Analysis and design of robust max consensus for wireless sensor networks

Research output: Contribution to journalArticle

Abstract

A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus algebra is used as a tool to study this ergodic process. The subadditive ergodic theorem is invoked to establish a constant growth rate for the state values due to noise, which is studied by analyzing the max-plus Lyapunov exponent of the product of noise matrices in a max-plus semiring. The growth rate of the state values is upper bounded by a constant which depends on the spectral radius of the network and the noise variance. Upper and lower bounds are derived for both fixed and random graphs. Finally, a two-run algorithm robust to additive noise in the network is proposed and its variance is analyzed using concentration inequalities. Simulation results supporting the theory are also presented.

Original languageEnglish (US)
Article number8859263
Pages (from-to)779-791
Number of pages13
JournalIEEE Transactions on Signal and Information Processing over Networks
Volume5
Issue number4
DOIs
StatePublished - Dec 2019

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Wireless sensor networks
Additive noise
Parallel algorithms
Algebra
Communication

Keywords

  • Max consensus
  • max-plus algebra
  • spectral radius
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications

Cite this

Analysis and design of robust max consensus for wireless sensor networks. / Muniraju, Gowtham; Tepedelenlioglu, Cihan; Spanias, Andreas.

In: IEEE Transactions on Signal and Information Processing over Networks, Vol. 5, No. 4, 8859263, 12.2019, p. 779-791.

Research output: Contribution to journalArticle

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