A cluster expansion approach to exponential random graph models

International Nuclear Information System (INIS)

Yin, Mei

2012-01-01

The exponential family of random graphs are among the most widely studied network models. We show that any exponential random graph model may alternatively be viewed as a lattice gas model with a finite Banach space norm. The system may then be treated using cluster expansion methods from statistical mechanics. In particular, we derive a convergent power series expansion for the limiting free energy in the case of small parameters. Since the free energy is the generating function for the expectations of other random variables, this characterizes the structure and behavior of the limiting network in this parameter region

Novel approaches to pin cluster synchronization on complex dynamical networks in Lur'e forms

Science.gov (United States)

Tang, Ze; Park, Ju H.; Feng, Jianwen

2018-04-01

This paper investigates the cluster synchronization of complex dynamical networks consisted of identical or nonidentical Lur'e systems. Due to the special topology structure of the complex networks and the existence of stochastic perturbations, a kind of randomly occurring pinning controller is designed which not only synchronizes all Lur'e systems in the same cluster but also decreases the negative influence among different clusters. Firstly, based on an extended integral inequality, the convex combination theorem and S-procedure, the conditions for cluster synchronization of identical Lur'e networks are derived in a convex domain. Secondly, randomly occurring adaptive pinning controllers with two independent Bernoulli stochastic variables are designed and then sufficient conditions are obtained for the cluster synchronization on complex networks consisted of nonidentical Lur'e systems. In addition, suitable control gains for successful cluster synchronization of nonidentical Lur'e networks are acquired by designing some adaptive updating laws. Finally, we present two numerical examples to demonstrate the validity of the control scheme and the theoretical analysis.

Clustering promotes switching dynamics in networks of noisy neurons

Science.gov (United States)

Franović, Igor; Klinshov, Vladimir

2018-02-01

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Automatic Decentralized Clustering for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Wen Chih-Yu

2005-01-01

Full Text Available We propose a decentralized algorithm for organizing an ad hoc sensor network into clusters. Each sensor uses a random waiting timer and local criteria to determine whether to form a new cluster or to join a current cluster. The algorithm operates without a centralized controller, it operates asynchronously, and does not require that the location of the sensors be known a priori. Simplified models are used to estimate the number of clusters formed, and the energy requirements of the algorithm are investigated. The performance of the algorithm is described analytically and via simulation.

Nonlinear random resistor diode networks and fractal dimensions of directed percolation clusters.

Science.gov (United States)

Stenull, O; Janssen, H K

2001-07-01

We study nonlinear random resistor diode networks at the transition from the nonpercolating to the directed percolating phase. The resistor-like bonds and the diode-like bonds under forward bias voltage obey a generalized Ohm's law V approximately I(r). Based on general grounds such as symmetries and relevance we develop a field theoretic model. We focus on the average two-port resistance, which is governed at the transition by the resistance exponent straight phi(r). By employing renormalization group methods we calculate straight phi(r) for arbitrary r to one-loop order. Then we address the fractal dimensions characterizing directed percolation clusters. Via considering distinct values of the nonlinearity r, we determine the dimension of the red bonds, the chemical path, and the backbone to two-loop order.

Text Clustering Algorithm Based on Random Cluster Core

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Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Clustering of correlated networks

OpenAIRE

Dorogovtsev, S. N.

2003-01-01

We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

Clustering of resting state networks.

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Megan H Lee

Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

Gossip in Random Networks

Science.gov (United States)

Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.

2006-11-01

We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.

Network formation determined by the diffusion process of random walkers

International Nuclear Information System (INIS)

Ikeda, Nobutoshi

2008-01-01

We studied the diffusion process of random walkers in networks formed by their traces. This model considers the rise and fall of links determined by the frequency of transports of random walkers. In order to examine the relation between the formed network and the diffusion process, a situation in which multiple random walkers start from the same vertex is investigated. The difference in diffusion rate of random walkers according to the difference in dimension of the initial lattice is very important for determining the time evolution of the networks. For example, complete subgraphs can be formed on a one-dimensional lattice while a graph with a power-law vertex degree distribution is formed on a two-dimensional lattice. We derived some formulae for predicting network changes for the 1D case, such as the time evolution of the size of nearly complete subgraphs and conditions for their collapse. The networks formed on the 2D lattice are characterized by the existence of clusters of highly connected vertices and their life time. As the life time of such clusters tends to be small, the exponent of the power-law distribution changes from γ ≅ 1-2 to γ ≅ 3

Clustering algorithm in initialization of multi-hop wireless sensor networks

NARCIS (Netherlands)

Guo, Peng; Tao, Jiang; Zhang, Kui; Chen, Hsiao-Hwa

2009-01-01

In most application scenarios of wireless sensor networks (WSN), sensor nodes are usually deployed randomly and do not have any knowledge about the network environment or even their ID's at the initial stage of their operations. In this paper, we address the clustering problems with a newly deployed

How to cluster in parallel with neural networks

Science.gov (United States)

Kamgar-Parsi, Behzad; Gualtieri, J. A.; Devaney, Judy E.; Kamgar-Parsi, Behrooz

1988-01-01

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

Cluster randomization and political philosophy.

Science.gov (United States)

Chwang, Eric

2012-11-01

In this paper, I will argue that, while the ethical issues raised by cluster randomization can be challenging, they are not new. My thesis divides neatly into two parts. In the first, easier part I argue that many of the ethical challenges posed by cluster randomized human subjects research are clearly present in other types of human subjects research, and so are not novel. In the second, more difficult part I discuss the thorniest ethical challenge for cluster randomized research--cases where consent is genuinely impractical to obtain. I argue that once again these cases require no new analytic insight; instead, we should look to political philosophy for guidance. In other words, the most serious ethical problem that arises in cluster randomized research also arises in political philosophy. © 2011 Blackwell Publishing Ltd.

Generating clustered scale-free networks using Poisson based localization of edges

Science.gov (United States)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Clustering Coefficients for Correlation Networks

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Naoki Masuda

2018-03-01

Full Text Available Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients

Clustering Coefficients for Correlation Networks.

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

Clustering Coefficients for Correlation Networks

Science.gov (United States)

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly

Unraveling spurious properties of interaction networks with tailored random networks.

Directory of Open Access Journals (Sweden)

Stephan Bialonski

Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Performance Analysis of Cluster Formation in Wireless Sensor Networks.

Science.gov (United States)

Montiel, Edgar Romo; Rivero-Angeles, Mario E; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo

2017-12-13

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Performance Analysis of Cluster Formation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Edgar Romo Montiel

2017-12-01

Full Text Available Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Single-cluster dynamics for the random-cluster model

NARCIS (Netherlands)

Deng, Y.; Qian, X.; Blöte, H.W.J.

2009-01-01

We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

Research on Some Bus Transport Networks with Random Overlapping Clique Structure

International Nuclear Information System (INIS)

Yang Xuhua; Sun Youxian; Wang Bo; Wang Wanliang

2008-01-01

On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs

Bond percolation on a class of correlated and clustered random graphs

International Nuclear Information System (INIS)

Allard, A; Hébert-Dufresne, L; Noël, P-A; Marceau, V; Dubé, L J

2012-01-01

We introduce a formalism for computing bond percolation properties of a class of correlated and clustered random graphs. This class of graphs is a generalization of the configuration model where nodes of different types are connected via different types of hyperedges, edges that can link more than two nodes. We argue that the multitype approach coupled with the use of clustered hyperedges can reproduce a wide spectrum of complex patterns, and thus enhances our capability to model real complex networks. As an illustration of this claim, we use our formalism to highlight unusual behaviours of the size and composition of the components (small and giant) in a synthetic, albeit realistic, social network. (paper)

Modulation aware cluster size optimisation in wireless sensor networks

Science.gov (United States)

Sriram Naik, M.; Kumar, Vinay

2017-07-01

Wireless sensor networks (WSNs) play a great role because of their numerous advantages to the mankind. The main challenge with WSNs is the energy efficiency. In this paper, we have focused on the energy minimisation with the help of cluster size optimisation along with consideration of modulation effect when the nodes are not able to communicate using baseband communication technique. Cluster size optimisations is important technique to improve the performance of WSNs. It provides improvement in energy efficiency, network scalability, network lifetime and latency. We have proposed analytical expression for cluster size optimisation using traditional sensing model of nodes for square sensing field with consideration of modulation effects. Energy minimisation can be achieved by changing the modulation schemes such as BPSK, 16-QAM, QPSK, 64-QAM, etc., so we are considering the effect of different modulation techniques in the cluster formation. The nodes in the sensing fields are random and uniformly deployed. It is also observed that placement of base station at centre of scenario enables very less number of modulation schemes to work in energy efficient manner but when base station placed at the corner of the sensing field, it enable large number of modulation schemes to work in energy efficient manner.

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

Science.gov (United States)

Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

2015-07-21

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Science.gov (United States)

Li, Min; Li, Dongyan; Tang, Yu; Wu, Fangxiang; Wang, Jianxin

2017-08-31

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Analyzing the factors affecting network lifetime cluster-based wireless sensor network

International Nuclear Information System (INIS)

Malik, A.S.; Qureshi, A.

2010-01-01

Cluster-based wireless sensor networks enable the efficient utilization of the limited energy resources of the deployed sensor nodes and hence prolong the node as well as network lifetime. Low Energy Adaptive Clustering Hierarchy (Leach) is one of the most promising clustering protocol proposed for wireless sensor networks. This paper provides the energy utilization and lifetime analysis for cluster-based wireless sensor networks based upon LEACH protocol. Simulation results identify some important factors that induce unbalanced energy utilization between the sensor nodes and hence affect the network lifetime in these types of networks. These results highlight the need for a standardized, adaptive and distributed clustering technique that can increase the network lifetime by further balancing the energy utilization among sensor nodes. (author)

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

Science.gov (United States)

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

Node clustering for wireless sensor networks

International Nuclear Information System (INIS)

Bhatti, S.; Qureshi, I.A.; Memon, S.

2012-01-01

Recent years have witnessed considerable growth in the development and deployment of clustering methods which are not only used to maintain network resources but also increases the reliability of the WSNs (Wireless Sensor Network) and the facts manifest by the wide range of clustering solutions. Node clustering by selecting key parameters to tackle the dynamic behaviour of resource constraint WSN is a challenging issue. This paper highlights the recent progress which has been carried out pertaining to the development of clustering solutions for the WSNs. The paper presents classification of node clustering methods and their comparison based on the objectives, clustering criteria and methodology. In addition, the potential open issues which need to be considered for future work are high lighted. Keywords: Clustering, Sensor Network, Static, Dynamic

Random matrix improved subspace clustering

KAUST Repository

Couillet, Romain; Kammoun, Abla

2017-01-01

This article introduces a spectral method for statistical subspace clustering. The method is built upon standard kernel spectral clustering techniques, however carefully tuned by theoretical understanding arising from random matrix findings. We show

Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

Directory of Open Access Journals (Sweden)

Olszewski Kellen L

2007-07-01

Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

Concurrent conditional clustering of multiple networks: COCONETS.

Directory of Open Access Journals (Sweden)

Sabrina Kleessen

Full Text Available The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components. We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

International Nuclear Information System (INIS)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-01-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network

Cluster synchronization induced by one-node clusters in networks with asymmetric negative couplings

Science.gov (United States)

Zhang, Jianbao; Ma, Zhongjun; Zhang, Gang

2013-12-01

This paper deals with the problem of cluster synchronization in networks with asymmetric negative couplings. By decomposing the coupling matrix into three matrices, and employing Lyapunov function method, sufficient conditions are derived for cluster synchronization. The conditions show that the couplings of multi-node clusters from one-node clusters have beneficial effects on cluster synchronization. Based on the effects of the one-node clusters, an effective and universal control scheme is put forward for the first time. The obtained results may help us better understand the relation between cluster synchronization and cluster structures of the networks. The validity of the control scheme is confirmed through two numerical simulations, in a network with no cluster structure and in a scale-free network.

Competitive cluster growth in complex networks.

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Moreira, André A; Paula, Demétrius R; Costa Filho, Raimundo N; Andrade, José S

2006-06-01

In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

Energy Aware Clustering Algorithms for Wireless Sensor Networks

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Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

2011-09-01

The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

Peeking Network States with Clustered Patterns

Energy Technology Data Exchange (ETDEWEB)

Kim, Jinoh [Texas A & M Univ., Commerce, TX (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-10-20

Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learning tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.

Design and methods of a social network isolation study for reducing respiratory infection transmission: The eX-FLU cluster randomized trial.

Science.gov (United States)

Aiello, Allison E; Simanek, Amanda M; Eisenberg, Marisa C; Walsh, Alison R; Davis, Brian; Volz, Erik; Cheng, Caroline; Rainey, Jeanette J; Uzicanin, Amra; Gao, Hongjiang; Osgood, Nathaniel; Knowles, Dylan; Stanley, Kevin; Tarter, Kara; Monto, Arnold S

2016-06-01

Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as "seed" participants, who nominated their social contacts to join the study, of which 328 "nominees" enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all Pisolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server

CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

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Franke, R.

2016-11-01

In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

A random walk evolution model of wireless sensor networks and virus spreading

International Nuclear Information System (INIS)

Wang Ya-Qi; Yang Xiao-Yuan

2013-01-01

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations. (general)

GraphCrunch 2: Software tool for network modeling, alignment and clustering.

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Kuchaiev, Oleksii; Stevanović, Aleksandar; Hayes, Wayne; Pržulj, Nataša

2011-01-19

Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI) data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL") for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other existing tool. Finally, GraphCruch 2 implements an

GraphCrunch 2: Software tool for network modeling, alignment and clustering

Directory of Open Access Journals (Sweden)

Hayes Wayne

2011-01-01

Full Text Available Abstract Background Recent advancements in experimental biotechnology have produced large amounts of protein-protein interaction (PPI data. The topology of PPI networks is believed to have a strong link to their function. Hence, the abundance of PPI data for many organisms stimulates the development of computational techniques for the modeling, comparison, alignment, and clustering of networks. In addition, finding representative models for PPI networks will improve our understanding of the cell just as a model of gravity has helped us understand planetary motion. To decide if a model is representative, we need quantitative comparisons of model networks to real ones. However, exact network comparison is computationally intractable and therefore several heuristics have been used instead. Some of these heuristics are easily computable "network properties," such as the degree distribution, or the clustering coefficient. An important special case of network comparison is the network alignment problem. Analogous to sequence alignment, this problem asks to find the "best" mapping between regions in two networks. It is expected that network alignment might have as strong an impact on our understanding of biology as sequence alignment has had. Topology-based clustering of nodes in PPI networks is another example of an important network analysis problem that can uncover relationships between interaction patterns and phenotype. Results We introduce the GraphCrunch 2 software tool, which addresses these problems. It is a significant extension of GraphCrunch which implements the most popular random network models and compares them with the data networks with respect to many network properties. Also, GraphCrunch 2 implements the GRAph ALigner algorithm ("GRAAL" for purely topological network alignment. GRAAL can align any pair of networks and exposes large, dense, contiguous regions of topological and functional similarities far larger than any other

Clustering coefficient and community structure of bipartite networks

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Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

2008-12-01

Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

Curvature of Indoor Sensor Network: Clustering Coefficient

Directory of Open Access Journals (Sweden)

2009-03-01

Full Text Available We investigate the geometric properties of the communication graph in realistic low-power wireless networks. In particular, we explore the concept of the curvature of a wireless network via the clustering coefficient. Clustering coefficient analysis is a computationally simplified, semilocal approach, which nevertheless captures such a large-scale feature as congestion in the underlying network. The clustering coefficient concept is applied to three cases of indoor sensor networks, under varying thresholds on the link packet reception rate (PRR. A transition from positive curvature (“meshed” network to negative curvature (“core concentric” network is observed by increasing the threshold. Even though this paper deals with network curvature per se, we nevertheless expand on the underlying congestion motivation, propose several new concepts (network inertia and centroid, and finally we argue that greedy routing on a virtual positively curved network achieves load balancing on the physical network.

ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

Directory of Open Access Journals (Sweden)

T. SHANKAR

2014-04-01

Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

Cluster-size entropy in the Axelrod model of social influence: Small-world networks and mass media

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Gandica, Y.; Charmell, A.; Villegas-Febres, J.; Bonalde, I.

2011-10-01

We study the Axelrod's cultural adaptation model using the concept of cluster-size entropy Sc, which gives information on the variability of the cultural cluster size present in the system. Using networks of different topologies, from regular to random, we find that the critical point of the well-known nonequilibrium monocultural-multicultural (order-disorder) transition of the Axelrod model is given by the maximum of the Sc(q) distributions. The width of the cluster entropy distributions can be used to qualitatively determine whether the transition is first or second order. By scaling the cluster entropy distributions we were able to obtain a relationship between the critical cultural trait qc and the number F of cultural features in two-dimensional regular networks. We also analyze the effect of the mass media (external field) on social systems within the Axelrod model in a square network. We find a partially ordered phase whose largest cultural cluster is not aligned with the external field, in contrast with a recent suggestion that this type of phase cannot be formed in regular networks. We draw a q-B phase diagram for the Axelrod model in regular networks.

Distributed controller clustering in software defined networks.

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Ahmed Abdelaziz

Full Text Available Software Defined Networking (SDN is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN SDN and Open Network Operating System (ONOS controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

Distributed controller clustering in software defined networks.

Science.gov (United States)

Abdelaziz, Ahmed; Fong, Ang Tan; Gani, Abdullah; Garba, Usman; Khan, Suleman; Akhunzada, Adnan; Talebian, Hamid; Choo, Kim-Kwang Raymond

2017-01-01

Software Defined Networking (SDN) is an emerging promising paradigm for network management because of its centralized network intelligence. However, the centralized control architecture of the software-defined networks (SDNs) brings novel challenges of reliability, scalability, fault tolerance and interoperability. In this paper, we proposed a novel clustered distributed controller architecture in the real setting of SDNs. The distributed cluster implementation comprises of multiple popular SDN controllers. The proposed mechanism is evaluated using a real world network topology running on top of an emulated SDN environment. The result shows that the proposed distributed controller clustering mechanism is able to significantly reduce the average latency from 8.1% to 1.6%, the packet loss from 5.22% to 4.15%, compared to distributed controller without clustering running on HP Virtual Application Network (VAN) SDN and Open Network Operating System (ONOS) controllers respectively. Moreover, proposed method also shows reasonable CPU utilization results. Furthermore, the proposed mechanism makes possible to handle unexpected load fluctuations while maintaining a continuous network operation, even when there is a controller failure. The paper is a potential contribution stepping towards addressing the issues of reliability, scalability, fault tolerance, and inter-operability.

Gradient networks on uncorrelated random scale-free networks

International Nuclear Information System (INIS)

Pan Guijun; Yan Xiaoqing; Huang Zhongbing; Ma Weichuan

2011-01-01

Uncorrelated random scale-free (URSF) networks are useful null models for checking the effects of scale-free topology on network-based dynamical processes. Here, we present a comparative study of the jamming level of gradient networks based on URSF networks and Erdos-Renyi (ER) random networks. We find that the URSF networks are less congested than ER random networks for the average degree (k)>k c (k c ∼ 2 denotes a critical connectivity). In addition, by investigating the topological properties of the two kinds of gradient networks, we discuss the relations between the topological structure and the transport efficiency of the gradient networks. These findings show that the uncorrelated scale-free structure might allow more efficient transport than the random structure.

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks.

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Wu, Jibing; Meng, Qinggang; Deng, Su; Huang, Hongbin; Wu, Yahui; Badii, Atta

2017-01-01

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.

Graph analysis of cell clusters forming vascular networks

Science.gov (United States)

Alves, A. P.; Mesquita, O. N.; Gómez-Gardeñes, J.; Agero, U.

2018-03-01

This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.

Maximal planar networks with large clustering coefficient and power-law degree distribution

International Nuclear Information System (INIS)

Zhou Tao; Yan Gang; Wang Binghong

2005-01-01

In this article, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called random Apollonian networks (RANs) as they can be considered as a variation of Apollonian networks. We obtain the analytic results of power-law exponent γ=3 and clustering coefficient C=(46/3)-36 ln (3/2)≅0.74, which agree with the simulation results very well. We prove that the increasing tendency of average distance of RANs is a little slower than the logarithm of the number of nodes in RANs. Since most real-life networks are both scale-free and small-world networks, RANs may perform well in mimicking the reality. The RANs possess hierarchical structure as C(k)∼k -1 that are in accord with the observations of many real-life networks. In addition, we prove that RANs are maximal planar networks, which are of particular practicability for layout of printed circuits and so on. The percolation and epidemic spreading process are also studied and the comparisons between RANs and Barabasi-Albert (BA) as well as Newman-Watts (NW) networks are shown. We find that, when the network order N (the total number of nodes) is relatively small (as N∼10 4 ), the performance of RANs under intentional attack is not sensitive to N, while that of BA networks is much affected by N. And the diseases spread slower in RANs than BA networks in the early stage of the suseptible-infected process, indicating that the large clustering coefficient may slow the spreading velocity, especially in the outbreaks

Creating, generating and comparing random network models with NetworkRandomizer.

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Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

2016-01-01

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Clusters in nonsmooth oscillator networks

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Nicks, Rachel; Chambon, Lucie; Coombes, Stephen

2018-03-01

For coupled oscillator networks with Laplacian coupling, the master stability function (MSF) has proven a particularly powerful tool for assessing the stability of the synchronous state. Using tools from group theory, this approach has recently been extended to treat more general cluster states. However, the MSF and its generalizations require the determination of a set of Floquet multipliers from variational equations obtained by linearization around a periodic orbit. Since closed form solutions for periodic orbits are invariably hard to come by, the framework is often explored using numerical techniques. Here, we show that further insight into network dynamics can be obtained by focusing on piecewise linear (PWL) oscillator models. Not only do these allow for the explicit construction of periodic orbits, their variational analysis can also be explicitly performed. The price for adopting such nonsmooth systems is that many of the notions from smooth dynamical systems, and in particular linear stability, need to be modified to take into account possible jumps in the components of Jacobians. This is naturally accommodated with the use of saltation matrices. By augmenting the variational approach for studying smooth dynamical systems with such matrices we show that, for a wide variety of networks that have been used as models of biological systems, cluster states can be explicitly investigated. By way of illustration, we analyze an integrate-and-fire network model with event-driven synaptic coupling as well as a diffusively coupled network built from planar PWL nodes, including a reduction of the popular Morris-Lecar neuron model. We use these examples to emphasize that the stability of network cluster states can depend as much on the choice of single node dynamics as it does on the form of network structural connectivity. Importantly, the procedure that we present here, for understanding cluster synchronization in networks, is valid for a wide variety of systems in

Clusters and holes: Exchange networks in hematite-ilmenite solid solutions

Science.gov (United States)

Fabian, K.; McEnroe, S. A.; Robinson, P.

2009-04-01

Holes and clusters of exchange networks dominate the low-temperature, metastable phase diagram of the system (1 - x)Fe2O3 xF eTiO3 (Ilmx ). By our measurements we have probed and extended the phase diagram of Ishikawa et al. (1985) in the light of magnetic influences of the random exchange links, which originate either by replacing random pairs of Fe2+ and Ti4+ ions in the ordered ilmenite lattice by two Fe3+ions (ordered Ilmx phase), or by randomly replacing two Fe3+ ions in the hematite lattice by a pair of Fe2+ and Ti4+ ions (disordered Ilmx phase). Now a large dataset is available from these measurements, and we propose several new ideas to interpret the sometimes unexpected results. By refining a method of Ishikawa (1967), we analyze the PM' region of the phase diagram in terms of a mean field theory of interacting clusters. This allows to determine cluster sizes and interaction field distribution by inverting hysteresis measurements of Ilm92 and Ilm97. To understand the relation between ordered and disordered phases we design a mean field theory to determine Neel and Curie temperatures of both. An especially interesting finding is that the experimentally observed intersection of PM-PM' crossover with the AF phase boundary close to Ilm97 can be explained by analyzing average exchange interaction strengths.

MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters.

Science.gov (United States)

Gonzalez-Dominguez, Jorge; Martin, Maria J

2017-10-10

In this work we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. Source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

Directory of Open Access Journals (Sweden)

Srinivas Kanakala

2014-01-01

Full Text Available In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing protocol to further reduce the energy consumption. The proposed energy-efficient coding-aware cluster based routing protocol (ECCRP scheme applies network coding at cluster heads to reduce number of transmissions. We also modify the queue management procedure of COPE protocol to further improve coding opportunities. We also use an energy efficient scheme while selecting the cluster head. It helps to increase the life time of the network. We evaluate the performance of proposed energy efficient cluster based protocol using simulation. Simulation results show that the proposed ECCRP algorithm reduces energy consumption and increases life time of the network.

Analytical network process based optimum cluster head selection in wireless sensor network.

Science.gov (United States)

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

A Clustering Routing Protocol for Mobile Ad Hoc Networks

Directory of Open Access Journals (Sweden)

Jinke Huang

2016-01-01

Full Text Available The dynamic topology of a mobile ad hoc network poses a real challenge in the design of hierarchical routing protocol, which combines proactive with reactive routing protocols and takes advantages of both. And as an essential technique of hierarchical routing protocol, clustering of nodes provides an efficient method of establishing a hierarchical structure in mobile ad hoc networks. In this paper, we designed a novel clustering algorithm and a corresponding hierarchical routing protocol for large-scale mobile ad hoc networks. Each cluster is composed of a cluster head, several cluster gateway nodes, several cluster guest nodes, and other cluster members. The proposed routing protocol uses proactive protocol between nodes within individual clusters and reactive protocol between clusters. Simulation results show that the proposed clustering algorithm and hierarchical routing protocol provide superior performance with several advantages over existing clustering algorithm and routing protocol, respectively.

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects.

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Dreyhaupt, Jens; Mayer, Benjamin; Keis, Oliver; Öchsner, Wolfgang; Muche, Rainer

2017-01-01

An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention) studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called "cluster randomization"). Compared with studies with individual randomization, studies with cluster randomization normally require (significantly) larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies. Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

New Heterogeneous Clustering Protocol for Prolonging Wireless Sensor Networks Lifetime

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Md. Golam Rashed

2014-06-01

Full Text Available Clustering in wireless sensor networks is one of the crucial methods for increasing of network lifetime. The network characteristics of existing classical clustering protocols for wireless sensor network are homogeneous. Clustering protocols fail to maintain the stability of the system, especially when nodes are heterogeneous. We have seen that the behavior of Heterogeneous-Hierarchical Energy Aware Routing Protocol (H-HEARP becomes very unstable once the first node dies, especially in the presence of node heterogeneity. In this paper we assume a new clustering protocol whose network characteristics is heterogeneous for prolonging of network lifetime. The computer simulation results demonstrate that the proposed clustering algorithm outperforms than other clustering algorithms in terms of the time interval before the death of the first node (we refer to as stability period. The simulation results also show the high performance of the proposed clustering algorithm for higher values of extra energy brought by more powerful nodes.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

Science.gov (United States)

Emmons, Scott; Kobourov, Stephen; Gallant, Mike; Börner, Katy

2016-01-01

Notions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms-Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information. Smart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

Cluster-randomized Studies in Educational Research: Principles and Methodological Aspects

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Dreyhaupt, Jens

2017-05-01

Full Text Available An increasing number of studies are being performed in educational research to evaluate new teaching methods and approaches. These studies could be performed more efficiently and deliver more convincing results if they more strictly applied and complied with recognized standards of scientific studies. Such an approach could substantially increase the quality in particular of prospective, two-arm (intervention studies that aim to compare two different teaching methods. A key standard in such studies is randomization, which can minimize systematic bias in study findings; such bias may result if the two study arms are not structurally equivalent. If possible, educational research studies should also achieve this standard, although this is not yet generally the case. Some difficulties and concerns exist, particularly regarding organizational and methodological aspects. An important point to consider in educational research studies is that usually individuals cannot be randomized, because of the teaching situation, and instead whole groups have to be randomized (so-called “cluster randomization”. Compared with studies with individual randomization, studies with cluster randomization normally require (significantly larger sample sizes and more complex methods for calculating sample size. Furthermore, cluster-randomized studies require more complex methods for statistical analysis. The consequence of the above is that a competent expert with respective special knowledge needs to be involved in all phases of cluster-randomized studies.Studies to evaluate new teaching methods need to make greater use of randomization in order to achieve scientifically convincing results. Therefore, in this article we describe the general principles of cluster randomization and how to implement these principles, and we also outline practical aspects of using cluster randomization in prospective, two-arm comparative educational research studies.

Impact of heuristics in clustering large biological networks.

Science.gov (United States)

Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel

2015-12-01

Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. Copyright © 2015 Elsevier Ltd. All rights reserved.

Minimizing Broadcast Expenses in Clustered Ad-hoc Networks

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S. Zeeshan Hussain

2018-01-01

Full Text Available One way to minimize the broadcast expenses of routing protocols is to cluster the network. In clustered ad-hoc networks, all resources can be managed easily by resolving scalability issues. However, blind query broadcast is a major issue that leads to the broadcast storm problem in clustered ad-hoc networks. This query broadcast is done to carry out the route-search task that leads to the unnecessary propagation of route-query even after route has been found. Hence, this query propagation poses the problem of congestion in the network. In particular this motivates us to propose a query-control technique in such networks which works based on broadcast repealing. A huge amount of work has been devoted to propose the query control broadcasting techniques. However, such techniques used in traditional broadcasting mechanisms need to be properly extended for use in the cluster based routing architecture. In this paper, query-control technique is proposed for cluster based routing technique to reduce the broadcast expenses. Finally, we report some experiments which compare the proposed technique to other commonly used techniques including standard one-class AODV that follows TTL-sequence based broadcasting technique.

Clustering and visualizing similarity networks of membrane proteins.

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Hu, Geng-Ming; Mai, Te-Lun; Chen, Chi-Ming

2015-08-01

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. © 2015 Wiley Periodicals, Inc.

Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.

Science.gov (United States)

Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu

2013-01-01

Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Science.gov (United States)

Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

2017-10-13

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

International Nuclear Information System (INIS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-01-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

Science.gov (United States)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jianbao [School of Science, Hangzhou Dianzi University, Hangzhou 310018 (China); Ma, Zhongjun, E-mail: mzj1234402@163.com [School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004 (China); Chen, Guanrong [Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong (China)

2014-06-15

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Simple product-form bounds for queueing networks with finite clusters

NARCIS (Netherlands)

van Dijk, N.M.; van der Sluis, E.

2001-01-01

Queueing networks are studied with finite capacity constraints for clusters of stations. First, by an instructive tandem cluster example it is shown how a product-form modification method for networks with finite stations can be extended to networks with finite clusters. Next, a general result is

The Design of Cluster Randomized Trials with Random Cross-Classifications

Science.gov (United States)

Moerbeek, Mirjam; Safarkhani, Maryam

2018-01-01

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…

An Energy-Efficient Adaptive Clustering Protocol for Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Lü Tao

2013-05-01

Full Text Available An energy-efficient adaptive clustering hierarchy EEACH in wireless sensor networks based on LEACH and LEACH-C is proposed in this paper. The main consideration is the LEACH cluster structure, each cluster is not uniform energy consumption; LEACH-C using a centralized algorithm can achieve better clustering, but do not contribute to the implementation of distributed. In EEACH, we analyzed the effects of different numbers of cluster member node on the network energy consumption; and re-planning time slice to balance the energy consumption of each cluster; and avoid the energy hole problem by reasonable cluster head selection algorithm. Its objective is to balance the energy consumption and maximize the network lifetime. Analysis and simulation results show that EEACH provides more uniform energy consumption among nodes and can prolong network lifetime compared to LEACH and LEACH-C.

Cluster synchronization transmission of different external signals in discrete uncertain network

Science.gov (United States)

Li, Chengren; Lü, Ling; Chen, Liansong; Hong, Yixuan; Zhou, Shuang; Yang, Yiming

2018-07-01

We research cluster synchronization transmissions of different external signals in discrete uncertain network. Based on the Lyapunov theorem, the network controller and the identification law of uncertain adjustment parameter are designed, and they are efficiently used to achieve the cluster synchronization and the identification of uncertain adjustment parameter. In our technical scheme, the network nodes in each cluster and the transmitted external signal can be different, and they allow the presence of uncertain parameters in the network. Especially, we are free to choose the clustering topologies, the cluster number and the node number in each cluster.

A priori data-driven multi-clustered reservoir generation algorithm for echo state network.

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Xiumin Li

Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.

Cluster Tails for Critical Power-Law Inhomogeneous Random Graphs

Science.gov (United States)

van der Hofstad, Remco; Kliem, Sandra; van Leeuwaarden, Johan S. H.

2018-04-01

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299-2361, 2012). It was proved that when the degrees obey a power law with exponent τ \\in (3,4), the sequence of clusters ordered in decreasing size and multiplied through by n^{-(τ -2)/(τ -1)} converges as n→ ∞ to a sequence of decreasing non-degenerate random variables. Here, we study the tails of the limit of the rescaled largest cluster, i.e., the probability that the scaling limit of the largest cluster takes a large value u, as a function of u. This extends a related result of Pittel (J Combin Theory Ser B 82(2):237-269, 2001) for the Erdős-Rényi random graph to the setting of rank-1 inhomogeneous random graphs with infinite third moment degrees. We make use of delicate large deviations and weak convergence arguments.

Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

International Nuclear Information System (INIS)

Deng Liping; Wu Zhaoyan

2012-01-01

In this paper, cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated. Community networks with two kinds of topological structure are investigated. Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization. Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers. Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory, several simple and useful synchronization criteria are derived. Finally, numerical simulations are provided to verify the effectiveness of the derived results.

Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

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FANG, W.

2012-02-01

Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

Science.gov (United States)

2016-05-20

RESEARCH ARTICLE Link-Prediction Enhanced Consensus Clustering for Complex Networks Matthew Burgess1*, Eytan Adar1,2, Michael Cafarella1 1Computer...consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that...types of complex networks exhibit community structure: groups of highly connected nodes. Communities or clusters often reflect nodes that share similar

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

Science.gov (United States)

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

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Yongwei Zhang

2017-01-01

Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.

Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network

OpenAIRE

Rashed, Md. Golam; Kabir, M. Hasnat; Rahim, Muhammad Sajjadur; Ullah, Shaikh Enayet

2012-01-01

The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that t...

Cluster Approach to Network Interaction in Pedagogical University

Science.gov (United States)

Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

2016-01-01

The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

CMOS: efficient clustered data monitoring in sensor networks.

Science.gov (United States)

Min, Jun-Ki

2013-01-01

Tiny and smart sensors enable applications that access a network of hundreds or thousands of sensors. Thus, recently, many researchers have paid attention to wireless sensor networks (WSNs). The limitation of energy is critical since most sensors are battery-powered and it is very difficult to replace batteries in cases that sensor networks are utilized outdoors. Data transmission between sensor nodes needs more energy than computation in a sensor node. In order to reduce the energy consumption of sensors, we present an approximate data gathering technique, called CMOS, based on the Kalman filter. The goal of CMOS is to efficiently obtain the sensor readings within a certain error bound. In our approach, spatially close sensors are grouped as a cluster. Since a cluster header generates approximate readings of member nodes, a user query can be answered efficiently using the cluster headers. In addition, we suggest an energy efficient clustering method to distribute the energy consumption of cluster headers. Our simulation results with synthetic data demonstrate the efficiency and accuracy of our proposed technique.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

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Gulnaz Ahmed

2017-02-01

Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

Two Tier Cluster Based Data Aggregation (TTCDA) in Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2012-01-01

Wireless Sensor Network (WSN) often used for monitoring and control applications where sensor nodes collect data and send it to the sink. Most of the nodes consume their energy in transmission of data packets without aggregation to sink, which may be located at single or multi hop distance....... The direct transmission of data packets to the sink from nodes in the network causes increased communication costs in terms of energy, average delay and network lifetime. In this context, the data aggregation techniques minimize the communication cost with efficient bandwidth utilization by decreasing...... the packet count reached at the sink. Here, we propose Two Tier Cluster based Data Aggregation (TTCDA) algorithm for the randomly distributed nodes to minimize computation and communication cost. The TTCDA is energy and bandwidth efficient since it reduces the transmission of the number of packets...

Donor Interventions and SME Networking in Industrial Clusters in Punjab Province, Pakistan

DEFF Research Database (Denmark)

Fayyaz, Anjum

. Thus, the author seeks to answer two interrelated questions in this thesis: a) How did the IDA interventions affect SME networks in the Lahore garments and Sialkot football clusters in Pakistan? b) Why did the IDA-supported SME networks in these clusters continue or discontinue after the IDA withdrew...... the IDA’s interventions affected the long-term sustainability of SME networks present in both clusters. In the case of the Lahore garments cluster, the IDA supported two networks in the cluster. One was known as the Lahore Garments Consortium and the other was named the Lahore Fashion Apparel Network...

Evaluation of clustering algorithms for protein-protein interaction networks

Directory of Open Access Journals (Sweden)

van Helden Jacques

2006-11-01

Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This

Study on distributed re-clustering algorithm for moblie wireless sensor networks

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XU Chaojie

2016-04-01

Full Text Available In mobile wireless sensor networks,node mobility influences the topology of the hierarchically clustered network,thus affects packet delivery ratio and energy consumption of communications in clusters.To reduce the influence of node mobility,a distributed re-clustering algorithm is proposed in this paper.In this algorithm,basing on the clustered network,nodes estimate their current locations with particle algorithm and predict the most possible locations of next time basing on the mobility model.Each boundary node of a cluster periodically estimates the need for re-clustering and re-cluster itself to the optimal cluster through communicating with the cluster headers when needed.The simulation results indicate that,with small re-clustering periods,the proposed algorithm can be effective to keep appropriate communication distance and outperforms existing schemes on packet delivery ratio and energy consumption.

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

Science.gov (United States)

Li, Huaizhou; Zhou, Haiyan; Yang, Yang; Wang, Haiyuan; Zhong, Ning

2017-10-01

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Random walks and diffusion on networks

Science.gov (United States)

Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud

2017-11-01

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

A similarity based agglomerative clustering algorithm in networks

Science.gov (United States)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

A systematic review of the usage of flow diagram in cluster randomized trials

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Kostić M.

2014-01-01

Full Text Available Flow diagram represent an integral part of consolidated standards of reporting trials (CONSORT. Its use in reporting cluster randomization trials is highly recommended. The aim of this article is to present frequency of the use of flow diagram in cluster randomized trials in accordance with standards of reporting. The team has researched Medline database and singled-out 474 studies with cluster randomization for analysis. The studies were reviewed to identify the use of graphic representation, compliance with standards of reporting and the date when study was published. Depending from its duration, studies were divided on completed, and those still ongoing. Usage of CONSORT is recorded in 145 (31% literature units. Frequency of flow diagram was statistically much higher in studies which were in compliance with standards (86,2%, in comparison to those which did not use CONSORT guidelines (71,4%, as well as in completed studies (81,2% in comparison to pilot project studies (54,3%. Number of cluster randomized trials gathered through MEDLINE's search of key words 'cluster randomized trial [ti]' and 'cluster randomised trial [ti]', as well as the use of CONSORT in the reports of cluster randomized trials, are showing linear growth over time (p<0,001. Frequency of flow diagram is higher in the reports of cluster randomized trials that were done in accordance with the standards of reporting.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

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Vasileios Karyotis

2018-04-01

Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Performance of networks of artificial neurons: The role of clustering

International Nuclear Information System (INIS)

Kim, Beom Jun

2004-01-01

The performance of the Hopfield neural network model is numerically studied on various complex networks, such as the Watts-Strogatz network, the Barabasi-Albert network, and the neuronal network of Caenorhabditis elegans. Through the use of a systematic way of controlling the clustering coefficient, with the degree of each neuron kept unchanged, we find that the networks with the lower clustering exhibit much better performance. The results are discussed in the practical viewpoint of application, and the biological implications are also suggested

Cluster emergence and network evolution A longitudinal analysis of the inventor network in Sophia-Antipolis

OpenAIRE

Anne L. J. ter Wal

2008-01-01

Abstract It is increasingly acknowledged that clusters do not necessarily exhibit networks of local collective learning. This paper addresses the question under which conditions this is the case. Through a longitudinal case study of the business park Sophia-Antipolis it investigates how networks of collective learning emerged throughout the growth of the cluster. Network reconstruction with patent data shows that an innovation network emerged only in Information Technology, in whic...

Artificial immune kernel clustering network for unsupervised image segmentation

Institute of Scientific and Technical Information of China (English)

Wenlong Huang; Licheng Jiao

2008-01-01

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

Fully convolutional network with cluster for semantic segmentation

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Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.

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Martin Rosvall

Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring.

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Uddin, Mohammad Ammad; Mansour, Ali; Jeune, Denis Le; Ayaz, Mohammad; Aggoune, El-Hadi M

2018-02-11

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.

Fitting Latent Cluster Models for Networks with latentnet

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Pavel N. Krivitsky

2007-12-01

Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.

Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks

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Francisco Porcel-Rodríguez

2016-08-01

Full Text Available Energy efficiency is a critical issue for wireless sensor networks (WSNs as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (φ = 0° and θ = 45°; φ = 45°, and θ = 45°. Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors’ knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs.

FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network.

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He, Wei; Pillement, Sebastien; Xu, Du

2017-11-23

Clustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS), which gives rise to the long distance communications problem, and in multi-hop routing, the CHs near the BS have to forward data from other nodes that lead those CHs to die prematurely, creating the hot zones problem. Unequal clustering has been proposed to solve these problems. Most of the current algorithms elect CH only by considering their competition radius, leading to unevenly distributed cluster heads. Furthermore, global distances values are needed when calculating the competition radius, which is a tedious task in large networks. To face these problems, we propose a flooding tree uneven clustering protocol (FTUC) suited for large networks. Based on the construction of a tree type sub-network to calculate the minimum and maximum distances values of the network, we then apply the unequal cluster theory. We also introduce referenced position circles to evenly elect cluster heads. Therefore, cluster heads are elected depending on the node's residual energy and their distance to a referenced circle. FTUC builds the best inter-cluster communications route by evaluating a cluster head cost function to find the best next hop to the BS. The simulation results show that the FTUC algorithm decreases the energy consumption of the nodes and balances the global energy consumption effectively, thus extending the lifetime of the network.

Cluster protocols in Underwater Sensor Networks: a Research Review

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K. Ovaliadis

2014-07-01

Full Text Available Underwater Wireless Sensor Networks (UWSN have different characteristics in relation to terrestrial wireless sensor networks; such as limited bandwidth capacity, high propagation delays and limited battery power. Although there has been much work completed in developing protocols and models for terrestrial networks, these are rarely applicable for underwater sensor networks. Up to today major efforts have been made for designing efficient protocols while considering the underwater communication characteristics. An important issue on this research area is the construction of an efficient clustering algorithm. Clustering in the context of UWSN is important as it contributes a great deal towards the efficient use of energy resources. This paper reviews the most significant cluster based protocols proposed for UWSN. Major performance issues of these protocols with respect to the network conditions such as packet delivery ratio, average packet delay, node mobility effect and energy consumption are examined. The advantages and disadvantages of each protocol are also pointed out.

Random walk centrality for temporal networks

International Nuclear Information System (INIS)

Rocha, Luis E C; Masuda, Naoki

2014-01-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included. (paper)

Random walk centrality for temporal networks

Science.gov (United States)

Rocha, Luis E. C.; Masuda, Naoki

2014-06-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

A Cluster- Based Secure Active Network Environment

Institute of Scientific and Technical Information of China (English)

CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

2005-01-01

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Collaborative Clustering for Sensor Networks

Science.gov (United States)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative

RMBNToolbox: random models for biochemical networks

Directory of Open Access Journals (Sweden)

Niemi Jari

2007-05-01

Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

Generalization of Clustering Coefficients to Signed Correlation Networks

Science.gov (United States)

Costantini, Giulio; Perugini, Marco

2014-01-01

The recent interest in network analysis applications in personality psychology and psychopathology has put forward new methodological challenges. Personality and psychopathology networks are typically based on correlation matrices and therefore include both positive and negative edge signs. However, some applications of network analysis disregard negative edges, such as computing clustering coefficients. In this contribution, we illustrate the importance of the distinction between positive and negative edges in networks based on correlation matrices. The clustering coefficient is generalized to signed correlation networks: three new indices are introduced that take edge signs into account, each derived from an existing and widely used formula. The performances of the new indices are illustrated and compared with the performances of the unsigned indices, both on a signed simulated network and on a signed network based on actual personality psychology data. The results show that the new indices are more resistant to sample variations in correlation networks and therefore have higher convergence compared with the unsigned indices both in simulated networks and with real data. PMID:24586367

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

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Shawkat K. Guirguis

2017-01-01

Full Text Available Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN. Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the noders residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.

Competing Contact Processes on Homogeneous Networks with Tunable Clusterization

Science.gov (United States)

Rybak, Marcin; Kułakowski, Krzysztof

2013-03-01

We investigate two homogeneous networks: the Watts-Strogatz network with mean degree ⟨k⟩ = 4 and the Erdös-Rényi network with ⟨k⟩ = 10. In both kinds of networks, the clustering coefficient C is a tunable control parameter. The network is an area of two competing contact processes, where nodes can be in two states, S or D. A node S becomes D with probability 1 if at least two its mutually linked neighbors are D. A node D becomes S with a given probability p if at least one of its neighbors is S. The competition between the processes is described by a phase diagram, where the critical probability pc depends on the clustering coefficient C. For p > pc the rate of state S increases in time, seemingly to dominate in the whole system. Below pc, the majority of nodes is in the D-state. The numerical results indicate that for the Watts-Strogatz network the D-process is activated at the finite value of the clustering coefficient C, close to 0.3. On the contrary, for the Erdös-Rényi network the transition is observed at the whole investigated range of C.

Energy Efficient Clustering in Multi-hop Wireless Sensor Networks Using Differential Evolutionary MOPSO

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D. Rajendra Prasad

Full Text Available ABSTRACT The primary challenge in organizing sensor networks is energy efficacy. This requisite for energy efficacy is because sensor nodes capacities are limited and replacing them is not viable. This restriction further decreases network lifetime. Node lifetime varies depending on the requisites expected of its battery. Hence, primary element in constructing sensor networks is resilience to deal with decreasing lifetime of all sensor nodes. Various network infrastructures as well as their routing protocols for reduction of power utilization as well as to prolong network lifetime are studied. After analysis, it is observed that network constructions that depend on clustering are the most effective methods in terms of power utilization. Clustering divides networks into inter-related clusters such that every cluster has several sensor nodes with a Cluster Head (CH at its head. Sensor gathered information is transmitted to data processing centers through CH hierarchy in clustered environments. The current study utilizes Multi-Objective Particle Swarm Optimization (MOPSO-Differential Evolution (DE (MOPSO-DE technique for optimizing clustering.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

Science.gov (United States)

Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

2015-12-26

Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Liu Yang

2015-12-01

Full Text Available Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs in the network act as routers to transmit data to base station (BS cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

Weighted tunable clustering in local-world networks with increment behavior

International Nuclear Information System (INIS)

Ma, Ying-Hong; Li, Huijia; Zhang, Xiao-Dong

2010-01-01

Since some realistic networks are influenced not only by increment behavior but also by the tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and the tunable clustering mechanism, is proposed and its properties are investigated, such as degree distribution and clustering coefficient. Numerical simulations are fitted to the model and also display good right-skewed scale-free properties. Furthermore, the correlation of vertices in our model is studied which shows the assortative property. The epidemic spreading process by weighted transmission rate on the model shows that the tunable clustering behavior has a great impact on the epidemic dynamic

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

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Junkai Yi

2017-01-01

Full Text Available Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network

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Roy Sohini

2015-09-01

Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.

Detection of protein complex from protein-protein interaction network using Markov clustering

International Nuclear Information System (INIS)

Ochieng, P J; Kusuma, W A; Haryanto, T

2017-01-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

Complex brain networks: From topological communities to clustered

Indian Academy of Sciences (India)

Complex brain networks: From topological communities to clustered dynamics ... Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. ... Pramana – Journal of Physics | News.

Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks.

Science.gov (United States)

Arunraja, Muruganantham; Malathi, Veluchamy; Sakthivel, Erulappan

2015-11-01

Wireless sensor networks are engaged in various data gathering applications. The major bottleneck in wireless data gathering systems is the finite energy of sensor nodes. By conserving the on board energy, the life span of wireless sensor network can be well extended. Data communication being the dominant energy consuming activity of wireless sensor network, data reduction can serve better in conserving the nodal energy. Spatial and temporal correlation among the sensor data is exploited to reduce the data communications. Data similar cluster formation is an effective way to exploit spatial correlation among the neighboring sensors. By sending only a subset of data and estimate the rest using this subset is the contemporary way of exploiting temporal correlation. In Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks, we construct data similar iso-clusters with minimal communication overhead. The intra-cluster communication is reduced using adaptive-normalized least mean squares based dual prediction framework. The cluster head reduces the inter-cluster data payload using a lossless compressive forwarding technique. The proposed work achieves significant data reduction in both the intra-cluster and the inter-cluster communications, with the optimal data accuracy of collected data. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Science.gov (United States)

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Jinsong Gui

2016-09-01

Full Text Available Multi-Input Multi-Output (MIMO can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs, clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO, which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

Science.gov (United States)

Julie, E Golden; Selvi, S Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks.

Science.gov (United States)

Hosen, A S M Sanwar; Cho, Gi Hwan

2018-05-11

Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

Random clustering ferns for multimodal object recognition

OpenAIRE

Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc

2017-01-01

The final publication is available at link.springer.com We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficient...

Evaluation of stability of k-means cluster ensembles with respect to random initialization.

Science.gov (United States)

Kuncheva, Ludmila I; Vetrov, Dmitry P

2006-11-01

Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results.

Measurement Error Correction Formula for Cluster-Level Group Differences in Cluster Randomized and Observational Studies

Science.gov (United States)

Cho, Sun-Joo; Preacher, Kristopher J.

2016-01-01

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

A random network based, node attraction facilitated network evolution method

Directory of Open Access Journals (Sweden)

WenJun Zhang

2016-03-01

Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

An ant colony based resilience approach to cascading failures in cluster supply network

Science.gov (United States)

Wang, Yingcong; Xiao, Renbin

2016-11-01

Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering

Directory of Open Access Journals (Sweden)

Christos Douligeris

2013-01-01

Full Text Available The wide utilization of Wireless Sensor Networks (WSNs is obstructed by the severely limited energy constraints of the individual sensor nodes. This is the reason why a large part of the research in WSNs focuses on the development of energy efficient routing protocols. In this paper, a new protocol called Equalized Cluster Head Election Routing Protocol (ECHERP, which pursues energy conservation through balanced clustering, is proposed. ECHERP models the network as a linear system and, using the Gaussian elimination algorithm, calculates the combinations of nodes that can be chosen as cluster heads in order to extend the network lifetime. The performance evaluation of ECHERP is carried out through simulation tests, which evince the effectiveness of this protocol in terms of network energy efficiency when compared against other well-known protocols.

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

Science.gov (United States)

Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Aboobeker Sidhik Koyamparambil Mammu

2015-04-01

Full Text Available Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs. One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs and a cluster head (CH. The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH and hybrid energy-efficient distributed clustering (HEED.

Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data.

Science.gov (United States)

Wang, Yishu; Zhao, Hongyu; Deng, Minghua; Fang, Huaying; Yang, Dejie

2017-08-24

Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponentialfamily random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g. the density, centrality and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

Directory of Open Access Journals (Sweden)

E. Golden Julie

2016-01-01

Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

Scalability analysis of the synchronizability for ring or chain networks with dense clusters

International Nuclear Information System (INIS)

Lu, Jun-An; Zhang, Yong; Chen, Juan; Lü, Jinhu

2014-01-01

It is well known that most real-world complex networks, such as the Internet and the World Wide Web, are evolving networks. An interesting fundamental question is: how do some important functions or dynamical behaviors of complex networks evolve with increasing network scale? This paper aims at investigating the scalability of the synchronizability for ring or chain networks with dense clusters as the network size increases. We discover some interesting phenomena as follows: (i) the synchronizability of ring or chain networks with clusters decreases with increasing network scale regardless of the inner structures of all communities; (ii) for the same network scale, the network synchronizability decreases more quickly with increasing number of cluster blocks than with increasing number of nodes within the cluster block; (iii) the number of rings or chains has a much more significant influence on the network synchronizability than the size of the rings or chains. Our results indicate that network synchronizability can be maintained with increasing network scale by avoiding ring and chain structures. (paper)

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Science.gov (United States)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Triangular clustering in document networks

Energy Technology Data Exchange (ETDEWEB)

Cheng Xueqi; Ren Fuxin [Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190 (China); Zhou Shi [Department of Computer Science, University College London, Malet Place, London WC1E 6BT (United Kingdom); Hu Maobin [School of Engineering Science, University of Science and Technology of China, Hefei 230026 (China)], E-mail: cxq@ict.ac.cn, E-mail: renfuxin@software.ict.ac.cn, E-mail: s.zhou@adastral.ucl.ac.uk, E-mail: humaobin@ustc.edu.cn

2009-03-15

Document networks have the characteristic that a document node, e.g. a webpage or an article, carries meaningful content. Properties of document networks are not only affected by topological connectivity between nodes, but are also strongly influenced by the semantic relation between the content of the nodes. We observed that document networks have a large number of triangles and a high value clustering coefficient. Also there is a strong correlation between the probability of formation of a triangle and the content similarity among the three nodes involved. We propose the degree-similarity product (DSP) model, which well reproduces these properties. The model achieves this by using a preferential attachment mechanism that favours the linkage between nodes that are both popular and similar. This work is a step forward towards a better understanding of the structure and evolution of document networks.

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

Science.gov (United States)

Goekoop, Rutger; Goekoop, Jaap G

2014-01-01

The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique. In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

Riemannian multi-manifold modeling and clustering in brain networks

Science.gov (United States)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Identifying influential nodes in large-scale directed networks: the role of clustering.

Science.gov (United States)

Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

2013-01-01

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

Identifying influential nodes in large-scale directed networks: the role of clustering.

Directory of Open Access Journals (Sweden)

Duan-Bing Chen

Full Text Available Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Science.gov (United States)

Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

2013-01-01

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

Random walks on generalized Koch networks

International Nuclear Information System (INIS)

Sun, Weigang

2013-01-01

For deterministically growing networks, it is a theoretical challenge to determine the topological properties and dynamical processes. In this paper, we study random walks on generalized Koch networks with features that include an initial state that is a globally connected network to r nodes. In each step, every existing node produces m complete graphs. We then obtain the analytical expressions for first passage time (FPT), average return time (ART), i.e. the average of FPTs for random walks from node i to return to the starting point i for the first time, and average sending time (AST), defined as the average of FPTs from a hub node to all other nodes, excluding the hub itself with regard to network parameters m and r. For this family of Koch networks, the ART of the new emerging nodes is identical and increases with the parameters m or r. In addition, the AST of our networks grows with network size N as N ln N and also increases with parameter m. The results obtained in this paper are the generalizations of random walks for the original Koch network. (paper)

Ferromagnetic clusters induced by a nonmagnetic random disorder in diluted magnetic semiconductors

Energy Technology Data Exchange (ETDEWEB)

Bui, Dinh-Hoi [Institute of Research and Development, Duy Tan University, K7/25 Quang Trung, Danang (Viet Nam); Physics Department, Hue University’s College of Education, 34 Le Loi, Hue (Viet Nam); Phan, Van-Nham, E-mail: phanvannham@dtu.edu.vn [Institute of Research and Development, Duy Tan University, K7/25 Quang Trung, Danang (Viet Nam)

2016-12-15

In this work, we analyze the nonmagnetic random disorder leading to a formation of ferromagnetic clusters in diluted magnetic semiconductors. The nonmagnetic random disorder arises from randomness in the host lattice. Including the disorder to the Kondo lattice model with random distribution of magnetic dopants, the ferromagnetic–paramagnetic transition in the system is investigated in the framework of dynamical mean-field theory. At a certain low temperature one finds a fraction of ferromagnetic sites transiting to the paramagnetic state. Enlarging the nonmagnetic random disorder strength, the paramagnetic regimes expand resulting in the formation of the ferromagnetic clusters.

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-01-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

Science.gov (United States)

Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

1994-07-01

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Balanced Cluster Head Selection Based on Modified k-Means in a Distributed Wireless Sensor Network

OpenAIRE

Periyasamy, Sasikumar; Khara, Sibaram; Thangavelu, Shankar

2016-01-01

A major problem with Wireless Sensor Networks (WSNs) is the maximization of effective network lifetime through minimization of energy usage in the network nodes. A modified k-means (Mk-means) algorithm for clustering was proposed which includes three cluster heads (simultaneously chosen) for each cluster. These cluster heads (CHs) use a load sharing mechanism to rotate as the active cluster head, which conserves residual energy of the nodes, thereby extending network lifetime. Moreover, it re...

A Multilevel Gamma-Clustering Layout Algorithm for Visualization of Biological Networks

Science.gov (United States)

Hruz, Tomas; Lucas, Christoph; Laule, Oliver; Zimmermann, Philip

2013-01-01

Visualization of large complex networks has become an indispensable part of systems biology, where organisms need to be considered as one complex system. The visualization of the corresponding network is challenging due to the size and density of edges. In many cases, the use of standard visualization algorithms can lead to high running times and poorly readable visualizations due to many edge crossings. We suggest an approach that analyzes the structure of the graph first and then generates a new graph which contains specific semantic symbols for regular substructures like dense clusters. We propose a multilevel gamma-clustering layout visualization algorithm (MLGA) which proceeds in three subsequent steps: (i) a multilevel γ-clustering is used to identify the structure of the underlying network, (ii) the network is transformed to a tree, and (iii) finally, the resulting tree which shows the network structure is drawn using a variation of a force-directed algorithm. The algorithm has a potential to visualize very large networks because it uses modern clustering heuristics which are optimized for large graphs. Moreover, most of the edges are removed from the visual representation which allows keeping the overview over complex graphs with dense subgraphs. PMID:23864855

Dense neuron clustering explains connectivity statistics in cortical microcircuits.

Directory of Open Access Journals (Sweden)

Vladimir V Klinshov

Full Text Available Local cortical circuits appear highly non-random, but the underlying connectivity rule remains elusive. Here, we analyze experimental data observed in layer 5 of rat neocortex and suggest a model for connectivity from which emerge essential observed non-random features of both wiring and weighting. These features include lognormal distributions of synaptic connection strength, anatomical clustering, and strong correlations between clustering and connection strength. Our model predicts that cortical microcircuits contain large groups of densely connected neurons which we call clusters. We show that such a cluster contains about one fifth of all excitatory neurons of a circuit which are very densely connected with stronger than average synapses. We demonstrate that such clustering plays an important role in the network dynamics, namely, it creates bistable neural spiking in small cortical circuits. Furthermore, introducing local clustering in large-scale networks leads to the emergence of various patterns of persistent local activity in an ongoing network activity. Thus, our results may bridge a gap between anatomical structure and persistent activity observed during working memory and other cognitive processes.

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

Science.gov (United States)

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Dynamics of cluster structures in a financial market network

Science.gov (United States)

Kocheturov, Anton; Batsyn, Mikhail; Pardalos, Panos M.

2014-11-01

In the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets. In this work we analyze stock markets of the USA and Sweden. We study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets. Such cluster structures are obtained by means of the P-Median Problem (PMP) whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks. Every cluster structure is an undirected disconnected weighted graph in which every connected component (cluster) is a star, or a tree with one central node (called a median) and several leaf nodes connected with the median by weighted edges. Our main observation is that in non-crisis periods of time cluster structures change more chaotically, while during crises they show more stable behavior and fewer changes. Thus an increasing stability of a market graph cluster structure obtained via the PMP could be used as an indicator of a coming crisis.

Energy Efficient and Safe Weighted Clustering Algorithm for Mobile Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Amine Dahane

2015-01-01

Full Text Available The main concern of clustering approaches for mobile wireless sensor networks (WSNs is to prolong the battery life of the individual sensors and the network lifetime. For a successful clustering approach the need of a powerful mechanism to safely elect a cluster head remains a challenging task in many research works that take into account the mobility of the network. The approach based on the computing of the weight of each node in the network is one of the proposed techniques to deal with this problem. In this paper, we propose an energy efficient and safe weighted clustering algorithm (ES-WCA for mobile WSNs using a combination of five metrics. Among these metrics lies the behavioral level metric which promotes a safe choice of a cluster head in the sense where this last one will never be a malicious node. Moreover, the highlight of our work is summarized in a comprehensive strategy for monitoring the network, in order to detect and remove the malicious nodes. We use simulation study to demonstrate the performance of the proposed algorithm.

A neural network clustering algorithm for the ATLAS silicon pixel detector

CERN Document Server

Aad, Georges; Abdallah, Jalal; Abdel Khalek, Samah; Abdinov, Ovsat; Aben, Rosemarie; Abi, Babak; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Agustoni, Marco; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Allbrooke, Benedict; Allison, Lee John; Allport, Phillip; Almond, John; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Alviggi, Mariagrazia; Amako, Katsuya; 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Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Engelmann, Roderich; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Fehling-Kaschek, Mirjam; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Perez, Sonia; Ferrag, Samir; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Florez Bustos, Andres Carlos; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Franchino, Silvia; Francis, David; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; French, Sky; Friedrich, Conrad; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gandrajula, Reddy Pratap; Gao, Jun; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Gemmell, Alistair; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giordano, Raffaele; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Glonti, George; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godfrey, Jennifer; Godlewski, Jan; Goeringer, Christian; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Gozpinar, Serdar; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Grebenyuk, Oleg; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Groth-Jensen, Jacob; Grout, Zara Jane; Guan, Liang; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guicheney, Christophe; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Gunther, Jaroslav; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guttman, Nir; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Hall, David; Halladjian, Garabed; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamilton, Samuel; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Heller, Claudio; Heller, Matthieu; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Hensel, Carsten; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hofmann, Julia Isabell; Hohlfeld, Marc; Holmes, Tova Ray; Hong, Tae Min; Hooft van Huysduynen, Loek; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Hurwitz, Martina; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansen, Hendrik; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jia, Jiangyong; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Erik; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jungst, Ralph Markus; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karastathis, Nikolaos; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasieczka, Gregor; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keil, Markus; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Keung, Justin; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Khodinov, Alexander; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khoroshilov, Andrey; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kittelmann, Thomas; Kiuchi, Kenji; Kladiva, Eduard; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Klok, Peter; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Koll, James; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Kruker, Tobias; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; La Rosa, Alessandro; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laier, Heiko; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Le, Bao Tran; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzen, Georg; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonhardt, Kathrin; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Lester, Christopher Michael; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Lewis, George; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Bo; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loddenkoetter, Thomas; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Lombardo, Vincenzo Paolo; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lungwitz, Matthias; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeno, Mayuko; Maeno, Tadashi; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Mahmoud, Sara; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany Andreina; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marques, Carlos; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Homero; Martinez, Mario; Martin-Haugh, Stewart; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Meric, Nicolas; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Merritt, Hayes; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Mitsui, Shingo; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moraes, Arthur; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Morvaj, Ljiljana; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Thibaut; Mueller, Timo; Muenstermann, Daniel; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagel, Martin; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Nanava, Gizo; Narayan, Rohin; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negri, Guido; Negrini, Matteo; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolics, Katalin; Nikolopoulos, Konstantinos; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Ohshima, Takayoshi; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olchevski, Alexander; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panduro Vazquez, William; Pani, Priscilla; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pearce, James; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; 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Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; Stonjek, Stefan; Stradling, Alden; Straessner, Arno; Stramaglia, Maria Elena; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Stroynowski, Ryszard; Stucci, Stefania Antonia; Stugu, Bjarne; Styles, Nicholas Adam; Su, Dong; Su, Jun; Subramania, Halasya Siva; Subramaniam, Rajivalochan; 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Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Lei; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Lei; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Zinonos, Zinonas; Ziolkowski, Michael; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zutshi, Vishnu; Zwalinski, Lukasz

2014-09-15

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

The Influence of Social Network Characteristics on Peer Clustering in Smoking: A Two-Wave Panel Study of 19- and 23-Year-Old Swedes.

Science.gov (United States)

Miething, Alexander; Rostila, Mikael; Edling, Christofer; Rydgren, Jens

2016-01-01

The present study examines how the composition of social networks and perceived relationship content influence peer clustering in smoking, and how the association changes during the transition from late adolescence to early adulthood. The analysis was based on a Swedish two-wave survey sample comprising ego-centric network data. Respondents were 19 years old in the initial wave, and 23 when the follow-up sample was conducted. 17,227 ego-alter dyads were included in the analyses, which corresponds to an average response rate of 48.7 percent. Random effects logistic regression models were performed to calculate gender-specific average marginal effects of social network characteristics on smoking. The association of egos' and alters' smoking behavior was confirmed and found to be stronger when correlated in the female sample. For females, the associations decreased between age 19 and 23. Interactions between network characteristics and peer clustering in smoking showed that intense social interactions with smokers increase egos' smoking probability. The influence of network structures on peer clustering in smoking decreased during the transition from late adolescence to early adulthood. The study confirmed peer clustering in smoking and revealed that females' smoking behavior in particular is determined by social interactions. Female smokers' propensity to interact with other smokers was found to be associated with the quality of peer relationships, frequent social interactions, and network density. The influence of social networks on peer clustering in smoking decreased during the transition from late adolescence to early adulthood.

An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Sungyoung Lee

2011-12-01

Full Text Available In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO clustering scheme and traditional multihop Single-Input-Single-Output (SISO routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

A Low Energy Intelligent Clustering Protocol for Wireless Sensor Network

DEFF Research Database (Denmark)

Li, Qiao; Cui, Lingguo; Zhang, Baihai

2010-01-01

LEACH (low-energy adaptive clustering hierarchy) is a well-known self-organizing, adaptive clustering protocol of wireless sensor networks. However it has some shortcomings when it faces such problems as the cluster construction and energy management. In this paper, LEICP (low energy intelligent...

A network view on psychiatric disorders: network clusters of symptoms as elementary syndromes of psychopathology.

Directory of Open Access Journals (Sweden)

Rutger Goekoop

Full Text Available INTRODUCTION: The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders. AIM: To examine the ability of network community detection (NCD to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry. METHODS: 192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS. Principal component analysis (PCA was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS. An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS was optimized using a previously published technique. RESULTS: In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "Depression", "Mania", "Anxiety", "Psychosis", "Retardation", and "Behavioral Disorganization". Network metrics were used to quantify the continuities between the elementary syndromes. CONCLUSION: We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a 'Psychopathology Web'. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.

One- and two-cluster synchronized dynamics of non-diffusively coupled Tchebycheff map networks

International Nuclear Information System (INIS)

Schäfer, Mirko; Greiner, Martin

2012-01-01

We use the master stability formalism to discuss one- and two-cluster synchronization of coupled Tchebycheff map networks. For diffusively coupled map systems, the one-cluster synchronized dynamics is given by the behaviour of the individual maps, and the coupling only determines the stability of the coherent state. For the case of non-diffusive coupling and for two-cluster synchronization, the synchronized dynamics on networks is different from the behaviour of the single individual map. Depending on the coupling, we study numerically the characteristics of various forms of the resulting synchronized dynamics. The stability properties of the respective one-cluster synchronized states are discussed for arbitrary network structures. For the case of two-cluster synchronization on bipartite networks we also present analytical expressions for fixed points and zig-zag patterns, and explicitly determine the linear stability of these orbits for the special case of ring-networks.

Generating random networks and graphs

CERN Document Server

Coolen, Ton; Roberts, Ekaterina

2017-01-01

This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...

Computing networks from cluster to cloud computing

CERN Document Server

Vicat-Blanc, Pascale; Guillier, Romaric; Soudan, Sebastien

2013-01-01

"Computing Networks" explores the core of the new distributed computing infrastructures we are using today:  the networking systems of clusters, grids and clouds. It helps network designers and distributed-application developers and users to better understand the technologies, specificities, constraints and benefits of these different infrastructures' communication systems. Cloud Computing will give the possibility for millions of users to process data anytime, anywhere, while being eco-friendly. In order to deliver this emerging traffic in a timely, cost-efficient, energy-efficient, and

Cluster synchronization for directed community networks via pinning partial schemes

International Nuclear Information System (INIS)

Hu Cheng; Jiang Haijun

2012-01-01

Highlights: ► Cluster synchronization for directed community networks is proposed by pinning partial schemes. ► Each community is considered as a whole. ► Several novel pinning criteria are derived based on the information of communities. ► A numerical example with simulation is provided. - Abstract: In this paper, we focus on driving a class of directed networks to achieve cluster synchronization by pinning schemes. The desired cluster synchronization states are no longer decoupled orbits but a set of un-decoupled trajectories. Each community is considered as a whole and the synchronization criteria are derived based on the information of communities. Several pinning schemes including feedback control and adaptive strategy are proposed to select controlled communities by analyzing the information of each community such as indegrees and outdegrees. In all, this paper answers several challenging problems in pinning control of directed community networks: (1) What communities should be chosen as controlled candidates? (2) How many communities are needed to be controlled? (3) How large should the control gains be used in a given community network to achieve cluster synchronization? Finally, an example with numerical simulations is given to demonstrate the effectiveness of the theoretical results.

Robustness and structure of complex networks

Science.gov (United States)

Shao, Shuai

This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

Efficacy of community-based physiotherapy networks for patients with Parkinson's disease: a cluster-randomised trial.

Science.gov (United States)

Munneke, Marten; Nijkrake, Maarten J; Keus, Samyra Hj; Kwakkel, Gert; Berendse, Henk W; Roos, Raymund Ac; Borm, George F; Adang, Eddy M; Overeem, Sebastiaan; Bloem, Bastiaan R

2010-01-01

Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured referrals to these trained physiotherapists to increase the numbers of patients they treat. We aimed to assess the efficacy of this approach for improving health-care outcomes. Between February, 2005, and August, 2007, we did a cluster-randomised trial with 16 clusters (defined as community hospitals and their catchment area). Clusters were randomly allocated by use of a variance minimisation algorithm to ParkinsonNet care (n=8) or usual care (n=8). Patients were assessed at baseline and at 8, 16, and 24 weeks of follow-up. The primary outcome was a patient preference disability score, the patient-specific index score, at 16 weeks. Health secondary outcomes were functional mobility, mobility-related quality of life, and total societal costs over 24 weeks. Analysis was by intention to treat. This trial is registered, number NCT00330694. We included 699 patients. Baseline characteristics of the patients were comparable between the ParkinsonNet clusters (n=358) and usual-care clusters (n=341). The primary endpoint was similar for patients within the ParkinsonNet clusters (mean 47.7, SD 21.9) and control clusters (48.3, 22.4). Health secondary endpoints were also similar for patients in both study groups. Total costs over 24 weeks were lower in ParkinsonNet clusters compared with usual-care clusters (difference euro727; 95% CI 56-1399). Implementation of ParkinsonNet networks did not change health outcomes for patients living in ParkinsonNet clusters. However, health-care costs were reduced in ParkinsonNet clusters compared with usual-care clusters. ZonMw; Netherlands Organisation for Scientific Research; Dutch Parkinson's Disease Society; National Parkinson Foundation; Stichting Robuust

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure.

Science.gov (United States)

Hao, Dapeng; Ren, Cong; Li, Chuanxing

2012-05-01

A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling). Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn't show dependence of degree. Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to "deterministic model" of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Dynamic defense and network randomization for computer systems

Science.gov (United States)

Chavez, Adrian R.; Stout, William M. S.; Hamlet, Jason R.; Lee, Erik James; Martin, Mitchell Tyler

2018-05-29

The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissance stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.

Person mobility in the design and analysis of cluster-randomized cohort prevention trials.

Science.gov (United States)

Vuchinich, Sam; Flay, Brian R; Aber, Lawrence; Bickman, Leonard

2012-06-01

Person mobility is an inescapable fact of life for most cluster-randomized (e.g., schools, hospitals, clinic, cities, state) cohort prevention trials. Mobility rates are an important substantive consideration in estimating the effects of an intervention. In cluster-randomized trials, mobility rates are often correlated with ethnicity, poverty and other variables associated with disparity. This raises the possibility that estimated intervention effects may generalize to only the least mobile segments of a population and, thus, create a threat to external validity. Such mobility can also create threats to the internal validity of conclusions from randomized trials. Researchers must decide how to deal with persons who leave study clusters during a trial (dropouts), persons and clusters that do not comply with an assigned intervention, and persons who enter clusters during a trial (late entrants), in addition to the persons who remain for the duration of a trial (stayers). Statistical techniques alone cannot solve the key issues of internal and external validity raised by the phenomenon of person mobility. This commentary presents a systematic, Campbellian-type analysis of person mobility in cluster-randomized cohort prevention trials. It describes four approaches for dealing with dropouts, late entrants and stayers with respect to data collection, analysis and generalizability. The questions at issue are: 1) From whom should data be collected at each wave of data collection? 2) Which cases should be included in the analyses of an intervention effect? and 3) To what populations can trial results be generalized? The conclusions lead to recommendations for the design and analysis of future cluster-randomized cohort prevention trials.

Topological properties of random wireless networks

Indian Academy of Sciences (India)

Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs find many new applications and are being studied in their own right. In this paper we first provide a brief introduction to the ...

A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

Directory of Open Access Journals (Sweden)

A. Tajari Siahmarzkooh

2016-12-01

Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

A Multi-Hop Clustering Mechanism for Scalable IoT Networks.

Science.gov (United States)

Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong

2018-03-23

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

Directory of Open Access Journals (Sweden)

Ichiro IWASAKI

2010-06-01

Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Muhammad Aslam

2017-01-01

Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.

DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

Science.gov (United States)

Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

2017-05-01

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

A density-based clustering model for community detection in complex networks

Science.gov (United States)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

MHBCDA: Mobility and Heterogeneity aware Bandwidth Efficient Cluster based Data Aggregation for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2013-01-01

Wireless Sensor Network (WSN) offers a variety of novel applications for mobile targets. It generates the large amount of redundant sensing data. The data aggregation techniques are extensively used to reduce the energy consumption and increase the network lifetime, although it has the side effect...... efficient. It exploits correlation of data packets generated by varying the packet generation rate. It prevents transmission of redundant data packets by improving energy consumption by 4.11% and prolongs the network life by 34.45% as compared with state-of-the-art solutions.......-based Data Aggregation (MHBCDA) algorithm for the randomly distributed nodes. It considers the mobile sink based packet aggregation for the heterogeneous WSN. It uses predefined region for the aggregation at cluster head to minimize computation and communication cost. The MHBCDA is energy and bandwidth...

Revisiting the variation of clustering coefficient of biological networks suggests new modular structure

Directory of Open Access Journals (Sweden)

Hao Dapeng

2012-05-01

Full Text Available Abstract Background A central idea in biology is the hierarchical organization of cellular processes. A commonly used method to identify the hierarchical modular organization of network relies on detecting a global signature known as variation of clustering coefficient (so-called modularity scaling. Although several studies have suggested other possible origins of this signature, it is still widely used nowadays to identify hierarchical modularity, especially in the analysis of biological networks. Therefore, a further and systematical investigation of this signature for different types of biological networks is necessary. Results We analyzed a variety of biological networks and found that the commonly used signature of hierarchical modularity is actually the reflection of spoke-like topology, suggesting a different view of network architecture. We proved that the existence of super-hubs is the origin that the clustering coefficient of a node follows a particular scaling law with degree k in metabolic networks. To study the modularity of biological networks, we systematically investigated the relationship between repulsion of hubs and variation of clustering coefficient. We provided direct evidences for repulsion between hubs being the underlying origin of the variation of clustering coefficient, and found that for biological networks having no anti-correlation between hubs, such as gene co-expression network, the clustering coefficient doesn’t show dependence of degree. Conclusions Here we have shown that the variation of clustering coefficient is neither sufficient nor exclusive for a network to be hierarchical. Our results suggest the existence of spoke-like modules as opposed to “deterministic model” of hierarchical modularity, and suggest the need to reconsider the organizational principle of biological hierarchy.

Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

Directory of Open Access Journals (Sweden)

Mazo Ilya

2007-07-01

Full Text Available Abstract Background Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets. Results We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology

A pilot cluster randomized controlled trial of structured goal-setting following stroke.

Science.gov (United States)

Taylor, William J; Brown, Melanie; William, Levack; McPherson, Kathryn M; Reed, Kirk; Dean, Sarah G; Weatherall, Mark

2012-04-01

To determine the feasibility, the cluster design effect and the variance and minimal clinical importance difference in the primary outcome in a pilot study of a structured approach to goal-setting. A cluster randomized controlled trial. Inpatient rehabilitation facilities. People who were admitted to inpatient rehabilitation following stroke who had sufficient cognition to engage in structured goal-setting and complete the primary outcome measure. Structured goal elicitation using the Canadian Occupational Performance Measure. Quality of life at 12 weeks using the Schedule for Individualised Quality of Life (SEIQOL-DW), Functional Independence Measure, Short Form 36 and Patient Perception of Rehabilitation (measuring satisfaction with rehabilitation). Assessors were blinded to the intervention. Four rehabilitation services and 41 patients were randomized. We found high values of the intraclass correlation for the outcome measures (ranging from 0.03 to 0.40) and high variance of the SEIQOL-DW (SD 19.6) in relation to the minimally importance difference of 2.1, leading to impractically large sample size requirements for a cluster randomized design. A cluster randomized design is not a practical means of avoiding contamination effects in studies of inpatient rehabilitation goal-setting. Other techniques for coping with contamination effects are necessary.

Random catalytic reaction networks

Science.gov (United States)

Stadler, Peter F.; Fontana, Walter; Miller, John H.

1993-03-01

We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.

Alignment and integration of complex networks by hypergraph-based spectral clustering

Science.gov (United States)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

Science.gov (United States)

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware

Energy Technology Data Exchange (ETDEWEB)

Tierney, Brian L; Vallentin, Matthias; Sommer, Robin; Lee, Jason; Leres, Craig; Paxson, Vern; Tierney, Brian

2007-09-19

In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.

A simple model of global cascades on random networks

Science.gov (United States)

Watts, Duncan J.

2002-04-01

The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascadesherein called global cascadesthat occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.

Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

OpenAIRE

Srinivas Kanakala; Venugopal Reddy Ananthula; Prashanthi Vempaty

2014-01-01

In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing proto...

A mathematical programming approach for sequential clustering of dynamic networks

Science.gov (United States)

Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

2016-02-01

A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

IMPLEMENTATION OF IMPROVED NETWORK LIFETIME TECHNIQUE FOR WSN USING CLUSTER HEAD ROTATION AND SIMULTANEOUS RECEPTION

Directory of Open Access Journals (Sweden)

Arun Vasanaperumal

2015-11-01

Full Text Available There are number of potential applications of Wireless Sensor Networks (WSNs like wild habitat monitoring, forest fire detection, military surveillance etc. All these applications are constrained for power from a stand along battery power source. So it becomes of paramount importance to conserve the energy utilized from this power source. A lot of efforts have gone into this area recently and it remains as one of the hot research areas. In order to improve network lifetime and reduce average power consumption, this study proposes a novel cluster head selection algorithm. Clustering is the preferred architecture when the numbers of nodes are larger because it results in considerable power savings for large networks as compared to other ones like tree or star. Since majority of the applications generally involve more than 30 nodes, clustering has gained widespread importance and is most used network architecture. The optimum number of clusters is first selected based on the number of nodes in the network. When the network is in operation the cluster heads in a cluster are rotated periodically based on the proposed cluster head selection algorithm to increase the network lifetime. Throughout the network single-hop communication methodology is assumed. This work will serve as an encouragement for further advances in the low power techniques for implementing Wireless Sensor Networks (WSNs.

Reliability analysis of cluster-based ad-hoc networks

International Nuclear Information System (INIS)

Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

2008-01-01

The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks

A random spatial network model based on elementary postulates

Science.gov (United States)

Karlinger, Michael R.; Troutman, Brent M.

1989-01-01

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

On the limiting characteristics of quantum random number generators at various clusterings of photocounts

Science.gov (United States)

Molotkov, S. N.

2017-03-01

Various methods for the clustering of photocounts constituting a sequence of random numbers are considered. It is shown that the clustering of photocounts resulting in the Fermi-Dirac distribution makes it possible to achieve the theoretical limit of the random number generation rate.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

Directory of Open Access Journals (Sweden)

C. Vimalarani

2016-01-01

Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Teng Gao

2015-01-01

Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.

Relative efficiency and sample size for cluster randomized trials with variable cluster sizes.

Science.gov (United States)

You, Zhiying; Williams, O Dale; Aban, Inmaculada; Kabagambe, Edmond Kato; Tiwari, Hemant K; Cutter, Gary

2011-02-01

The statistical power of cluster randomized trials depends on two sample size components, the number of clusters per group and the numbers of individuals within clusters (cluster size). Variable cluster sizes are common and this variation alone may have significant impact on study power. Previous approaches have taken this into account by either adjusting total sample size using a designated design effect or adjusting the number of clusters according to an assessment of the relative efficiency of unequal versus equal cluster sizes. This article defines a relative efficiency of unequal versus equal cluster sizes using noncentrality parameters, investigates properties of this measure, and proposes an approach for adjusting the required sample size accordingly. We focus on comparing two groups with normally distributed outcomes using t-test, and use the noncentrality parameter to define the relative efficiency of unequal versus equal cluster sizes and show that statistical power depends only on this parameter for a given number of clusters. We calculate the sample size required for an unequal cluster sizes trial to have the same power as one with equal cluster sizes. Relative efficiency based on the noncentrality parameter is straightforward to calculate and easy to interpret. It connects the required mean cluster size directly to the required sample size with equal cluster sizes. Consequently, our approach first determines the sample size requirements with equal cluster sizes for a pre-specified study power and then calculates the required mean cluster size while keeping the number of clusters unchanged. Our approach allows adjustment in mean cluster size alone or simultaneous adjustment in mean cluster size and number of clusters, and is a flexible alternative to and a useful complement to existing methods. Comparison indicated that we have defined a relative efficiency that is greater than the relative efficiency in the literature under some conditions. Our measure

Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey

Directory of Open Access Journals (Sweden)

Bilal Jan

2017-01-01

Full Text Available Wireless sensor networks (WSN are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Rack Aware Data Placement for Network Consumption in Erasure-Coded Clustered Storage Systems

Directory of Open Access Journals (Sweden)

Bilin Shao

2018-06-01

Full Text Available The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency.

Spiking neural networks on high performance computer clusters

Science.gov (United States)

Chen, Chong; Taha, Tarek M.

2011-09-01

In this paper we examine the acceleration of two spiking neural network models on three clusters of multicore processors representing three categories of processors: x86, STI Cell, and NVIDIA GPGPUs. The x86 cluster utilized consists of 352 dualcore AMD Opterons, the Cell cluster consists of 320 Sony Playstation 3s, while the GPGPU cluster contains 32 NVIDIA Tesla S1070 systems. The results indicate that the GPGPU platform can dominate in performance compared to the Cell and x86 platforms examined. From a cost perspective, the GPGPU is more expensive in terms of neuron/s throughput. If the cost of GPGPUs go down in the future, this platform will become very cost effective for these models.

Universal Prevention for Anxiety and Depressive Symptoms in Children: A Meta-analysis of Randomized and Cluster-Randomized Trials.

Science.gov (United States)

Ahlen, Johan; Lenhard, Fabian; Ghaderi, Ata

2015-12-01

Although under-diagnosed, anxiety and depression are among the most prevalent psychiatric disorders in children and adolescents, leading to severe impairment, increased risk of future psychiatric problems, and a high economic burden to society. Universal prevention may be a potent way to address these widespread problems. There are several benefits to universal relative to targeted interventions because there is limited knowledge as to how to screen for anxiety and depression in the general population. Earlier meta-analyses of the prevention of depression and anxiety symptoms among children suffer from methodological inadequacies such as combining universal, selective, and indicated interventions in the same analyses, and comparing cluster-randomized trials with randomized trials without any correction for clustering effects. The present meta-analysis attempted to determine the effectiveness of universal interventions to prevent anxiety and depressive symptoms after correcting for clustering effects. A systematic search of randomized studies in PsychINFO, Cochrane Library, and Google Scholar resulted in 30 eligible studies meeting inclusion criteria, namely peer-reviewed, randomized or cluster-randomized trials of universal interventions for anxiety and depressive symptoms in school-aged children. Sixty-three percent of the studies reported outcome data regarding anxiety and 87 % reported outcome data regarding depression. Seventy percent of the studies used randomization at the cluster level. There were small but significant effects regarding anxiety (.13) and depressive (.11) symptoms as measured at immediate posttest. At follow-up, which ranged from 3 to 48 months, effects were significantly larger than zero regarding depressive (.07) but not anxiety (.11) symptoms. There was no significant moderation effect of the following pre-selected variables: the primary aim of the intervention (anxiety or depression), deliverer of the intervention, gender distribution

Alternative Path Communication in Wide-Scale Cluster-Tree Wireless Sensor Networks Using Inactive Periods.

Science.gov (United States)

Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

2017-05-06

The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable technology to deploy wide-scale Wireless Sensor Networks (WSNs). These networks are usually designed to support convergecast traffic, where all communication paths go through the PAN (Personal Area Network) coordinator. Nevertheless, peer-to-peer communication relationships may be also required for different types of WSN applications. That is the typical case of sensor and actuator networks, where local control loops must be closed using a reduced number of communication hops. The use of communication schemes optimised just for the support of convergecast traffic may result in higher network congestion and in a potentially higher number of communication hops. Within this context, this paper proposes an Alternative-Route Definition (ARounD) communication scheme for WSNs. The underlying idea of ARounD is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths. These alternative paths consider shorter inter-cluster paths, using a set of intermediate nodes to relay messages during their inactive periods in the cluster-tree network. Simulation results show that the ARounD communication scheme can significantly decrease the end-to-end communication delay, when compared to the use of standard cluster-tree communication schemes. Moreover, the ARounD communication scheme is able to reduce the network congestion around the PAN coordinator, enabling the reduction of the number of message drops due to queue overflows in the cluster-tree network.

A Latent Variable Clustering Method for Wireless Sensor Networks

DEFF Research Database (Denmark)

Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

2016-01-01

In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

Cross over of recurrence networks to random graphs and random ...

Indian Academy of Sciences (India)

2017-01-27

Jan 27, 2017 ... that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to .... municative [19] or social [20], deviate from the random ..... He has shown that the spatial effects become.

Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Peng; QI Wen-Juan; DENG Zi-Li

2014-01-01

This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

Science.gov (United States)

Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

2000-01-01

Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

Information Filtering via Clustering Coefficients of User-Object Bipartite Networks

Science.gov (United States)

Guo, Qiang; Leng, Rui; Shi, Kerui; Liu, Jian-Guo

The clustering coefficient of user-object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user-object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user-object bipartite networks should be investigated to estimate users' tastes.

A semi-supervised method to detect seismic random noise with fuzzy GK clustering

International Nuclear Information System (INIS)

Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

2008-01-01

We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

Mathematical model on malicious attacks in a mobile wireless network with clustering

International Nuclear Information System (INIS)

Haldar, Kaushik; Mishra, Bimal Kumar

2015-01-01

A mathematical model has been formulated for the analysis of a wireless epidemic on a clustered heterogeneous network. The model introduces mobility into the epidemic framework assuming that the component nodes have a tendency to be attached with a frequently visited home cluster. This underlines the inherent regularity in the mobility pattern of mobile nodes in a wireless network. The analysis focuses primarily on features that arise because of the mobility considerations compared in the larger scenario formed by the epidemic aspects. A result on the invariance of the home cluster populations with respect to time provides an important view-point of the long-term behavior of the system. The analysis also focuses on obtaining a basic threshold condition that guides the epidemic behavior of the system. Analytical as well as numerical results have also been obtained to establish the asymptotic behavior of the connected components of the network, and that of the whole network when the underlying graph turns out to be irreducible. Applications to proximity based attacks and to scenarios with high cluster density have also been outlined

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment.

Science.gov (United States)

Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong

2015-12-10

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network's running and the degree of candidate nodes' effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime.

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Science.gov (United States)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers

Damage Spreading in Spatial and Small-world Random Boolean Networks

Energy Technology Data Exchange (ETDEWEB)

Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.

2014-02-18

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks.

Science.gov (United States)

Yang, Liu; Lu, Yinzhi; Xiong, Lian; Tao, Yang; Zhong, Yuanchang

2017-11-17

Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

Evaluation of cluster-randomized trials on maternal and child health research in developing countries

DEFF Research Database (Denmark)

Handlos, Line Neerup; Chakraborty, Hrishikesh; Sen, Pranab Kumar

2009-01-01

To summarize and evaluate all publications including cluster-randomized trials used for maternal and child health research in developing countries during the last 10 years. METHODS: All cluster-randomized trials published between 1998 and 2008 were reviewed, and those that met our criteria...... for inclusion were evaluated further. The criteria for inclusion were that the trial should have been conducted in maternal and child health care in a developing country and that the conclusions should have been made on an individual level. Methods of accounting for clustering in design and analysis were......, and the trials generally improved in quality. CONCLUSIONS: Shortcomings exist in the sample-size calculations and in the analysis of cluster-randomized trials conducted during maternal and child health research in developing countries. Even though there has been improvement over time, further progress in the way...

Robustness of the Artificial Neural Networks Used for Clustering in the ATLAS Pixel Detector

CERN Document Server

The ATLAS collaboration

2015-01-01

A study of the robustness of the ATLAS pixel neural network clustering algorithm is presented. The sensitivity to variations to its input is evaluated. These variations are motivated by potential discrepancies between data and simulation due to uncertainties in the modelling of pixel clusters in simulation, as well as uncertainties from the detector calibration. Within reasonable variation magnitudes, the neural networks prove to be robust to most variations. The neural network used to identify pixel clusters created by multiple charged particles, is most sensitive to variations affecting the total amount of charge collected in the cluster. Modifying the read-out threshold has the biggest effect on the clustering's ability to estimate the position of the particle's intersection with the detector.

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

Science.gov (United States)

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

Energy Efficient Clustering Based Network Protocol Stack for 3D Airborne Monitoring System

Directory of Open Access Journals (Sweden)

Abhishek Joshi

2017-01-01

Full Text Available Wireless Sensor Network consists of large number of nodes densely deployed in ad hoc manner. Usually, most of the application areas of WSNs require two-dimensional (2D topology. Various emerging application areas such as airborne networks and underwater wireless sensor networks are usually deployed using three-dimensional (3D network topology. In this paper, a static 3D cluster-based network topology has been proposed for airborne networks. A network protocol stack consisting of various protocols such as TDMA MAC and dynamic routing along with services such as time synchronization, Cluster Head rotation, and power level management has been proposed for this airborne network. The proposed protocol stack has been implemented on the hardware platform consisting of number of TelosB nodes. This 3D airborne network architecture can be used to measure Air Quality Index (AQI in an area. Various parameters of network such as energy consumption, Cluster Head rotation, time synchronization, and Packet Delivery Ratio (PDR have been analyzed. Detailed description of the implementation of the protocol stack along with results of implementation has been provided in this paper.

Impact of an automated email notification system for results of tests pending at discharge: a cluster-randomized controlled trial.

Science.gov (United States)

Dalal, Anuj K; Roy, Christopher L; Poon, Eric G; Williams, Deborah H; Nolido, Nyryan; Yoon, Cathy; Budris, Jonas; Gandhi, Tejal; Bates, David W; Schnipper, Jeffrey L

2014-01-01

Physician awareness of the results of tests pending at discharge (TPADs) is poor. We developed an automated system that notifies responsible physicians of TPAD results via secure, network email. We sought to evaluate the impact of this system on self-reported awareness of TPAD results by responsible physicians, a necessary intermediary step to improve management of TPAD results. We conducted a cluster-randomized controlled trial at a major hospital affiliated with an integrated healthcare delivery network in Boston, Massachusetts. Adult patients with TPADs who were discharged from inpatient general medicine and cardiology services were assigned to the intervention or usual care arm if their inpatient attending physician and primary care physician (PCP) were both randomized to the same study arm. Patients of physicians randomized to discordant study arms were excluded. We surveyed these physicians 72 h after all TPAD results were finalized. The primary outcome was awareness of TPAD results by attending physicians. Secondary outcomes included awareness of TPAD results by PCPs, awareness of actionable TPAD results, and provider satisfaction. We analyzed data on 441 patients. We sent 441 surveys to attending physicians and 353 surveys to PCPs and received 275 and 152 responses from 83 different attending physicians and 112 different PCPs, respectively (attending physician survey response rate of 63%). Intervention attending physicians and PCPs were significantly more aware of TPAD results (76% vs 38%, adjusted/clustered OR 6.30 (95% CI 3.02 to 13.16), pemail notification represents a promising strategy for managing TPAD results, potentially mitigating an unresolved patient safety concern. ClinicalTrials.gov (NCT01153451).

SURVEI TEKNIK CLUSTERING ROUTING BERDASARKAN MOBILITAS PADA WIRELESS AD-HOC NETWORK

Directory of Open Access Journals (Sweden)

I Nyoman Trisna Wirawan

2016-01-01

Full Text Available Wireless ad-hoc merupakan sebuah skema jaringan yang didesain supaya mampu beroperasi tanpa membutuhkan infrastruktur tetap serta bersifat otonom. Teknik flooding pada proses path discovery dalam kasus wireless ad-hoc network dapat menimbulkan masalah beban jaringan yang berlebihan. Oleh karena itu, sebuah skema clustering diusulkan untuk mengurangi adanya flooding paket yang berlebihan dengan membagi node-node dalam jaringan menjadi beberapa bagian berdasarkan parameter tertentu. Teknik ini efektifuntuk mengurangi paket yang harus dilewatkan dalam jaringan. Namun masalah muncul ketika sebuah jaringan wireless ad-hoc harus membentuk sebuah cluster dengan mempertimbangkan beberapa parameter khusus. Parameter tersebut harus disesuaikan dengan kasus yang dihadapi. Pada tulisan ini akan dibahas secara khusus mengenai penerapan skema clustering dalam lingkungan wireless ad-hoc network, baik pada MANETdan penyesuaian skema clustering yang harus dilakukan pada VANET berdasarkan mobilitasnya.

Vijana Vijiweni II: a cluster-randomized trial to evaluate the efficacy of a microfinance and peer health leadership intervention for HIV and intimate partner violence prevention among social networks of young men in Dar es Salaam.

Science.gov (United States)

Kajula, Lusajo; Balvanz, Peter; Kilonzo, Mrema Noel; Mwikoko, Gema; Yamanis, Thespina; Mulawa, Marta; Kajuna, Deus; Hill, Lauren; Conserve, Donaldson; Reyes, Heathe Luz McNaughton; Leatherman, Sheila; Singh, Basant; Maman, Suzanne

2016-02-03

Intimate partner violence (IPV) and sexually transmitted infections (STIs), including HIV, remain important public health problems with devastating health effects for men and women in sub-Saharan Africa. There have been calls to engage men in prevention efforts, however, we lack effective approaches to reach and engage them. Social network approaches have demonstrated effective and sustained outcomes on changing risk behaviors in the U.S. Our team has identified and engaged naturally occurring social networks comprised mostly of young men in Dar es Salaam in an intervention designed to jointly reduce STI incidence and the perpetration of IPV. These stable networks are locally referred to as "camps." In a pilot study we demonstrated the feasibility and acceptability of a combined microfinance and peer health leadership intervention within these camp-based peer networks. We are implementing a cluster-randomized trial to evaluate the efficacy of an intervention combining microfinance with health leadership training in 60 camps in Dar es Salaam, Tanzania. Half of the camps have been randomized to the intervention arm, and half to a control arm. The camps in the intervention arm will receive a combined microfinance and health leadership intervention for a period of two years. The camps in the control arm will receive a delayed intervention. We have enrolled 1,258 men across the 60 study camps. Behavioral surveys will be conducted at baseline, 12-months post intervention launch and 30-month post intervention launch and biological samples will be drawn to test for Neisseria gonorrhea (NG), Chlamydia trachomatis (CT), and Trichomonas vaginalis (TV) at baseline and 30-months. The primary endpoints for assessing intervention impact are IPV perpetration and STI incidence. This is the first cluster-randomized trial targeting social networks of men in sub-Saharan Africa that jointly addresses HIV and IPV perpetration and has both biological and behavioral endpoints. Effective

A Scheduling Algorithm for Minimizing the Packet Error Probability in Clusterized TDMA Networks

Directory of Open Access Journals (Sweden)

Arash T. Toyserkani

2009-01-01

Full Text Available We consider clustered wireless networks, where transceivers in a cluster use a time-slotted mechanism (TDMA to access a wireless channel that is shared among several clusters. An approximate expression for the packet-loss probability is derived for networks with one or more mutually interfering clusters in Rayleigh fading environments, and the approximation is shown to be good for relevant scenarios. We then present a scheduling algorithm, based on Lagrangian duality, that exploits the derived packet-loss model in an attempt to minimize the average packet-loss probability in the network. Computer simulations of the proposed scheduling algorithm show that a significant increase in network throughput can be achieved compared to uncoordinated scheduling. Empirical trials also indicate that the proposed optimization algorithm almost always converges to an optimal schedule with a reasonable number of iterations. Thus, the proposed algorithm can also be used for bench-marking suboptimal scheduling algorithms.

Clustering of Mobile Ad Hoc Networks: An Adaptive Broadcast Period Approach

OpenAIRE

Gavalas, Damianos; Pantziou, Grammati; Konstantopoulos, Charalampos; Mamalis, Basilis

2011-01-01

Organization, scalability and routing have been identified as key problems hindering viability and commercial success of mobile ad hoc networks. Clustering of mobile nodes among separate domains has been proposed as an efficient approach to address those issues. In this work, we introduce an efficient distributed clustering algorithm that uses both location and energy metrics for cluster formation. Our proposed solution mainly addresses cluster stability, manageability and energy efficiency i...

Mobility-aware Hybrid Synchronization for Wireless Sensor Network

DEFF Research Database (Denmark)

Dnyaneshwar, Mantri; Prasad, Neeli R.; Prasad, Ramjee

2015-01-01

Random mobility of node causes the frequent changes in the network dynamics causing the increased cost in terms of energy and bandwidth. It needs the additional efforts to synchronize the activities of nodes during data collection and transmission in Wireless Sensor Networks (WSNs). A key challenge...... in maintaining the effective data collection and transmission is to schedule and synchronize the activities of the nodes with the global clock. This paper proposes the Mobility-aware Hybrid Synchronization Algorithm (MHS) which works on the formation of cluster based on spanning tree mechanism (SPT). Nodes used...... for formation of the network have random mobility and heterogeneous in terms of energy with static sink. The nodes in the cluster and cluster heads in the network are synchronized with the notion of global time scale. In the initial stage, the algorithm establishes the hierarchical structure of the network...

A DISTRIBUTED ENERGY EFFICIENT CLUSTERING ALGORITHM FOR DATA AGGREGATION IN WIRELESS SENSOR NETWORKS

Directory of Open Access Journals (Sweden)

Seyed Mohammad Bagher Musavi Shirazi

2018-06-01

Full Text Available Wireless sensor networks (WSNs are a new generation of networks typically consisting of a large number of inexpensive nodes with wireless communications. The main purpose of these networks is to collect information from the environment for further processing. Nodes in the network have been equipped with limited battery lifetime, so energy saving is one of the major issues in WSNs. If we balance the load among cluster heads and prevent having an extra load on just a few nodes in the network, we can reach longer network lifetime. One solution to control energy consumption and balance the load among nodes is to use clustering techniques. In this paper, we propose a new distributed energy-efficient clustering algorithm for data aggregation in wireless sensor networks, called Distributed Clustering for Data Aggregation (DCDA. In our new approach, an optimal transmission tree is constructed among sensor nodes with a new greedy method. Base station (BS is the root, cluster heads (CHs and relay nodes are intermediate nodes, and other nodes (cluster member nodes are the leaves of this transmission tree. DCDA balances load among CHs in intra-cluster and inter-cluster data communications using different cluster sizes. For efficient inter-cluster communications, some relay nodes will transfer data between CHs. Energy consumption, distance to the base station, and cluster heads’ centric metric are three main adjustment parameters for the cluster heads election. Simulation results show that the proposed protocol leads to the reduction of individual sensor nodes’ energy consumption and prolongs network lifetime, in comparison with other known methods. ABSTRAK: Rangkaian sensor wayarles (WSN adalah rangkaian generasi baru yang terdiri daripada nod-nod murah komunikasi wayarles. Tujuan rangkaian-rangkaian ini adalah bagi mengumpul maklumat sekeliling untuk proses seterusnya. Nod dalam rangkaian ini dilengkapi bateri kurang jangka hayat, jadi simpanan tenaga

Selection bias and subject refusal in a cluster-randomized controlled trial

Directory of Open Access Journals (Sweden)

Rochelle Yang

2017-07-01

Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

Performance Analysis of a Cluster-Based MAC Protocol for Wireless Ad Hoc Networks

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Jesús Alonso-Zárate

2010-01-01

Full Text Available An analytical model to evaluate the non-saturated performance of the Distributed Queuing Medium Access Control Protocol for Ad Hoc Networks (DQMANs in single-hop networks is presented in this paper. DQMAN is comprised of a spontaneous, temporary, and dynamic clustering mechanism integrated with a near-optimum distributed queuing Medium Access Control (MAC protocol. Clustering is executed in a distributed manner using a mechanism inspired by the Distributed Coordination Function (DCF of the IEEE 802.11. Once a station seizes the channel, it becomes the temporary clusterhead of a spontaneous cluster and it coordinates the peer-to-peer communications between the clustermembers. Within each cluster, a near-optimum distributed queuing MAC protocol is executed. The theoretical performance analysis of DQMAN in single-hop networks under non-saturation conditions is presented in this paper. The approach integrates the analysis of the clustering mechanism into the MAC layer model. Up to the knowledge of the authors, this approach is novel in the literature. In addition, the performance of an ad hoc network using DQMAN is compared to that obtained when using the DCF of the IEEE 802.11, as a benchmark reference.

Network based approaches reveal clustering in protein point patterns

Science.gov (United States)

Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

2014-03-01

Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

A combined community- and facility-based approach to improve pregnancy outcomes in low-resource settings: a Global Network cluster randomized trial.

Science.gov (United States)

Pasha, Omrana; McClure, Elizabeth M; Wright, Linda L; Saleem, Sarah; Goudar, Shivaprasad S; Chomba, Elwyn; Patel, Archana; Esamai, Fabian; Garces, Ana; Althabe, Fernando; Kodkany, Bhala; Mabeya, Hillary; Manasyan, Albert; Carlo, Waldemar A; Derman, Richard J; Hibberd, Patricia L; Liechty, Edward K; Krebs, Nancy; Hambidge, K Michael; Buekens, Pierre; Moore, Janet; Jobe, Alan H; Koso-Thomas, Marion; Wallace, Dennis D; Stalls, Suzanne; Goldenberg, Robert L

2013-10-03

Fetal and neonatal mortality rates in low-income countries are at least 10-fold greater than in high-income countries. These differences have been related to poor access to and poor quality of obstetric and neonatal care. This trial tested the hypothesis that teams of health care providers, administrators and local residents can address the problem of limited access to quality obstetric and neonatal care and lead to a reduction in perinatal mortality in intervention compared to control locations. In seven geographic areas in five low-income and one middle-income country, most with high perinatal mortality rates and substantial numbers of home deliveries, we performed a cluster randomized non-masked trial of a package of interventions that included community mobilization focusing on birth planning and hospital transport, community birth attendant training in problem recognition, and facility staff training in the management of obstetric and neonatal emergencies. The primary outcome was perinatal mortality at ≥28 weeks gestation or birth weight ≥1000 g. Despite extensive effort in all sites in each of the three intervention areas, no differences emerged in the primary or any secondary outcome between the intervention and control clusters. In both groups, the mean perinatal mortality was 40.1/1,000 births (P = 0.9996). Neither were there differences between the two groups in outcomes in the last six months of the project, in the year following intervention cessation, nor in the clusters that best implemented the intervention. This cluster randomized comprehensive, large-scale, multi-sector intervention did not result in detectable impact on the proposed outcomes. While this does not negate the importance of these interventions, we expect that achieving improvement in pregnancy outcomes in these settings will require substantially more obstetric and neonatal care infrastructure than was available at the sites during this trial, and without them provider training

Enhancement of Adaptive Cluster Hierarchical Routing Protocol using Distance and Energy for Wireless Sensor Networks

International Nuclear Information System (INIS)

Nawar, N.M.; Soliman, S.E.; Kelash, H.M.; Ayad, N.M.

2014-01-01

The application of wireless networking is widely used in nuclear applications. This includes reactor control and fire dedication system. This paper is devoted to the application of this concept in the intrusion system of the Radioisotope Production Facility (RPF) of the Egyptian Atomic Energy Authority. This includes the tracking, monitoring and control components of this system. The design and implementation of wireless sensor networks has become a hot area of research due to the extensive use of sensor networks to enable applications that connect the physical world to the virtual world [1-2]. The original LEACH is named a communication protocol (clustering-based); the extended LEACH’s stochastic cluster head selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime can be accomplished [3]. The proposed routing mechanisms after enhancement divide the nodes into clusters. A cluster head performs its task which is considerably more energy-intensive than the rest of the nodes inside sensor network. So, nodes rotate tasks at different rounds between a cluster head and other sensors throughout the lifetime of the network to balance the energy dissipation [4-5].The performance improvement when using routing protocol after enhancement of the algorithm which takes into consideration the distance and the remaining energy for choosing the cluster head by obtains from the advertise message. Network Simulator (Ns2 simulator) is used to prove that LEACH after enhancement performs better than the original LEACH protocol in terms of Average Energy, Network Life Time, Delay, Throughput and Overhead.

Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Multi-agent coordination in directed moving neighbourhood random networks

International Nuclear Information System (INIS)

Yi-Lun, Shang

2010-01-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results. (general)

Softening in Random Networks of Non-Identical Beams.

Science.gov (United States)

Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R

2016-02-01

Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

Science.gov (United States)

Shen, Lili; Guo, Jiming; Wang, Lei

2018-06-06

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

Non-parametric co-clustering of large scale sparse bipartite networks on the GPU

DEFF Research Database (Denmark)

Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai

2011-01-01

of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale...... sparse bipartite networks and achieve a speedup of two orders of magnitude compared to estimation based on conventional CPUs. In terms of scalability we find for networks with more than 100 million links that reliable inference can be achieved in less than an hour on a single GPU. To efficiently manage...

Quenched Large Deviations for Simple Random Walks on Percolation Clusters Including Long-Range Correlations

Science.gov (United States)

Berger, Noam; Mukherjee, Chiranjib; Okamura, Kazuki

2018-03-01

We prove a quenched large deviation principle (LDP) for a simple random walk on a supercritical percolation cluster (SRWPC) on {Z^d} ({d ≥ 2}). The models under interest include classical Bernoulli bond and site percolation as well as models that exhibit long range correlations, like the random cluster model, the random interlacement and the vacant set of random interlacements (for {d ≥ 3}) and the level sets of the Gaussian free field ({d≥ 3}). Inspired by the methods developed by Kosygina et al. (Commun Pure Appl Math 59:1489-1521, 2006) for proving quenched LDP for elliptic diffusions with a random drift, and by Yilmaz (Commun Pure Appl Math 62(8):1033-1075, 2009) and Rosenbluth (Quenched large deviations for multidimensional random walks in a random environment: a variational formula. Ph.D. thesis, NYU, arXiv:0804.1444v1) for similar results regarding elliptic random walks in random environment, we take the point of view of the moving particle and prove a large deviation principle for the quenched distribution of the pair empirical measures of the environment Markov chain in the non-elliptic case of SRWPC. Via a contraction principle, this reduces easily to a quenched LDP for the distribution of the mean velocity of the random walk and both rate functions admit explicit variational formulas. The main difficulty in our set up lies in the inherent non-ellipticity as well as the lack of translation-invariance stemming from conditioning on the fact that the origin belongs to the infinite cluster. We develop a unifying approach for proving quenched large deviations for SRWPC based on exploiting coercivity properties of the relative entropies in the context of convex variational analysis, combined with input from ergodic theory and invoking geometric properties of the supercritical percolation cluster.

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

Directory of Open Access Journals (Sweden)

Taegwon Jeong

2011-05-01

Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

Science.gov (United States)

Lee, Chongdeuk; Jeong, Taegwon

2011-01-01

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

Designing neural networks that process mean values of random variables

International Nuclear Information System (INIS)

Barber, Michael J.; Clark, John W.

2014-01-01

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

Designing neural networks that process mean values of random variables

Energy Technology Data Exchange (ETDEWEB)

Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

2014-06-13

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding

OpenAIRE

Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank

2014-01-01

This work proposes a new protocol applying on–the–fly random linear network coding in wireless mesh net-works. The protocol provides increased reliability, low delay,and high throughput to the upper layers, while being obliviousto their specific requirements. This seemingly conflicting goalsare achieved by design, using an on–the–fly network codingstrategy. Our protocol also exploits relay nodes to increasethe overall performance of individual links. Since our protocolnaturally masks random p...

Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

NARCIS (Netherlands)

S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

2000-01-01

textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

Random matrix approach to plasmon resonances in the random impedance network model of disordered nanocomposites

Science.gov (United States)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric and other two-component nanocomposites. In the present work, the spectral properties of resonances in random networks are studied within the framework of the random matrix theory. We have shown that the appropriate ensemble of random matrices for the considered problem is the Jacobi ensemble (the MANOVA ensemble). The obtained analytical expressions for the density of states in such resonant networks show a good agreement with the results of numerical simulations in a wide range of metal filling fractions 0

A Local Search Algorithm for Clustering in Software as a Service Networks

NARCIS (Netherlands)

J.P. van der Gaast (Jelmer); C.A. Rietveld (Niels); A.F. Gabor (Adriana); Y. Zhang (Yingqian)

2011-01-01

textabstractIn this paper we present and analyze a model for clustering in networks that offer Software as a Service (SaaS). In this problem, organizations requesting a set of applications have to be assigned to clusters such that the costs of opening clusters and installing the necessary

Weighted Scaling in Non-growth Random Networks

International Nuclear Information System (INIS)

Chen Guang; Yang Xuhua; Xu Xinli

2012-01-01

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

Science.gov (United States)

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Transmission Range Assignment with Balancing Connectivity in Clustered Wireless Networks

OpenAIRE

Hussein, Abd Ali

2014-01-01

Currently, the main challenge for researchers in the field of wireless sensor networks is associated with reducing the energy consumption as much as possible to increase the lifetime of the nodes and improve the performance of the network. Furthermore, delivery of data to its destination is also an important key issue that represents throughput of the network. On the other hand, transmission range assignment in clustered wireless networks is the bottleneck of the balance between energy con...

Experimental observation of chimera and cluster states in a minimal globally coupled network

Energy Technology Data Exchange (ETDEWEB)

Hart, Joseph D. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Bansal, Kanika [Department of Mathematics, University at Buffalo, SUNY Buffalo, New York 14260 (United States); US Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005 (United States); Murphy, Thomas E. [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20742 (United States); Roy, Rajarshi [Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20742 (United States); Department of Physics, University of Maryland, College Park, Maryland 20742 (United States); Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742 (United States)

2016-09-15

A “chimera state” is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Science.gov (United States)

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

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Dao-Wei Bi

2007-07-01

Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

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Lili Shen

2018-06-01

Full Text Available The network real-time kinematic (RTK technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI, and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs, robotic equipment, etc. require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

Impacts of clustering on interacting epidemics.

Science.gov (United States)

Wang, Bing; Cao, Lang; Suzuki, Hideyuki; Aihara, Kazuyuki

2012-07-07

Since community structures in real networks play a major role for the epidemic spread, we therefore explore two interacting diseases spreading in networks with community structures. As a network model with community structures, we propose a random clique network model composed of different orders of cliques. We further assume that each disease spreads only through one type of cliques; this assumption corresponds to the issue that two diseases spread inside communities and outside them. Considering the relationship between the susceptible-infected-recovered (SIR) model and the bond percolation theory, we apply this theory to clique random networks under the assumption that the occupation probability is clique-type dependent, which is consistent with the observation that infection rates inside a community and outside it are different, and obtain a number of statistical properties for this model. Two interacting diseases that compete the same hosts are also investigated, which leads to a natural generalization of analyzing an arbitrary number of infectious diseases. For two-disease dynamics, the clustering effect is hypersensitive to the cohesiveness and concentration of cliques; this illustrates the impacts of clustering and the composition of subgraphs in networks on epidemic behavior. The analysis of coexistence/bistability regions provides significant insight into the relationship between the network structure and the potential epidemic prevalence. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis.

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Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

2017-09-27

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Robustness of the ATLAS pixel clustering neural network algorithm

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration

2016-01-01

Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In the ATLAS track reconstruction algorithm, an artificial neural network is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.

The covariance matrix of the Potts model: A random cluster analysis

International Nuclear Information System (INIS)

Borgs, C.; Chayes, J.T.

1996-01-01

We consider the covariance matrix, G mn = q 2 x ,m); δ(σ y ,n)>, of the d-dimensional q-states Potts model, rewriting it in the random cluster representation of Fortuin and Kasteleyn. In many of the q ordered phases, we identify the eigenvalues of this matrix both in terms of representations of the unbroken symmetry group of the model and in terms of random cluster connectivities and covariances, thereby attributing algebraic significance to these stochastic geometric quantities. We also show that the correlation length and the correlation length corresponding to the decay rate of one on the eigenvalues in the same as the inverse decay rate of the diameter of finite clusters. For dimension of d=2, we show that this correlation length and the correlation length of two-point function with free boundary conditions at the corresponding dual temperature are equal up to a factor of two. For systems with first-order transitions, this relation helps to resolve certain inconsistencies between recent exact and numerical work on correlation lengths at the self-dual point β o . For systems with second order transitions, this relation implies the equality of the correlation length exponents from above below threshold, as well as an amplitude ratio of two. In the course of proving the above results, we establish several properties of independent interest, including left continuity of the inverse correlation length with free boundary conditions and upper semicontinuity of the decay rate for finite clusters in all dimensions, and left continuity of the two-dimensional free boundary condition percolation probability at β o . We also introduce DLR equations for the random cluster model and use them to establish ergodicity of the free measure. In order to prove these results, we introduce a new class of events which we call decoupling events and two inequalities for these events

How large are the consequences of covariate imbalance in cluster randomized trials: a simulation study with a continuous outcome and a binary covariate at the cluster level.

Science.gov (United States)

Moerbeek, Mirjam; van Schie, Sander

2016-07-11

The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. The covariate is binary and measured at the cluster level; the outcome is continuous and measured at the individual level. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Power levels based on the unadjusted linear mixed model are often too low. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels.

Application of the dynamically allocated virtual clustering management system to emulated tactical network experimentation

Science.gov (United States)

Marcus, Kelvin

2014-06-01

The U.S Army Research Laboratory (ARL) has built a "Network Science Research Lab" to support research that aims to improve their ability to analyze, predict, design, and govern complex systems that interweave the social/cognitive, information, and communication network genres. Researchers at ARL and the Network Science Collaborative Technology Alliance (NS-CTA), a collaborative research alliance funded by ARL, conducted experimentation to determine if automated network monitoring tools and task-aware agents deployed within an emulated tactical wireless network could potentially increase the retrieval of relevant data from heterogeneous distributed information nodes. ARL and NS-CTA required the capability to perform this experimentation over clusters of heterogeneous nodes with emulated wireless tactical networks where each node could contain different operating systems, application sets, and physical hardware attributes. Researchers utilized the Dynamically Allocated Virtual Clustering Management System (DAVC) to address each of the infrastructure support requirements necessary in conducting their experimentation. The DAVC is an experimentation infrastructure that provides the means to dynamically create, deploy, and manage virtual clusters of heterogeneous nodes within a cloud computing environment based upon resource utilization such as CPU load, available RAM and hard disk space. The DAVC uses 802.1Q Virtual LANs (VLANs) to prevent experimentation crosstalk and to allow for complex private networks. Clusters created by the DAVC system can be utilized for software development, experimentation, and integration with existing hardware and software. The goal of this paper is to explore how ARL and the NS-CTA leveraged the DAVC to create, deploy and manage multiple experimentation clusters to support their experimentation goals.

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

Science.gov (United States)

Almeida, Fernando R; Brayner, Angelo; Rodrigues, Joel J P C; Maia, Jose E Bessa

2017-06-07

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering . To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

The brain matures with stronger functional connectivity and decreased randomness of its network.

Directory of Open Access Journals (Sweden)

Dirk J A Smit

Full Text Available We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998 graph parameters C (local clustering and L (global path length for alpha (~10 Hz, beta (~20 Hz, and theta (~4 Hz oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ~50 yrs. Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ~18 yrs. Older age (55+ was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05, while path length was related to both white matter (alpha: max. r = 38, p<001 and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001 volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.

Distributed cluster management techniques for unattended ground sensor networks

Science.gov (United States)

Essawy, Magdi A.; Stelzig, Chad A.; Bevington, James E.; Minor, Sharon

2005-05-01

Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central "management" node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also

Small-world effect induced by weight randomization on regular networks

International Nuclear Information System (INIS)

Li, Menghui; Fan, Ying; Wang, Dahui; Li, Daqing; Wu, Jinshan; Di, Zengru

2007-01-01

The concept of edge weight provides additional depth for describing and adjusting the properties of networks. Redistribution of edge weight can effectively change the properties of networks even though the corresponding binary topology remains unchanged. Based on regular networks with initially homogeneous dissimilarity weights, random redistribution of edge weight can be enough to induce small world phenomena. The effects of random weight redistribution on both static properties and dynamical models of networks are investigated. The results reveal that randomization of weight can enhance the ability of synchronization of chaotic systems dramatically

Spectral dimensionality of random superconducting networks

International Nuclear Information System (INIS)

Day, A.R.; Xia, W.; Thorpe, M.F.

1988-01-01

We compute the spectral dimensionality d of random superconducting-normal networks by directly examining the low-frequency density of states at the percolation threshold. We find that d = 4.1 +- 0.2 and 5.8 +- 0.3 in two and three dimensions, respectively, which confirms the scaling relation d = 2d/(2-s/ν), where s is the superconducting exponent and ν the correlation-length exponent for percolation. We also consider the one-dimensional problem where scaling arguments predict, and our numerical simulations confirm, that d = 0. A simple argument provides an expression for the density of states of the localized high-frequency modes in this special case. We comment on the connection between our calculations and the ''termite'' problem of a random walker on a random superconducting-normal network and point out difficulties in inferring d from simulations of the termite problem

A scaling law for random walks on networks

Science.gov (United States)

Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick

2014-10-01

The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.

Topological clustering as a tool for planning water quality monitoring in water distribution networks

DEFF Research Database (Denmark)

Kirstein, Jonas Kjeld; Albrechtsen, Hans-Jørgen; Rygaard, Martin

2015-01-01

) identify steady clusters for a part of the network where an actual contamination has occurred; (2) analyze this event by the use of mesh diagrams; and (3) analyze the use of mesh diagrams as a decision support tool for planning water quality monitoring. Initially, the network model was divided...... into strongly and weakly connected clusters for selected time periods and mesh diagrams were used for analysing cluster connections in the Nørrebro district. Here, areas of particular interest for water quality monitoring were identified by including user-information about consumption rates and consumers...... particular sensitive towards water quality deterioration. The analysis revealed sampling locations within steady clusters, which increased samples' comparability over time. Furthermore, the method provided a simplified overview of water movement in complex distribution networks, and could assist...

Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

Directory of Open Access Journals (Sweden)

Muthukkumar R.

2017-04-01

Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

Energy Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks Using Distributed Dynamic Load Balanced Clustering Scheme

Directory of Open Access Journals (Sweden)

Muthukkumar R.

2016-07-01

Full Text Available Cognitive Radio (CR is a promising and potential technique to enable secondary users (SUs or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs or licenced users. The proven clustering approach is used to organize nodes in the network into the logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing (CSS. In this paper, a distributed dynamic load balanced clustering (DDLBC algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head (CH through cooperative gain, and residual energy that minimises network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process (MDP model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs reporting time slots of unavailability. The simulation results reveal that the clusters convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

Routing in Networks with Random Topologies

Science.gov (United States)

Bambos, Nicholas

1997-01-01

We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.

Directed clustering coefficient as a measure of systemic risk in complex banking networks

Science.gov (United States)

Tabak, Benjamin M.; Takami, Marcelo; Rocha, Jadson M. C.; Cajueiro, Daniel O.; Souza, Sergio R. S.

2014-01-01

Recent literature has focused on the study of systemic risk in complex networks. It is clear now, after the crisis of 2008, that the aggregate behavior of the interaction among agents is not straightforward and it is very difficult to predict. Contributing to this debate, this paper shows that the directed clustering coefficient may be used as a measure of systemic risk in complex networks. Furthermore, using data from the Brazilian interbank network, we show that the directed clustering coefficient is negatively correlated with domestic interest rates.

Bipartite quantum states and random complex networks

International Nuclear Information System (INIS)

Garnerone, Silvano; Zanardi, Paolo; Giorda, Paolo

2012-01-01

We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs, we derive an analytic expression for the averaged entanglement entropy S-bar while for general complex networks we rely on numerics. For a large number of nodes n we find a scaling S-bar ∼c log n +g e where both the prefactor c and the sub-leading O(1) term g e are characteristic of the different classes of complex networks. In particular, g e encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool for the analysis of large complex networks with non-trivial topological properties. (paper)

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

Science.gov (United States)

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Directory of Open Access Journals (Sweden)

Farhan Aadil

2018-05-01

Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Logarithmic corrections to scaling in critical percolation and random resistor networks.

Science.gov (United States)

Stenull, Olaf; Janssen, Hans-Karl

2003-09-01

We study the critical behavior of various geometrical and transport properties of percolation in six dimensions. By employing field theory and renormalization group methods we analyze fluctuation induced logarithmic corrections to scaling up to and including the next-to-leading order correction. Our study comprehends the percolation correlation function, i.e., the probability that two given points are connected, and some of the fractal masses describing percolation clusters. To be specific, we calculate the mass of the backbone, the red bonds, and the shortest path. Moreover, we study key transport properties of percolation as represented by the random resistor network. We investigate the average two-point resistance as well as the entire family of multifractal moments of the current distribution.

Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network

Directory of Open Access Journals (Sweden)

G. Kannan

2015-07-01

Full Text Available Wireless Sensor Network (WSN provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis

Directory of Open Access Journals (Sweden)

Chao Zhang

2017-09-01

Full Text Available A wireless-powered sensor network (WPSN consisting of one hybrid access point (HAP, a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Zhilu Wu

2015-01-01

Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

Cluster synchronization of community network with distributed time delays via impulsive control

International Nuclear Information System (INIS)

Leng Hui; Wu Zhao-Yan

2016-01-01

Cluster synchronization is an important dynamical behavior in community networks and deserves further investigations. A community network with distributed time delays is investigated in this paper. For achieving cluster synchronization, an impulsive control scheme is introduced to design proper controllers and an adaptive strategy is adopted to make the impulsive controllers unified for different networks. Through taking advantage of the linear matrix inequality technique and constructing Lyapunov functions, some synchronization criteria with respect to the impulsive gains, instants, and system parameters without adaptive strategy are obtained and generalized to the adaptive case. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results. (paper)

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

International Nuclear Information System (INIS)

Ammendola, R; Salamon, A; Salina, G; Biagioni, A; Prezza, O; Cicero, F Lo; Lonardo, A; Paolucci, P S; Rossetti, D; Tosoratto, L; Vicini, P; Simula, F

2011-01-01

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera ® FPGA, are provided.

APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

Energy Technology Data Exchange (ETDEWEB)

Ammendola, R; Salamon, A; Salina, G [INFN Tor Vergata, Roma (Italy); Biagioni, A; Prezza, O; Cicero, F Lo; Lonardo, A; Paolucci, P S; Rossetti, D; Tosoratto, L; Vicini, P [INFN Roma, Roma (Italy); Simula, F [Sapienza Universita di Roma, Roma (Italy)

2011-12-23

We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera{sup Registered-Sign} FPGA, are provided.

Cluster tails for critical power-law inhomogeneous random graphs

NARCIS (Netherlands)

van der Hofstad, R.; Kliem, S.; van Leeuwaarden, J.S.H.

2018-01-01

Recently, the scaling limit of cluster sizes for critical inhomogeneous random graphs of rank-1 type having finite variance but infinite third moment degrees was obtained in Bhamidi et al. (Ann Probab 40:2299–2361, 2012). It was proved that when the degrees obey a power law with exponent τ∈ (3 , 4)

Alerts Visualization and Clustering in Network-based Intrusion Detection

Energy Technology Data Exchange (ETDEWEB)

Yang, Dr. Li [University of Tennessee; Gasior, Wade C [ORNL; Dasireddy, Swetha [University of Tennessee

2010-04-01

Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administrator with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.

Energy Efficient Medium Access Control Protocol for Clustered Wireless Sensor Networks with Adaptive Cross-Layer Scheduling.

Science.gov (United States)

Sefuba, Maria; Walingo, Tom; Takawira, Fambirai

2015-09-18

This paper presents an Energy Efficient Medium Access Control (MAC) protocol for clustered wireless sensor networks that aims to improve energy efficiency and delay performance. The proposed protocol employs an adaptive cross-layer intra-cluster scheduling and an inter-cluster relay selection diversity. The scheduling is based on available data packets and remaining energy level of the source node (SN). This helps to minimize idle listening on nodes without data to transmit as well as reducing control packet overhead. The relay selection diversity is carried out between clusters, by the cluster head (CH), and the base station (BS). The diversity helps to improve network reliability and prolong the network lifetime. Relay selection is determined based on the communication distance, the remaining energy and the channel quality indicator (CQI) for the relay cluster head (RCH). An analytical framework for energy consumption and transmission delay for the proposed MAC protocol is presented in this work. The performance of the proposed MAC protocol is evaluated based on transmission delay, energy consumption, and network lifetime. The results obtained indicate that the proposed MAC protocol provides improved performance than traditional cluster based MAC protocols.

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

Science.gov (United States)

McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with

Secure and Fair Cluster Head Selection Protocol for Enhancing Security in Mobile Ad Hoc Networks

Directory of Open Access Journals (Sweden)

B. Paramasivan

2014-01-01

Full Text Available Mobile ad hoc networks (MANETs are wireless networks consisting of number of autonomous mobile devices temporarily interconnected into a network by wireless media. MANETs become one of the most prevalent areas of research in the recent years. Resource limitations, energy efficiency, scalability, and security are the great challenging issues in MANETs. Due to its deployment nature, MANETs are more vulnerable to malicious attack. The secure routing protocols perform very basic security related functions which are not sufficient to protect the network. In this paper, a secure and fair cluster head selection protocol (SFCP is proposed which integrates security factors into the clustering approach for achieving attacker identification and classification. Byzantine agreement based cooperative technique is used for attacker identification and classification to make the network more attack resistant. SFCP used to solve this issue by making the nodes that are totally surrounded by malicious neighbors adjust dynamically their belief and disbelief thresholds. The proposed protocol selects the secure and energy efficient cluster head which acts as a local detector without imposing overhead to the clustering performance. SFCP is simulated in network simulator 2 and compared with two protocols including AODV and CBRP.

Secure and fair cluster head selection protocol for enhancing security in mobile ad hoc networks.

Science.gov (United States)

Paramasivan, B; Kaliappan, M

2014-01-01

Mobile ad hoc networks (MANETs) are wireless networks consisting of number of autonomous mobile devices temporarily interconnected into a network by wireless media. MANETs become one of the most prevalent areas of research in the recent years. Resource limitations, energy efficiency, scalability, and security are the great challenging issues in MANETs. Due to its deployment nature, MANETs are more vulnerable to malicious attack. The secure routing protocols perform very basic security related functions which are not sufficient to protect the network. In this paper, a secure and fair cluster head selection protocol (SFCP) is proposed which integrates security factors into the clustering approach for achieving attacker identification and classification. Byzantine agreement based cooperative technique is used for attacker identification and classification to make the network more attack resistant. SFCP used to solve this issue by making the nodes that are totally surrounded by malicious neighbors adjust dynamically their belief and disbelief thresholds. The proposed protocol selects the secure and energy efficient cluster head which acts as a local detector without imposing overhead to the clustering performance. SFCP is simulated in network simulator 2 and compared with two protocols including AODV and CBRP.

Optimal Quantum Spatial Search on Random Temporal Networks

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Optimal Quantum Spatial Search on Random Temporal Networks.

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G(n,p), where p is the probability that any two given nodes are connected: After every time interval τ, a new graph G(n,p) replaces the previous one. We prove analytically that, for any given p, there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O(sqrt[n]), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Clustering predicts memory performance in networks of spiking and non-spiking neurons

Directory of Open Access Journals (Sweden)

Weiliang eChen

2011-03-01

Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

Cluster synchronization in community network with hybrid coupling

International Nuclear Information System (INIS)

Yang, Lixin; Jiang, Jun; Liu, Xiaojun

2016-01-01

Highlights: • A community network model with hybrid coupling is proposed. • Control scheme is designed via combining adaptive external coupling strength and feedback control. • The influence of topology structure on synchronization of community network is discussed. - Abstract: A general model of community network with hybrid coupling is proposed in this paper. In the community network model with hybrid coupling, the inner connections are in the same type of coupling within the same community and in different types of coupling in different communities. The connections between different pair of communities are also nonidentical. Cluster synchronization of community network with hybrid coupling is investigated via adaptive couplings control scheme. Effective controllers are designed for constructing an effective control scheme and adjusting automatically the adaptive external coupling strength by taking external coupling strength as adaptive variables on a small fraction of network edges. Moreover, the impact of the topology on the synchronizability of community network is investigated. The numerical results reveal that the number of links between communities and the degree of the connector nodes have significant effects on the synchronization performance.

Rumor Diffusion in an Interests-Based Dynamic Social Network

Directory of Open Access Journals (Sweden)

Mingsheng Tang

2013-01-01

Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.

The dilute random field Ising model by finite cluster approximation

International Nuclear Information System (INIS)

Benyoussef, A.; Saber, M.

1987-09-01

Using the finite cluster approximation, phase diagrams of bond and site diluted three-dimensional simple cubic Ising models with a random field have been determined. The resulting phase diagrams have the same general features for both bond and site dilution. (author). 7 refs, 4 figs

Generic Properties of Random Gene Regulatory Networks.

Science.gov (United States)

Li, Zhiyuan; Bianco, Simone; Zhang, Zhaoyang; Tang, Chao

2013-12-01

Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

Science.gov (United States)

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Navigation by anomalous random walks on complex networks.

Science.gov (United States)

Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

2016-11-23

Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.

Navigation by anomalous random walks on complex networks

Science.gov (United States)

Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

2016-11-01

Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.

Network Signaling Channel for Improving ZigBee Performance in Dynamic Cluster-Tree Networks

Directory of Open Access Journals (Sweden)

D. Hämäläinen

2008-03-01

Full Text Available ZigBee is one of the most potential standardized technologies for wireless sensor networks (WSNs. Yet, sufficient energy-efficiency for the lowest power WSNs is achieved only in rather static networks. This severely limits the applicability of ZigBee in outdoor and mobile applications, where operation environment is harsh and link failures are common. This paper proposes a network channel beaconing (NCB algorithm for improving ZigBee performance in dynamic cluster-tree networks. NCB reduces the energy consumption of passive scans by dedicating one frequency channel for network beacon transmissions and by energy optimizing their transmission rate. According to an energy analysis, the power consumption of network maintenance operations reduces by 70%–76% in dynamic networks. In static networks, energy overhead is negligible. Moreover, the service time for data routing increases up to 37%. The performance of NCB is validated by ns-2 simulations. NCB can be implemented as an extension on MAC and NWK layers and it is fully compatible with ZigBee.

Robustness of Dengue Complex Network under Targeted versus Random Attack

Directory of Open Access Journals (Sweden)

Hafiz Abid Mahmood Malik

2017-01-01

Full Text Available Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO, 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia.

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Directory of Open Access Journals (Sweden)

Fei Wang

2017-01-01

Full Text Available An Intensified Charge-Coupled Device (ICCD image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a Different from the independent identically distributed (i.i.d. noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

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Deepa Devasenapathy

2015-01-01

Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

Science.gov (United States)

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

Science.gov (United States)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Quasirandom geometric networks from low-discrepancy sequences

Science.gov (United States)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Securing Gateways within Clustered Power Centric Network of Nodes

Directory of Open Access Journals (Sweden)

Qaisar Javaid

2016-01-01

Full Text Available Knowledge Networks are gaining momentum within cyber world. Knowledge leads to innovation and for this reason organizations focus on research and information gathering in order to gain and improve existing knowledge. This of information era, which is primarily based on world wide web technologies, enables significantly expanded networks of people to communicate and collaborate 'virtually' across teams, across entire organizations and across the world, anytime and anywhere. Innovations in computing and telecommunications have transformed the corporations from structured and manageable types to interwoven network of blurred boundaries such as; ad hoc networks and mobile wireless networks, etc. This study explores knowledge networks in Information Technology and security leaks that are found, as well as measures that are taken to counter this menace which is coming up with optimal Secure Clustered Power Centric node network. The paper concludes these measures, evaluating and integrating them to come up with a secured network design.

Structure of a randomly grown 2-d network

DEFF Research Database (Denmark)

Ajazi, Fioralba; Napolitano, George M.; Turova, Tatyana

2015-01-01

We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in...... in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons.......We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes...

IR wireless cluster synapses of HYDRA very large neural networks

Science.gov (United States)

Jannson, Tomasz; Forrester, Thomas

2008-04-01

RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

Making birthing safe for Pakistan women: a cluster randomized trial

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Khan Muhammad

2012-07-01

Full Text Available Abstract Background Two out of three neonatal deaths occur in just 10 countries and Pakistan stands third among them. Maternal mortality is also high with most deaths occurring during labor, birth, and first few hours after birth. Enhanced access and utilization of skilled delivery and emergency obstetric care is the demonstrated strategy in reducing maternal and neonatal mortality. This trial aims to compare reduction in neonate mortality and utilization of available safe birthing and Emergency Obstetric and Neonatal Care services among pregnant mothers receiving ‘structured birth planning’, and/or ‘transport facilitation’ compared to routine care. Methods A pragmatic cluster randomized trial, with qualitative and economic studies, will be conducted in Jhang, Chiniot and Khanewal districts of Punjab, Pakistan, from February 2011 to May 2013. At least 29,295 pregnancies will be registered in the three arms, seven clusters per arm; 1 structured birth planning and travel facilitation, 2 structured birth planning, and 3 control arm. Trial will be conducted through the Lady Health Worker program. Main outcomes are difference in neonatal mortality and service utilization; maternal mortality being the secondary outcome. Cluster level analysis will be done according to intention-to-treat. Discussion A nationwide network of about 100,000 lady health workers is already involved in antenatal and postnatal care of pregnant women. They also act as “gatekeepers” for the child birthing services. This gate keeping role mainly includes counseling and referral for skill birth attendance and travel arrangements for emergency obstetric care (if required. The review of current arrangements and practices show that the care delivery process needs enhancement to include adequate information provision as well as informed “decision” making and planned “action” by the pregnant women. The proposed three-year research is to develop, through national

Training and validation of the ATLAS pixel clustering neural networks

CERN Document Server

The ATLAS collaboration

2018-01-01

The high centre-of-mass energy of the LHC gives rise to dense environments, such as the core of high-pT jets, in which the charge clusters left by ionising particles in the silicon sensors of the pixel detector can merge, compromising the tracking and vertexing efficiency. To recover optimal performance, a neural network-based approach is used to separate clusters originating from single and multiple particles and to estimate all hit positions within clusters. This note presents the training strategy employed and a set of benchmark performance measurements on a Monte Carlo sample of high-pT dijet events.

Cluster-based service discovery for heterogeneous wireless sensor networks

NARCIS (Netherlands)

Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

2007-01-01

We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the

Researches on the Security of Cluster-based Communication Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yanhong Sun

2014-08-01

Full Text Available Along with the in-depth application of sensor networks, the security issues have gradually become the bottleneck of wireless sensor applications. To provide a solution for security scheme is a common concern not only of researchers but also of providers, integrators and users of wireless sensor networks. Based on this demand, this paper focuses on the research of strengthening the security of cluster-based wireless sensor networks. Based on the systematic analysis of the clustering protocol and its security enhancement scheme, the paper introduces the broadcast authentication scheme, and proposes an SA-LEACH network security enhancement protocol. The performance analysis and simulation experiments prove that the protocol consumes less energy with the same security requirements, and when the base station is comparatively far from the network deployment area, it is more advantageous in terms of energy consumption and t more suitable for wireless sensor networks.

Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks

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Jeungeun Song

2018-01-01

Full Text Available Duty-cycled sensor networks provide a new perspective for improvement of energy efficiency and reliability assurance of multi-hop cooperative sensor networks. In this paper, we consider the energy-efficient cooperative node sleeping and clustering problems in cooperative sensor networks where clusters of relay nodes jointly transmit sensory data to the next hop. Our key idea for guaranteeing reliability is to exploit the on-demand number of cooperative nodes, facilitating the prediction of personalized end-to-end (ETE reliability. Namely, a novel reliability-aware cooperative routing (RCR scheme is proposed to select k-cooperative nodes at every hop (RCR-selection. After selecting k cooperative nodes at every hop, all of the non-cooperative nodes will go into sleep status. In order to solve the cooperative node clustering problem, we propose the RCR-based optimal relay assignment and cooperative data delivery (RCR-delivery scheme to provide a low-communication-overhead data transmission and an optimal duty cycle for a given number of cooperative nodes when the network is dynamic, which enables part of cooperative nodes to switch into idle status for further energy saving. Through the extensive OPNET-based simulations, we show that the proposed scheme significantly outperforms the existing geographic routing schemes and beaconless geographic routings in wireless sensor networks with a highly dynamic wireless channel and controls energy consumption, while ETE reliability is effectively guaranteed.

The investigation of social networks based on multi-component random graphs

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Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Ethical and policy issues in cluster randomized trials: rationale and design of a mixed methods research study

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Chaudhry Shazia H

2009-07-01

Full Text Available Abstract Background Cluster randomized trials are an increasingly important methodological tool in health research. In cluster randomized trials, intact social units or groups of individuals, such as medical practices, schools, or entire communities – rather than individual themselves – are randomly allocated to intervention or control conditions, while outcomes are then observed on individual cluster members. The substantial methodological differences between cluster randomized trials and conventional randomized trials pose serious challenges to the current conceptual framework for research ethics. The ethical implications of randomizing groups rather than individuals are not addressed in current research ethics guidelines, nor have they even been thoroughly explored. The main objectives of this research are to: (1 identify ethical issues arising in cluster trials and learn how they are currently being addressed; (2 understand how ethics reviews of cluster trials are carried out in different countries (Canada, the USA and the UK; (3 elicit the views and experiences of trial participants and cluster representatives; (4 develop well-grounded guidelines for the ethical conduct and review of cluster trials by conducting an extensive ethical analysis and organizing a consensus process; (5 disseminate the guidelines to researchers, research ethics boards (REBs, journal editors, and research funders. Methods We will use a mixed-methods (qualitative and quantitative approach incorporating both empirical and conceptual work. Empirical work will include a systematic review of a random sample of published trials, a survey and in-depth interviews with trialists, a survey of REBs, and in-depth interviews and focus group discussions with trial participants and gatekeepers. The empirical work will inform the concurrent ethical analysis which will lead to a guidance document laying out principles, policy options, and rationale for proposed guidelines. An

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

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Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

2017-07-03

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial

NARCIS (Netherlands)

Vlemmix, Floortje; Rosman, Ageeth N.; Rijnders, Marlies E.; Beuckens, Antje; Opmeer, Brent C.; Mol, Ben W. J.; Kok, Marjolein; Fleuren, Margot A. H.

2015-01-01

To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Cluster randomized controlled trial. Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the Netherlands. Singleton breech

Implementation of client versus care-provider strategies to improve external cephalic version rates: a cluster randomized controlled trial

NARCIS (Netherlands)

Vlemmix, F.; Rosman, A.N.; Rijnders, M.E.; Beuckens, A.; Opmeer, B.C.; Mol, B.W.J.; Kok, M.; Fleuren, M.A.H.

2015-01-01

Onjective: To determine the effectiveness of a client or care-provider strategy to improve the implementation of external cephalic version. Design: Cluster randomized controlled trial.Setting: Twenty-five clusters; hospitals and their referring midwifery practices randomly selected in the

Evidence for the additions of clustered interacting nodes during the evolution of protein interaction networks from network motifs

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Guo Hao

2011-05-01

Full Text Available Abstract Background High-throughput screens have revealed large-scale protein interaction networks defining most cellular functions. How the proteins were added to the protein interaction network during its growth is a basic and important issue. Network motifs represent the simplest building blocks of cellular machines and are of biological significance. Results Here we study the evolution of protein interaction networks from the perspective of network motifs. We find that in current protein interaction networks, proteins of the same age class tend to form motifs and such co-origins of motif constituents are affected by their topologies and biological functions. Further, we find that the proteins within motifs whose constituents are of the same age class tend to be densely interconnected, co-evolve and share the same biological functions, and these motifs tend to be within protein complexes. Conclusions Our findings provide novel evidence for the hypothesis of the additions of clustered interacting nodes and point out network motifs, especially the motifs with the dense topology and specific function may play important roles during this process. Our results suggest functional constraints may be the underlying driving force for such additions of clustered interacting nodes.

Improving Language Comprehension in Preschool Children with Language Difficulties: A Cluster Randomized Trial

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Hagen, Åste M.; Melby-Lervåg, Monica; Lervåg, Arne

2017-01-01

Background: Children with language comprehension difficulties are at risk of educational and social problems, which in turn impede employment prospects in adulthood. However, few randomized trials have examined how such problems can be ameliorated during the preschool years. Methods: We conducted a cluster randomized trial in 148 preschool…

Photo-z with CuBANz: An improved photometric redshift estimator using Clustering aided Back propagation Neural network

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Samui, Saumyadip; Samui Pal, Shanoli

2017-02-01

We present an improved photometric redshift estimator code, CuBANz, that is publicly available at https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which makes it more efficient than existing neural network codes. In CuBANz, the training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. u - g , g - r etc.) rather than the apparent magnitudes at various photometric bands as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. Separate neural networks are trained for each cluster using all possible colors, magnitudes and uncertainties in the measurements. For a galaxy with unknown redshift, we identify the closest possible clusters having similar photometric properties and use those clusters to get the photometric redshifts using the particular networks that were trained using those cluster members. For galaxies that do not match with any training cluster, the photometric redshifts are obtained from a separate network that uses entire training set. This clustering method enables us to determine the redshifts more accurately. SDSS Stripe 82 catalog has been used here for the demonstration of the code. For the clustered sources with redshift range zspec training/testing phase is as low as 0.03 compared to the existing ANNz code that provides residual error on the same test data set of 0.05. Further, we provide a much better estimate of the uncertainty of the derived photometric redshift.

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

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Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.

2017-09-01

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

Cluster Head Selection in a Homogeneous Wireless Sensor Network Ensuring Full Connectivity with Minimum Isolated Nodes

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Tapan Kumar Jain

2014-01-01

Full Text Available The research work proposes a cluster head selection algorithm for a wireless sensor network. A node can be a cluster head if it is connected to at least one unique neighbor node where the unique neighbor is the one that is not connected to any other node. If there is no connected unique node then the CH is selected on the basis of residual energy and the number of neighbor nodes. With the increase in number of clusters, the processing energy of the network increases; hence, this algorithm proposes minimum number of clusters which further leads to increased network lifetime. The major novel contribution of the proposed work is an algorithm that ensures a completely connected network with minimum number of isolated nodes. An isolated node will remain only if it is not within the transmission range of any other node. With the maximum connectivity, the coverage of the network is automatically maximized. The superiority of the proposed design is verified by simulation results done in MATLAB, where it clearly depicts that the total numbers of rounds before the network dies out are maximum compared to other existing protocols.

IP2P K-means: an efficient method for data clustering on sensor networks

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Peyman Mirhadi

2013-03-01

Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.

CoCMA: Energy-Efficient Coverage Control in Cluster-Based Wireless Sensor Networks Using a Memetic Algorithm

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Yung-Chung Wang

2009-06-01

Full Text Available Deployment of wireless sensor networks (WSNs has drawn much attention in recent years. Given the limited energy for sensor nodes, it is critical to implement WSNs with energy efficiency designs. Sensing coverage in networks, on the other hand, may degrade gradually over time after WSNs are activated. For mission-critical applications, therefore, energy-efficient coverage control should be taken into consideration to support the quality of service (QoS of WSNs. Usually, coverage-controlling strategies present some challenging problems: (1 resolving the conflicts while determining which nodes should be turned off to conserve energy; (2 designing an optimal wake-up scheme that avoids awakening more nodes than necessary. In this paper, we implement an energy-efficient coverage control in cluster-based WSNs using a Memetic Algorithm (MA-based approach, entitled CoCMA, to resolve the challenging problems. The CoCMA contains two optimization strategies: a MA-based schedule for sensor nodes and a wake-up scheme, which are responsible to prolong the network lifetime while maintaining coverage preservation. The MA-based schedule is applied to a given WSN to avoid unnecessary energy consumption caused by the redundant nodes. During the network operation, the wake-up scheme awakens sleeping sensor nodes to recover coverage hole caused by dead nodes. The performance evaluation of the proposed CoCMA was conducted on a cluster-based WSN (CWSN under either a random or a uniform deployment of sensor nodes. Simulation results show that the performance yielded by the combination of MA and wake-up scheme is better than that in some existing approaches. Furthermore, CoCMA is able to activate fewer sensor nodes to monitor the required sensing area.

Generating Realistic Labelled, Weighted Random Graphs

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Michael Charles Davis

2015-12-01

Full Text Available Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs. Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

A High-Efficiency Uneven Cluster Deployment Algorithm Based on Network Layered for Event Coverage in UWSNs

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Shanen Yu

2016-12-01

Full Text Available Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.

Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study

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Ma Jinhui

2013-01-01

Full Text Available Abstracts Background The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE and cluster-specific (i.e. random-effects logistic regression (RELR models for analyzing data from cluster randomized trials (CRTs with missing binary responses. Methods In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE, and coverage probability. Results GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF 50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied. Conclusion GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.

Clustering and fault tolerance for target tracking using wireless sensor networks

International Nuclear Information System (INIS)

Bhatti, S.; Khanzada, S.; Memon, S.

2012-01-01

Over the last few years, the deployment of WSNs (Wireless Sensor Networks) has been fostered in diverse applications. WSN has great potential for a variety of domains ranging from scientific experiments to commercial applications. Due to the deployment of WSNs in dynamic and unpredictable environments. They have potential to cope with variety of faults. This paper proposes an energy-aware fault-tolerant clustering protocol for target tracking applications termed as the FITf (Fault Tolerant Target Tracking) protocol The identification of RNs (Redundant Nodes) makes SN (Sensor Node) fault tolerance plausible and the clustering endorsed recovery of sensors supervised by a faulty CH (Cluster Head). The FfTT protocol intends two steps of reducing energy consumption: first, by identifying RNs in the network; secondly, by restricting the numbers of SNs sending data to the CH. Simulations validate the scalability and low power consumption of the FITf protocol in comparison with LEACH protocol. (author)

A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

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ZiQi Hao

2015-01-01

Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

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Gholami, Mohammad; Brennan, Robert W

2016-01-06

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

Effect of Behavior Modification on Outcome in Early- to Moderate-Stage Chronic Kidney Disease: A Cluster-Randomized Trial.

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Yamagata, Kunihiro; Makino, Hirofumi; Iseki, Kunitoshi; Ito, Sadayoshi; Kimura, Kenjiro; Kusano, Eiji; Shibata, Takanori; Tomita, Kimio; Narita, Ichiei; Nishino, Tomoya; Fujigaki, Yoshihide; Mitarai, Tetsuya; Watanabe, Tsuyoshi; Wada, Takashi; Nakamura, Teiji; Matsuo, Seiichi

2016-01-01

Owing to recent changes in our understanding of the underlying cause of chronic kidney disease (CKD), the importance of lifestyle modification for preventing the progression of kidney dysfunction and complications has become obvious. In addition, effective cooperation between general physicians (GPs) and nephrologists is essential to ensure a better care system for CKD treatment. In this cluster-randomized study, we studied the effect of behavior modification on the outcome of early- to moderate-stage CKD. Stratified open cluster-randomized trial. A total of 489 GPs belonging to 49 local medical associations (clusters) in Japan. A total of 2,379 patients (1,195 in group A (standard intervention) and 1,184 in group B (advanced intervention)) aged between 40 and 74 years, who had CKD and were under consultation with GPs. All patients were managed in accordance with the current CKD guidelines. The group B clusters received three additional interventions: patients received both educational intervention for lifestyle modification and a CKD status letter, attempting to prevent their withdrawal from treatment, and the group B GPs received data sheets to facilitate reducing the gap between target and practice. The primary outcome measures were 1) the non-adherence rate of accepting continuous medical follow-up of the patients, 2) the collaboration rate between GPs and nephrologists, and 3) the progression of CKD. The rate of discontinuous clinical visits was significantly lower in group B (16.2% in group A vs. 11.5% in group B, p = 0.01). Significantly higher referral and co-treatment rates were observed in group B (pbehavior modification of CKD patients, namely, significantly lower discontinuous clinical visits, and behavior modification of both GPs and nephrologists, namely significantly higher referral and co-treatment rates, resulting in the retardation of CKD progression, especially in patients with proteinuric Stage 3 CKD. The University Hospital Medical Information

Event-based cluster synchronization of coupled genetic regulatory networks