Stochastic Adaptive Sampling for Mobile Sensor Networks using Kernel Regression

被引：0
|
作者：
Xu, Yunfei [1 ]
Choi, Jongeun [1 ]
机构：
[1] Michigan State Univ, Dept Mech Engn, E Lansing, MI 48824 USA
来源：
2010 AMERICAN CONTROL CONFERENCE | 2010年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2897 / 2902
页数：6
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