Estimation of spatially distributed processes in wireless sensor networks with random packet loss

Priyadip Ray, Pramod K. Varshney

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

This paper studies the effect of wireless channel imperfections on the transport and estimation of spatially distributed events using wireless sensor networks (WSNs). It is observed that the quality of event estimation at the sink (fusion center) degrades considerably with correlated packet losses during transmission from the sensors. A novel diversity technique based on field estimation is proposed to mitigate the effects of packet losses on the quality of estimation at the sink. Dense deployment of sensor nodes and the spatial nature of the observed physical phenomenon result in the sensor observations being noisy spatial samples of an unknown underlying function. The proposed algorithm exploits this feature, using supervised learning to achieve diversity. A new information fusion methodology based on approximate likelihood is proposed to integrate the information obtained from the learning algorithm into the classical estimation framework. Simulation results are provided to demonstrate the performance of the proposed approach.

Original languageEnglish (US)
Article number5089997
Pages (from-to)3162-3171
Number of pages10
JournalIEEE Transactions on Wireless Communications
Volume8
Issue number6
DOIs
StatePublished - Jun 1 2009

    Fingerprint

Keywords

  • Bootstrap
  • Field estimation
  • Information fusion
  • Spatially distributed processes
  • Supervised learning
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this