN) in predicting stock market prices in Kenya. In particular the paper looks at the application of ANN in predicting future Equity Bank share prices using historical data. We have assumed that only previous prices affect future prices, then fitted ARIMA ...

  9. Predicting Water Levels at Kainji Dam Using Artificial Neural Networks

    African Journals Online (AJOL)

    Poor electricity generation in Nigeria is a very serious problem. Accurate prediction of water levels in dams is very important in power planning. Effective power planning helps in ensuring steady supply of electric power to consumers. The aim of this study is to develop artificial neural network models for predicting water ...

  10. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  11. Meta-path based heterogeneous combat network link prediction

    Science.gov (United States)

    Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

    2017-09-01

    The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

  12. A novel function prediction approach using protein overlap networks.

    Science.gov (United States)

    Liang, Shide; Zheng, Dandan; Standley, Daron M; Guo, Huarong; Zhang, Chi

    2013-07-17

    Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database. The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein. The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.

  13. Quantitative Method for Network Security Situation Based on Attack Prediction

    Directory of Open Access Journals (Sweden)

    Hao Hu

    2017-01-01

    Full Text Available Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication.

  14. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  15. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  16. A Neural Network Model for Prediction of Sound Quality

    DEFF Research Database (Denmark)

    Nielsen,, Lars Bramsløw

    An artificial neural network structure has been specified, implemented and optimized for the purpose of predicting the perceived sound quality for normal-hearing and hearing-impaired subjects. The network was implemented by means of commercially available software and optimized to predict results...... error on the test set. The overall concept proved functional, but further testing with data obtained from a new rating experiment is necessary to better assess the utility of this measure. The weights in the trained neural networks were analyzed to qualitatively interpret the relation between...... obtained in subjective sound quality rating experiments based on input data from an auditory model. Various types of input data and data representations from the auditory model were used as input data for the chosen network structure, which was a three-layer perceptron. This network was trained by means...

  17. The predictive validity of personality tests in air traffic controller selection

    NARCIS (Netherlands)

    Roe, R.A.; Oprins, E.A.P.B.; Geven, E.

    2012-01-01

    A brief historical review of test methods used for selecting air traffic controllers (ATCOs) shows that in contrast to e.g. ability tests and job samples, personality tests have been used rather infrequently. The lesser popularity of personality tests may be explained from the belief that

  18. An optimal general type-2 fuzzy controller for Urban Traffic Network

    DEFF Research Database (Denmark)

    Khooban, Mohammad Hassan; Vafamand, Navid; Liaghat, Alireza

    2017-01-01

    , a combination of the general type-2 fuzzy logic sets and the Modified Backtracking Search Algorithm (MBSA) techniques are used in order to control the traffic signal scheduling and phase succession so as to guarantee a smooth flow of traffic with the least wait times and average queue length. The parameters...... of input and output membership functions are optimized simultaneously by the novel heuristic algorithm MBSA. A comparison is made between the achieved results with those of optimal and conventional type-1 fuzzy logic controllers....

  19. Prediction of macroscopic properties of elastomeric networks

    Energy Technology Data Exchange (ETDEWEB)

    Al-ghamdi, A.M.S.; Rayes, T.B.; Galiatsatos, V. [Univ. of Akron, OH (United States)

    1993-12-31

    Monte Carlo simulations of amorphous elastomeric networks of polyisoprene and polybutadiene cured with sulfur have been prepared. The effect of molecular weight of the prepolymer, and the concentration and type of cross-links is studied. The affine modulus as a function of the extent of reaction is reported. Comparisons between the two polymers and reasons for their differing behavior are being attributed to their molecular characteristics.

  20. Predicting network structure using unlabeled interaction information

    OpenAIRE

    Nasim, Mehwish; Brandes, Ulrik

    2014-01-01

    We are interested in the question whether interactions in online social networks (OSNs) can serve as a proxy for more persistent social relation. With Facebook as the context of our analysis, we look at commenting on wall posts as a form of interaction, and friendship ties as social relations. Findings from a pretest suggest that others’ joint commenting patterns on someone’s status posts are indeed indicative of friendship ties between them, independent of the contents. This would have impli...

  1. Neural network for prediction of superheater fireside corrosion

    Energy Technology Data Exchange (ETDEWEB)

    Makkonen, P. [Foster Wheeler Energia Oy, Karhula R and D Center, Karhula (Finland)

    1998-12-31

    Superheater corrosion causes vast annual losses to the power companies. If the corrosion could be reliably predicted, new power plants could be designed accordingly, and knowledge of fuel selection and determination of process conditions could be utilized to minimize superheater corrosion. If relations between inputs and the output are poorly known, conventional models depending on corrosion theories will fail. A prediction model based on a neural network is capable of learning from errors and improving its performance as the amount of data increases. The neural network developed during this study predicts superheater corrosion with 80 % accuracy at early stage of the project. (orig.) 10 refs.

  2. Neural network definitions of highly predictable protein secondary structure classes

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  3. Compressed sensing based missing nodes prediction in temporal communication network

    Science.gov (United States)

    Cheng, Guangquan; Ma, Yang; Liu, Zhong; Xie, Fuli

    2018-02-01

    The reconstruction of complex network topology is of great theoretical and practical significance. Most research so far focuses on the prediction of missing links. There are many mature algorithms for link prediction which have achieved good results, but research on the prediction of missing nodes has just begun. In this paper, we propose an algorithm for missing node prediction in complex networks. We detect the position of missing nodes based on their neighbor nodes under the theory of compressed sensing, and extend the algorithm to the case of multiple missing nodes using spectral clustering. Experiments on real public network datasets and simulated datasets show that our algorithm can detect the locations of hidden nodes effectively with high precision.

  4. Predicting Successful Memes using Network and Community Structure

    OpenAIRE

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2014-01-01

    We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of...

  5. A Piecewise Deterministic Markov Toy Model for Traffic/Maintenance and Associated Hamilton–Jacobi Integrodifferential Systems on Networks

    Energy Technology Data Exchange (ETDEWEB)

    Goreac, Dan, E-mail: Dan.Goreac@u-pem.fr; Kobylanski, Magdalena, E-mail: Magdalena.Kobylanski@u-pem.fr; Martinez, Miguel, E-mail: Miguel.Martinez@u-pem.fr [Université Paris-Est, LAMA (UMR 8050), UPEMLV, UPEC, CNRS (France)

    2016-10-15

    We study optimal control problems in infinite horizon whxen the dynamics belong to a specific class of piecewise deterministic Markov processes constrained to star-shaped networks (corresponding to a toy traffic model). We adapt the results in Soner (SIAM J Control Optim 24(6):1110–1122, 1986) to prove the regularity of the value function and the dynamic programming principle. Extending the networks and Krylov’s “shaking the coefficients” method, we prove that the value function can be seen as the solution to a linearized optimization problem set on a convenient set of probability measures. The approach relies entirely on viscosity arguments. As a by-product, the dual formulation guarantees that the value function is the pointwise supremum over regular subsolutions of the associated Hamilton–Jacobi integrodifferential system. This ensures that the value function satisfies Perron’s preconization for the (unique) candidate to viscosity solution.

  6. System and Method for Network Bandwidth, Buffers and Timing Management Using Hybrid Scheduling of Traffic with Different Priorities and Guarantees

    Science.gov (United States)

    Varadarajan, Srivatsan (Inventor); Hall, Brendan (Inventor); Smithgall, William Todd (Inventor); Bonk, Ted (Inventor); DeLay, Benjamin F. (Inventor)

    2017-01-01

    Systems and methods for network bandwidth, buffers and timing management using hybrid scheduling of traffic with different priorities and guarantees are provided. In certain embodiments, a method of managing network scheduling and configuration comprises, for each transmitting end station, reserving one exclusive buffer for each virtual link to be transmitted from the transmitting end station; for each receiving end station, reserving exclusive buffers for each virtual link to be received at the receiving end station; and for each switch, reserving a exclusive buffer for each virtual link to be received at an input port of the switch. The method further comprises determining if each respective transmitting end station, receiving end station, and switch has sufficient capability to support the reserved buffers; and reporting buffer infeasibility if each respective transmitting end station, receiving end station, and switch does not have sufficient capability to support the reserved buffers.

  7. A scalable acoustic sensor network for model based monitoring of urban traffic noise

    NARCIS (Netherlands)

    Basten, T.G.H.; Wessels, P.W.; Eerden, F.J.M. van der

    2012-01-01

    A good understanding of the acoustic environment due to traffic in urban areas is very important. Long term monitoring within large areas provides a clear insight in the actual noise situation. This is needed to take appropriate and cost efficient measures; to asses the effect of measures by

  8. A real-time traffic scheduling algorithm in CDMA packet networks

    NARCIS (Netherlands)

    Zan, Lei; Heijenk, Geert; El Zarki, Magda; Gong, K.; Niu, Z.

    2003-01-01

    The demands for multimedia and packet data services over wireless devices have increased over the past few years. The direct impact on performance makes scheduling for real-time traffic important. This paper presents a novel scheduling algorithm called fair channel-dependent scheduling which

  9. Efficient IP Traffic over Optical Network Based on Wavelength Translation Switching

    DEFF Research Database (Denmark)

    Jha, Vikas; Kalia, Kartik; Chowdhary, Bhawani Shankar

    2016-01-01

    With the advent of TCP/IP protocol suite the overall era of communication technologies had been redefined. Now, we can’t ignore the presence of huge amount of IP traffic; data, voice or video increasing day by day creating more pressure on existing communicating media and supporting back bone...

  10. A method for classification of network traffic based on C5.0 Machine Learning Algorithm

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup

    2012-01-01

    and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options...

  11. Domestic Heat Demand Prediction using Neural Networks

    NARCIS (Netherlands)

    Bakker, Vincent; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria

    2008-01-01

    By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural

  12. Social networks predict selective observation and information spread in ravens

    Science.gov (United States)

    Rubenstein, Daniel I.; Bugnyar, Thomas; Hoppitt, William; Mikus, Nace; Schwab, Christine

    2016-01-01

    Animals are predicted to selectively observe and learn from the conspecifics with whom they share social connections. Yet, hardly anything is known about the role of different connections in observation and learning. To address the relationships between social connections, observation and learning, we investigated transmission of information in two raven (Corvus corax) groups. First, we quantified social connections in each group by constructing networks on affiliative interactions, aggressive interactions and proximity. We then seeded novel information by training one group member on a novel task and allowing others to observe. In each group, an observation network based on who observed whose task-solving behaviour was strongly correlated with networks based on affiliative interactions and proximity. Ravens with high social centrality (strength, eigenvector, information centrality) in the affiliative interaction network were also central in the observation network, possibly as a result of solving the task sooner. Network-based diffusion analysis revealed that the order that ravens first solved the task was best predicted by connections in the affiliative interaction network in a group of subadult ravens, and by social rank and kinship (which influenced affiliative interactions) in a group of juvenile ravens. Our results demonstrate that not all social connections are equally effective at predicting the patterns of selective observation and information transmission. PMID:27493780

  13. Pain tolerance predicts human social network size.

    Science.gov (United States)

    Johnson, Katerina V-A; Dunbar, Robin I M

    2016-04-28

    Personal social network size exhibits considerable variation in the human population and is associated with both physical and mental health status. Much of this inter-individual variation in human sociality remains unexplained from a biological perspective. According to the brain opioid theory of social attachment, binding of the neuropeptide β-endorphin to μ-opioid receptors in the central nervous system (CNS) is a key neurochemical mechanism involved in social bonding, particularly amongst primates. We hypothesise that a positive association exists between activity of the μ-opioid system and the number of social relationships that an individual maintains. Given the powerful analgesic properties of β-endorphin, we tested this hypothesis using pain tolerance as an assay for activation of the endogenous μ-opioid system. We show that a simple measure of pain tolerance correlates with social network size in humans. Our results are in line with previous studies suggesting that μ-opioid receptor signalling has been elaborated beyond its basic function of pain modulation to play an important role in managing our social encounters. The neuroplasticity of the μ-opioid system is of future research interest, especially with respect to psychiatric disorders associated with symptoms of social withdrawal and anhedonia, both of which are strongly modulated by endogenous opioids.

  14. Steady traffic scheduler for internet video traffic across large delay ...

    African Journals Online (AJOL)

    Steady traffic scheduler for internet video traffic across large delay networks. O.E. Ojo, A.O. Oluwatope, I.T. Arowobusoye. Abstract. No Abstract. Keywords: Computer Networks, Multimedia Networking, TCP and Large-Delay Networks. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD ...

  15. Particle filter-based real-time estimation and prediction of traffic conditions. In: Christos H. Skiadas (Ed.), Recent Advances in Stochastic Modelling and Data Analysis

    OpenAIRE

    Sau, J.; EL-FAOUZI, NE; BEN-AISSA, A; DE MOUZON, O

    2007-01-01

    Real-time estimation and short-term prediction of traffic conditions is one of major concern of traffic managers and ITS-oriented systems. Model-based methods appear now as very promising ways in order to reach this purpose. Such methods are already used in process control (Kalman filtering, Luenberger observers). In the application presented in this paper, due to the high non linearity of the traffic models, particle filter (PF) approach is applied in combination with the well-known first or...

  16. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  17. ACCIDENT PREDICTION METHODOLOGY USING CONFLICT ZONE METHOD FOR “TRANSIT TRANSPORT-PEDESTRIAN” CONFLICT SITUATION AND MODELS OF TRAFFIC FLOWS AT CONTROLLED INTERSECTION

    Directory of Open Access Journals (Sweden)

    D. V. Kapsky

    2015-01-01

    Full Text Available Accidents are considered as the most significant cost of road traffic. Therefore any measures for road traffic management should be evaluated according to a minimization  criterion of accident losses. In order to develop a method for evaluation of the accident losses it is necessary to prepare a methodology for cost estimate of road accidents of various severity with due account of their consequences and prediction (economic assessment and severity level of their consequences (quantitative risk assessment. The research has been carried with the purpose to devise appropriate models for accident prediction at a decision-making stage while organizing road traffic in respect of  the “transport-pedestrian” conflict. An interaction of pedestrian and transit road traffic flows  is characterized by rather high risk level. In order to reduce number of road accidents  and  severity of their consequences in the observed conflict, it is necessary to evaluate  proposed solutions, in other words to predict accidents at the stage of object designing and  development of measures.The paper presents its observations on specificity of road traffic and pedestrian flow interactions and analysis of spatial conflict point formation and conflict zone creation in the studied conflict between transport facilities and pedestrians at controlled pedestrian crossings which are located in the area of intersections. Methodology has been developed for accident prediction in accordance with the conflict zone method for various traffic modes at intersections. Dependences of the represented road traffic accidents (according to consequence severity on potential danger of conflicts have been determined for various traffic modes and various conditions of conflict interaction.

  18. Framework for multi-resolution analyses of advanced traffic management strategies [summary].

    Science.gov (United States)

    2017-01-01

    Transportation planning relies extensively on software that can simulate and predict travel behavior in response to alternative transportation networks. However, different software packages view traffic at different scales. Some programs are based on...

  19. In silico network topology-based prediction of gene essentiality

    CERN Document Server

    da Silva, Joao Paulo Muller; Mombach, Jose Carlos Merino; Vieira, Renata; da Silva, Jose Guliherme Camargo; Lemke, Ney; Sinigaglia, Marialva

    2007-01-01

    The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision tree-based machine learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes...

  20. High solar activity predictions through an artificial neural network

    Science.gov (United States)

    Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

    The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.