Belief propagation in wireless sensor networks - A practical approach

Tal Anker*, Danny Dolev, Bracha Hod

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

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 challenging. Many papers that proposed using BP for WSNs do not consider all of the constraints which these networks impose. This paper presents a framework that 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.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - Third International Conference, WASA 2008, Proceedings
PublisherSpringer Verlag
Pages466-479
Number of pages14
ISBN (Print)3540885811, 9783540885818
DOIs
StatePublished - 2008
Event3rd International Conference on Wireless Algorithms, Systems, and Applications, WASA 2008 - Dallas, TX, United States
Duration: 26 Oct 200828 Oct 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5258 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Wireless Algorithms, Systems, and Applications, WASA 2008
Country/TerritoryUnited States
CityDallas, TX
Period26/10/0828/10/08

Keywords

  • Belief propagation
  • Wireless sensor networks

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