Decision-making in distributed sensor networks: A belief-theoretic Bayes-like theorem

K. Premaratne, J. Zhang, K. K.R.G.K. Hewawasam

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

A Dempster-Shafer (DS) belief theoretic evidence updating strategy is ideally suited to accommodate the difficulties associated with the availability of only incomplete information at each node of a distributed sensor network (DSN). Such a strategy however must also account for sensor heterogeneity, 'inertia' and 'integrity' of the existing knowledge base and reliability of the data generated at each sensor node. In this paper, we propose a Bayes-like theorem that can conveniently address these issues while allowing one to compute the 'posterior' belief of a 'hypothesis' given an 'observation' when the corresponding 'likelihoods' and 'priors' are available. Unlike previous work on DS belief theoretic generalizations of Bayes' theorem, our work is based on the Fagin-Halpern conditional notions that can be considered more 'natural extensions' of corresponding Bayesian notions.

Original languageEnglish (US)
Pages (from-to)II497-II500
JournalMidwest Symposium on Circuits and Systems
Volume2
StatePublished - Dec 1 2004
EventThe 2004 47th Midwest Symposium on Circuits and Systems - Conference Proceedings - Hiroshima, Japan
Duration: Jul 25 2004Jul 28 2004

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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