Recursive online em algorithm for adaptive sensor deployment and boundary estimation in sensor networks

Zhen Guo, Mengchu Zhou, Guofei Jiang

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

1 Scopus citations

Abstract

More and more sensor networks are required to monitor and track a large number of objects. Since the topology of mass objects is often dynamic in the real world, their boundary estimation and sensor deployment should be conducted in an adaptive manner. The "current" locations of objects detected by sensors are deemed as new observations into stochastic learning process through recursive distributed EM (Expectation-Maximization) algorithm. This paper first builds a probabilistic Gaussian Mixture model to estimate the mixture distribution of objects locations and then proposes a novel methodology to optimize the sensor deployment and estimate the boundary of objects locations dynamically.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
Pages862-867
Number of pages6
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06 - Ft. Lauderdale, FL, United States
Duration: Apr 23 2006Apr 25 2006

Publication series

NameProceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06

Other

Other2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06
CountryUnited States
CityFt. Lauderdale, FL
Period4/23/064/25/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

Keywords

  • Boundary estimation
  • EM algorithm
  • Maximal likelihood
  • Sensor deployment

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