Stochastic adaptive sampling for mobile sensor networks using kernel regression

Yunfei Xu, Jongeun Choi

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


In this paper, we provide a stochastic adaptive sampling strategy for mobile sensor networks to estimate scalar fields over surveillance regions using kernel regression, which does not require a priori statistical knowledge of the field. Our approach builds on a Markov Chain Monte Carlo (MCMC) algorithm, viz., the fastest mixing Markov chain under a quantized finite state space, for generating the optimal sampling probability distribution asymptotically. The proposed adaptive sampling algorithm for multiple mobile sensors is numerically evaluated under scalar fields. The comparison simulation study with a random walk benchmark strategy demonstrates the excellent performance of the proposed scheme.

Original languageEnglish
Pages (from-to)778-786
Number of pages9
JournalInternational Journal of Control, Automation and Systems
Issue number4
Publication statusPublished - 2012 Aug

Bibliographical note

Funding Information:
This work has been supported by the National Science Foundation through CAREER Award CMMI-0846547. This support is gratefully acknowledged.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications


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