PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Decentralized Detection with Energy-Aware Greedy Selective Sensors
Jesus Fernandez-Bes and Jesus Cid-Sueiro
In: 3rd International Workshop on Cognitive Information Processing (CIP), 2012, 28-30 May 2012, Bayona-Vigo, Spain.

Abstract

The decentralized detection of events is a primary task in many applications of wireless sensor networks. Since energy consumption is the main constraint in networks of battery powered sensors, as it limits their lifetime, taking explicitly into account the energy costs in the design of any decentralized detection algorithm becomes a major issue. Based on state-of-the art censoring techniques and a selective communications framework we develop an energy-aware decentralized detection scheme that, in a greedy fashion, makes an efficient use of the energy resources of a detection network during its lifetime. Simulated scenarios with different number of nodes, consumption patterns and signal models have shown the high gain of our scheme over the uncensored ones in the network lifetime-detection accuracy compromise.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9627
Deposited By:Jesus Cid-Sueiro
Deposited On:06 December 2012