Distributed measurement censoring for estimation with wireless sensor networks

Eric J. Msechu, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Motivated by the savings in communication bandwidth and sensor transmission energy, data selection for estimation with wireless sensor networks is investigated in this paper. Existing approaches to data selection inherently treat sensing and transmission to a central fusion unit as of equal cost. However, energy expenditure in sensing is generally a fraction of that needed for communication. To alleviate the latter, measurement censoring at sensor nodes is proposed here for data reduction, along with a novel maximum likelihood estimator that optimally incorporates knowledge of the censored data model. Furthermore, a closed-form expression for the Cramér-Rao lower bound on the estimator variance is presented. Numerical studies show that the estimator using censored measurements achieves error values that are competitive with alternative methods, under various sensing conditions, while retaining lower computational complexity.

Original languageEnglish (US)
Title of host publication2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2011
Pages176-180
Number of pages5
DOIs
StatePublished - Sep 16 2011
Event2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2011 - San Francisco, CA, United States
Duration: Jun 26 2011Jun 29 2011

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Other

Other2011 IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2011
CountryUnited States
CitySan Francisco, CA
Period6/26/116/29/11

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