Stochastic adaptive sampling for mobile sensor networks using kernel regression

Yunfei Xu, Jongeun Choi

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

Abstract

In this paper, we provide a stochastic adaptive sampling strategy for mobile sensor networks to estimate scalar fields over a surveillance region using kernel regression. Our approach builds on a Markov Chain Monte Carlo (MCMC) algorithm particularly known as the Fastest Mixing Markov Chain (FMMC) under a quantized finite state space for generating the optimal sampling probability distribution asymptotically. An adaptive sampling algorithm for multiple mobile sensors is designed and numerically evaluated under a complicated scalar field. The comparison simulation study with a random walk benchmark strategy demonstrates the good performance of the proposed scheme.

Original languageEnglish
Title of host publicationProceedings of the 2010 American Control Conference, ACC 2010
Pages2897-2902
Number of pages6
Publication statusPublished - 2010 Oct 15
Event2010 American Control Conference, ACC 2010 - Baltimore, MD, United States
Duration: 2010 Jun 302010 Jul 2

Publication series

NameProceedings of the 2010 American Control Conference, ACC 2010

Other

Other2010 American Control Conference, ACC 2010
CountryUnited States
CityBaltimore, MD
Period10/6/3010/7/2

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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  • Cite this

    Xu, Y., & Choi, J. (2010). Stochastic adaptive sampling for mobile sensor networks using kernel regression. In Proceedings of the 2010 American Control Conference, ACC 2010 (pp. 2897-2902). [5531511] (Proceedings of the 2010 American Control Conference, ACC 2010).