|Stochastic Adaptive Sampling for Mobile Sensor Networks using Kernel Regression
Yunfei Xu and Jongeun Choi*
International Journal of Control, Automation, and Systems, vol. 10, no. 4, pp.778-786, 2012
Abstract : 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.
Fastest mixing reversible markov chain, mobile sensor networks, stochastic adaptive sampling.
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