Non-linear distributed average consensus using bounded transmissions

Sivaraman Dasarathan, Cihan Tepedelenliolu, Mahesh K. Banavar, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.

Original languageEnglish (US)
Article number6605593
Pages (from-to)6000-6009
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume61
Issue number23
DOIs
StatePublished - Nov 27 2013

Keywords

  • Asymptotic Covariance
  • Bounded Transmissions
  • Distributed Consensus
  • Markov Processes
  • Sensor Networks
  • Stochastic Approximation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Non-linear distributed average consensus using bounded transmissions'. Together they form a unique fingerprint.

Cite this