Distributed adaptive quantization for wireless sensor networks

Jun Fang, Hongbin Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We investigate the problem of distributed parameter estimation under the most stringent bandwidth constraint that each sensor quantizes its local observation into one bit of information. Conventional fixed quantization (FQ) approaches, which employ a fixed threshold for all sensors, incur an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To address this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, with sensors sequentially broadcasting their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, three adaptive schemes are presented in this paper. The maximum likelihood (ML) estimators associated with these three AQ schemes are developed and their corresponding Cramér-Rao bounds (CRBs) are analyzed. The analysis shows that our proposed one-bit AQ approach can asymptotically attain an estimation variance as least as only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations. Numerical results are illustrated to show the effectiveness of the proposed approach and to corroborate our claim.

Original languageEnglish
Title of host publicationConference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages1372-1376
Number of pages5
DOIs
StatePublished - 2007
Event41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States
Duration: 4 Nov 20077 Nov 2007

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/TerritoryUnited States
CityPacific Grove, CA
Period4/11/077/11/07

Keywords

  • Adaptive quantization (AQ)
  • Distributed estimation
  • Wireless sensor networks (WSNs).

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