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.
|Number of pages||9|
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2012 Aug|
Bibliographical noteFunding 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