Efficient Clustering for Improving Network Performance in Wireless Sensor Networks

Anker, Tal; Bickson, Danny; Dolev, Danny; Hod, Bracha

doi:10.1007/978-3-540-77690-1_14

Cited by 40 publications

(29 citation statements)

References 13 publications

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“…Instead of receiving a unicast message m ji (x i ) t from node j, node i is required to extract the relevant information from the broadcast message, by a subtraction of its own information from the previous round's, and by calculating it on its own [9]:…”

Section: Robustness Against Failuresmentioning

confidence: 99%

“…Using the BP method, it is possible to achieve a good approximation in polynomial time, since the computation is dispersed and divided among all the nodes, Following the graphical model definitions and the cost functions as presented in [9], the clustering problem can be formulated as follows: x i is defined to be a CH candidate of node i and ψ i (x i ) defines a local cost function of connecting node i to x i . ψ ij (x i , x j ) represents the constraints between two neighbors i and j to prevent improper assignment of CH association.…”

Section: Case Study: Clusteringmentioning

confidence: 99%

“…The information regarding these costs is updated based on the information received from the parent and the descendants in the tree structure, according to the Min-Sum algorithm. The final goal of the nodes is to select CHs that minimize the overall cost, over the whole network, based on the cost values and the constraints between the nodes [9]. In this application, two connected nodes in the graph are considered to be in the same clique if they have some predefined number of common neighbors.…”

Section: Case Study: Clusteringmentioning

confidence: 99%

“…In contrast with previous work in this area [9], this paper focuses on the general construction and properties of the BP framework in the WSN, rather than on the application itself. The goal of this paper is to understand the general characteristics of the BP framework, which has not been presented to date.…”

Section: Introductionmentioning

confidence: 99%

“…The goal of the algorithm is to minimize the overall cost over all the nodes in the network, based on the local cost functions and the constraints among the nodes. The reader is referred to [9] for further details about the Min-Sum algorithm and its broadcast implementation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Belief Propagation in Wireless Sensor Networks - A Practical Approach

Anker

Dolev

Hod

2008

Wireless Algorithms, Systems, and Applications

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Abstract. Distributed inference schemes for detection, estimation and learning comprise an attractive approach to Wireless Sensor Networks (WSNs), because of properties such as asynchronous operation and robustness in the face of failures. Belief Propagation (BP) is a method for distributed inference which provides accurate results with rapid convergence properties. However, applying a BP algorithm to WSN is not trivial, due to the unique characteristics of WSN networks. Many papers which have proposed using BP for WSNs do not consider all of the constraints which these networks impose. This paper first undertakes a thorough study of the practical challenges of WSNs which are raised in the context of distributed inference. It then presents a framework which implements both localized and data-centric approaches to improve the effectiveness and the robustness of this algorithm in the WSN environment. The proposed solution is empirically evaluated, as applied to the clustering problem, and it can be easily extended to suit many other applications that use BP as an underlying algorithm.

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Section: Robustness Against Failuresmentioning

confidence: 99%

Section: Case Study: Clusteringmentioning

confidence: 99%

Section: Case Study: Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Belief Propagation in Wireless Sensor Networks - A Practical Approach

Anker

Dolev

Hod

2008

Wireless Algorithms, Systems, and Applications

Self Cite

View full text Add to dashboard Cite

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Optimal Cluster Sizes for Wireless Sensor Networks: An Experimental Analysis

Förster

Murphy

2010

Ad Hoc Networks

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Abstract. Node clustering and data aggregation are popular techniques to reduce energy consumption in large WSNs and a large body of literature has emerged describing various clustering protocols. Unfortunately, for practitioners wishing to exploit clustering in deployments, there is little help when trying to identify a protocol that meets their needs. This paper takes a step back from specific protocols to consider the fundamental question: what is the optimal cluster size in terms of the resulting communication generated to collect data. Our experimental analysis considers a wide range of parameters that characterize the WSN, and shows that in the most common cases, clusters in which all nodes can communicate in one hop to the cluster head are optimal.

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An Entropic Approach to Data Aggregation with Divergence Measure Based Clustering in Sensor Network

Sinha

Lobiyal

2011

Advances in Computing and Communications

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Efficient Clustering for Improving Network Performance in Wireless Sensor Networks

Cited by 40 publications

References 13 publications

Belief Propagation in Wireless Sensor Networks - A Practical Approach

Belief Propagation in Wireless Sensor Networks - A Practical Approach

Optimal Cluster Sizes for Wireless Sensor Networks: An Experimental Analysis

An Entropic Approach to Data Aggregation with Divergence Measure Based Clustering in Sensor Network

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