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OPTIMALITY OF EXPECTATION PROPAGATION BASED DISTRIBUTED ESTIMATION FOR WIRELESS SENSOR NETWORK INITIALIZATION
Conference proceeding

OPTIMALITY OF EXPECTATION PROPAGATION BASED DISTRIBUTED ESTIMATION FOR WIRELESS SENSOR NETWORK INITIALIZATION

John MacLaren Walsh, S. Ramanan, Phillip A. Regalia and IEEE
2008 IEEE 9TH WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, VOLS 1 AND 2, pp 620-624
01 Jan 2008

Abstract

Engineering Engineering, Electrical & Electronic Science & Technology Technology Telecommunications
We establish that expectation propagation (EP), under some mild requirements and when properly organized, provides sensors with optimal Bayes estimators during the initialization phase of a large randomly deployed wireless sensor network, regardless of the cost function chosen. We are considering the initialization phase to be the period during which the sensors do not yet know their locations and channel/interference strengths, and thus must use random sleep schedules until they have estimated them. During this initialization phase, any other scheme for distributed Bayesian estimation utilizing communication among the same nodes must have equal or worse performance to EP. We discuss the sub-optimality of some other proposed schemes for distributed estimation in sensor networks: consensus propagation and distributed adaptive filtering, arguing that these techniques may presently be seen as seeking suboptimal performance among particular cost functions and with a goal of reduced computation and complexity relative to EP.

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Collaboration types
Domestic collaboration
Web of Science research areas
Engineering, Electrical & Electronic
Telecommunications
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