Distributed average consensus using bounded transmissions

Sivaraman Dasarathan, Mahesh Banavar, Cihan Tepedelenlioglu, Andreas Spanias

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

1 Scopus citations

Abstract

A distributed consensus algorithm in which every sensor maps its state value through a bounded function before transmission is proposed. It is shown that when the step size of the algorithm is chosen appropriately, the state values of all the nodes converge exponentially to the sample average of the initial observations provided that the transmission function has a bounded first derivative. The convergence factor is shown to depend on the derivative of the transmission function. The performance of various bounded transmission functions are studied through simulations. It is shown that by appropriately choosing the step size, the proposed algorithm could achieve the same speed of convergence as that of the best case linear consensus algorithm based on the Laplacian heuristic.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages1202-1206
Number of pages5
DOIs
StatePublished - Dec 1 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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