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

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.