Adaptive sensor placement and boundary estimation for monitoring mass objects

Zhen Guo, Meng Chu Zhou, Guofei Jiang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects.

Original languageEnglish (US)
Pages (from-to)222-232
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number1
DOIs
StatePublished - Feb 2008

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Expectation-maximization (EM)
  • Gaussian mixture model (GMM)
  • Learning
  • Maximum likelihood (ML)
  • Sensor networks
  • Sensor placement
  • Wireless sensor network

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