Robust distributed estimation in sensor networks using the embedded polygons algorithm

Véronique Delouille, Ramesh Neelamani, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

Abstract

We propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow a Gaussian hidden Markov graphical model with cycles. The embedded polygons algorithm decomposes a loopy graphical model into a number of linked embedded polygons and then applies a parallel block Gauss-Seidel iteration comprising local LMMSE estimation on each polygon (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and polygons. The algorithm is robust to temporary communication faults such as link failures and sleeping nodes and enjoys guaranteed convergence under mild conditions. A simulation study indicates that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually be counterproductive.

Original languageEnglish (US)
Title of host publicationThird International Symposium on Information Processing in Sensor Networks, IPSN 2004
PublisherAssociation for Computing Machinery (ACM)
Pages405-413
Number of pages9
ISBN (Print)1581138466, 9781581138467
DOIs
StatePublished - 2004
EventThird International Symposium on Information Processing in Sensor Networks, IPSN 2004 - Berkeley, CA., United States
Duration: Apr 26 2004Apr 27 2004

Publication series

NameThird International Symposium on Information Processing in Sensor Networks, IPSN 2004

Other

OtherThird International Symposium on Information Processing in Sensor Networks, IPSN 2004
CountryUnited States
CityBerkeley, CA.
Period4/26/044/27/04

Keywords

  • Distributed estimation
  • Graphical models
  • Hidden Markov models
  • Matrix splitting
  • Sensor networks
  • Wiener filter

ASJC Scopus subject areas

  • Engineering(all)

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