A space-time diffusion scheme for peer-to-peer least-squares estimation

Lin Xiao, Stephen Boyd, Sanjay Lall

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

140 Scopus citations

Abstract

We consider a sensor network in which each sensor takes measurements, at various times, of some unknown parameters, corrupted by independent Gaussian noises. Each node can take a finite or infinite number of measurements, at arbitrary times (i.e., asynchronously). We propose a space-time diffusion scheme, that relies only on peer-to-peer communication, and allows every node to asymptotically compute the global maximum-likelihood estimate of the unknown parameters. At each iteration, information is diffused across the network by a temporal update step and a spatial update step. Both steps update each node's state by a weighted average of its current value and locally available data: new measurements for the time update, and neighbors' data for the spatial update. At any time, any node can compute a local weighted least-squares estimate of the unknown parameters, which converges to the global maximum-likelihood solution. With an infinite number of measurements, these estimates converge to the true parameter values in the sense of mean-square convergence. We show that this scheme is robust to unreliable communication links, and works in a network with dynamically changing topology.

Original languageEnglish
Title of host publicationProceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Pages168-176
Number of pages9
DOIs
StatePublished - 2006
Externally publishedYes
EventFifth International Conference on Information Processing in Sensor Networks, IPSN '06 - Nashville, TN, United States
Duration: Apr 19 2006Apr 21 2006

Publication series

NameProceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Volume2006

Conference

ConferenceFifth International Conference on Information Processing in Sensor Networks, IPSN '06
Country/TerritoryUnited States
CityNashville, TN
Period04/19/0604/21/06

Keywords

  • Distributed algorithms
  • Estimation
  • Least-squares
  • Sensor networks

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