The embedded triangles algorithm for distributed estimation in sensor networks

V. Delouille, R. Neelamani, V. Chandrasekaran, R. G. Baraniuk

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

8 Scopus citations

Abstract

We propose a new iterative distributed estimation algorithm for Gaussian hidden Markov graphical models with loops. We decompose a loopy graph into a number of linked embedded triangles and then apply a parallel block-Jacobi iteration comprising local linear minimum mean-square-error estimation on each triangle (involving a simple 3x3 matrix inverse computation) followed by an information exchange between neighboring nodes and triangles. A simulation study demonstrates that the algorithm converges extremely rapidly, outperforming a number of existing algorithms. Embedded triangles are simple, local, scalable, fault-tolerant, and energy-efficient, and thus ideally suited for wireless sensor networks.

Original languageEnglish (US)
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages371-374
Number of pages4
Volume2003-January
ISBN (Print)0780379977
DOIs
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Other

OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
CountryUnited States
CitySt. Louis
Period9/28/0310/1/03

Keywords

  • Computational modeling
  • Concurrent computing
  • Embedded computing
  • Energy efficiency
  • Fault tolerance
  • Graphical models
  • Hidden Markov models
  • Iterative algorithms
  • Matrix decomposition
  • Wireless sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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