Bayesian detection in bounded height tree networks

Authors:
Wee Peng Tay;John N. Tsitsiklis;Moe Z. Win
Affiliations:
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA;Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA
Venue:
IEEE Transactions on Signal Processing
Year:
2009

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Abstract

We study the detection performance of large scale sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that, under a Bayesian formulation, the error probability decays exponentially fast, and we provide bounds for the error exponent. We then focus on the case where the tree has certain symmetry properties. We derive the form of the optimal exponent within a restricted class of easily implementable strategies, as well as optimal strategies within that class. We also find conditions under which (suitably defined) majority rules are optimal. Finally, we provide evidence that in designing a network it is preferable to keep the branching factor small for nodes other than the neighbors of the leaves.