NDLI: Classification in sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Venkatesh, S. Alanyali, M. Savas, O. Aeron, S.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Boston Univ., MA, USA (Venkatesh, S.; Alanyali, M.; Savas, O.; Aeron, S.)
Abstract	We consider the problem of classifying among a set of M hypothesis with N distributed noisy sensors. The N sensors can collaborate over a finite link-capacity network. The task is to arrive at a consensus about the event after exchanging such messages. In contrast to the conventional decentralized detection approach, wherein the bit rates for each link is explicitly constrained, our approach is based on high-rate limit perspective. We apply a variant of belief propagation as a strategy for collaboration to arrive at a solution to the distributed classification problem. We show that the message evolution can be reformulated as the evolution of a linear dynamical system, which is primarily characterized by network connectivity. It turns out that consensus is almost always reached by the sensors for any arbitrary network. We then derive conditions under which the consensus is the centralized MAP estimate and show that this is achieved with O(M log/sub 2/ N) bits.
Sponsorship	IEEE Information Theory Soc
File Size	246971
File Format	PDF
ISBN	0780382803
DOI	10.1109/ISIT.2004.1365289
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent networks Sensor fusion Signal processing algorithms Collaboration Belief propagation Sensor phenomena and characterization Network topology Quantization Distributed computing Bit rate
Content Type	Text
Resource Type	Article

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