Distributed binary quantizers for communication constrained large-scale sensor networks

Ying Lin, Biao Chen, Peter Willett, Bruce Suter

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

3 Scopus citations

Abstract

We consider in this paper local sensor quantizer design for large-scale bandwidth and/or energy constrained wireless sensor networks (WSNs) operating in fading channels. In particular, under the Neyman-Pearson framework, we address the design of binary local sensor quantizers for a binary hypothesis problem in the asymptotic regime where the number of sensors is large. Motivated by the sensor censoring idea for reduced communication rate, each sensor either transmits '1' to a fusion center or remains silent. By adopting energy detector as the fusion rule, we develop a procedure to obtain local sensor threshold that maximizes the Kullback-Leibler distance of the distributions of the fusion statistic under the two hypotheses. The proposed quantizerdesign is well suited for the emerging large scale resource-constrained WSNs applications. Numerical results based on Gaussian and exponential observations are presented to demonstrate the design procedure.

Original languageEnglish (US)
Title of host publication2006 9th International Conference on Information Fusion, FUSION
DOIs
StatePublished - Dec 1 2006
Event2006 9th International Conference on Information Fusion, FUSION - Florence, Italy
Duration: Jul 10 2006Jul 13 2006

Publication series

Name2006 9th International Conference on Information Fusion, FUSION

Other

Other2006 9th International Conference on Information Fusion, FUSION
CountryItaly
CityFlorence
Period7/10/067/13/06

Keywords

  • Asymptotic regime
  • Censoring sensors
  • Distributed detection
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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  • Cite this

    Lin, Y., Chen, B., Willett, P., & Suter, B. (2006). Distributed binary quantizers for communication constrained large-scale sensor networks. In 2006 9th International Conference on Information Fusion, FUSION [4086030] (2006 9th International Conference on Information Fusion, FUSION). https://doi.org/10.1109/ICIF.2006.301744