Belief Propagation in Wireless Sensor Networks - A Practical Approach

  • Tal Anker
  • Danny Dolev
  • Bracha Hod
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5258)


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.


Belief Propagation Wireless Sensor Networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tal Anker
    • 1
    • 2
  • Danny Dolev
    • 1
  • Bracha Hod
    • 1
  1. 1.The Hebrew University of JerusalemIsrael
  2. 2.Marvell SemiconductorUSA

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