TY - GEN
T1 - Stochastic adaptive sampling for mobile sensor networks using kernel regression
AU - Xu, Yunfei
AU - Choi, Jongeun
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77957803876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957803876&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:77957803876
SN - 9781424474264
T3 - Proceedings of the 2010 American Control Conference, ACC 2010
SP - 2897
EP - 2902
BT - Proceedings of the 2010 American Control Conference, ACC 2010
T2 - 2010 American Control Conference, ACC 2010
Y2 - 30 June 2010 through 2 July 2010
ER -