Sensor Selection for Target Tracking in Wireless Sensor Networks With Uncertainty

Nianxia Cao, Sora Choi, Engin Masazade, Pramod Kumar Varshney

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

In this paper, we propose a multiobjective optimization framework for the sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The uncertainties of the WSNs result in a set of sensor observations with insufficient information about the target. We propose a novel mutual information upper bound (MIUB)-based sensor selection scheme, which has a low computational complexity, same as the Fisher information (FI)-based sensor selection scheme, and gives an estimation performance similar to the mutual information-based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the multiobjective optimization problem (MOP) gives a set of sensor selection strategies that reveal different tradeoffs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Illustrative numerical results that provide valuable insights are presented.

Original languageEnglish (US)
Article number7524035
Pages (from-to)5191-5204
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume64
Issue number20
DOIs
StatePublished - Oct 15 2016

Keywords

  • Fisher information (FI)
  • information fusion
  • multiobjective optimization
  • mutual information (MI)
  • sensor selection
  • Target tracking
  • wireless sensor networks (WSNs)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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