Distributed Approximation and Tracking Using Selective Gossip

  • Deniz Üstebay
  • Rui Castro
  • Mark Coates
  • Michael Rabbat
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

This chapter presents selective gossip which is an algorithm that applies the idea of iterative information exchange to vectors of data. Instead of communicating the entire vector and wasting network resources, our method adaptively focuses communication on the most significant entries of the vector. We prove that nodes running selective gossip asymptotically reach consensus on these significant entries, and they simultaneously reach an agreement on the indices of entries which are insignificant. The results demonstrate that selective gossip provides significant communication savings in terms of the number of scalars transmitted. In the second part of the chapter we propose a distributed particle filter employing selective gossip. We show that distributed particle filters employing selective gossip provide comparable results to the centralized bootstrap particle filter while decreasing the communication overhead compared to using randomized gossip to distribute the filter computations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Deniz Üstebay
    • 1
  • Rui Castro
    • 2
  • Mark Coates
    • 1
  • Michael Rabbat
    • 1
  1. 1.Department of Electrical and Computer EngineeringMcGill UniversityMontrealCanada
  2. 2.Department of MathematicsEindhoven University of TechnologyEindhovenThe Netherlands

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