Stochastic Adaptive Sampling for Mobile Sensor Networks using Kernel Regression

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.