Bayesian Detection in Bounded Height Tree Networks
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Tay-et_al-Bayesian.pdf
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282.79 KB
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Author(s)
Tay, Wee Peng
Win, Moe Z.
Tsitsiklis, John N.
Date Issued
October 2009
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers
Citation
Bayesian Detection in Bounded Height Tree Networks
Wee Peng Tay; Tsitsiklis, J.N.; Win, M.Z.;
Signal Processing, IEEE Transactions on
Volume 57, Issue 10, Oct. 2009 Page(s):4042 - 4051
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dx.doi.org/10.1109/tsp.2009.2023374
