Distributed Detection of Dynamic Event Regions in Sensor Networks with a Gibbs Field Distribution and Gaussian Corrupted Measurements

Tsang Yi Wang, Ming Hsun Yang, Jwo-Yuh Wu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Various methods have been proposed for monitoring continuously varying event-region scenarios using wireless sensor networks (WSNs). However, these methods use either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive real-value data exchange takes place among the sensors. By contrast, this paper proposes a two-phase distributed dynamic event-region detection scheme for WSNs characterized by a space-time Markov random field with a particular Gibbs distribution. In Phase I of the proposed scheme, each sensor makes an initial decision in accordance with its current Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions based on the decision information received from their neighbors. Notably, the proposed scheme has a low-communication-rate characteristic, and is thus ideally suited to WSN applications. The performance of the proposed scheme is compared with that of a semicentralized detection scheme by means of computer simulations.

Original languageEnglish
Article number7517348
Pages (from-to)3932-3945
Number of pages14
JournalIEEE Transactions on Communications
Volume64
Issue number9
DOIs
StatePublished - 1 Sep 2016

Keywords

  • change detection
  • Cooperative fusion
  • dynamic event
  • event-region detection
  • Markov random field

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