DocumentCode :
1395126
Title :
Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks
Author :
Xu, Yunfei ; Choi, Jongeun ; Dass, Sarat ; Maiti, Tapabrata
Author_Institution :
Dept. of Mech. Eng., Michigan State Univ., East Lansing, MI, USA
Volume :
57
Issue :
8
fYear :
2012
Firstpage :
2078
Lastpage :
2084
Abstract :
In this technical note, we formulate a fully Bayesian approach for spatio-temporal Gaussian process regression such that multifactorial effects of observations, measurement noise and prior distributions are all correctly incorporated in the predictive distribution. Using discrete prior probabilities and compactly supported kernels, we provide a way to design sequential Bayesian prediction algorithms in which exact predictive distributions can be computed in constant time as the number of observations increases. For a special case, a distributed implementation of sequential Bayesian prediction algorithms has been proposed for mobile sensor networks. An adaptive sampling strategy for mobile sensors, using the maximum a posteriori (MAP) estimation, has been proposed to minimize the prediction error variances. Simulation results illustrate the practical usefulness of the proposed theoretically-correct algorithms.
Keywords :
Bayes methods; Gaussian processes; maximum likelihood estimation; mobile radio; sampling methods; wireless sensor networks; MAP estimation; adaptive sampling algorithms; compact supported kernels; discrete prior probabilities; maximum a posteriori estimation; measurement noise; mobile sensor networks; multifactorial effects; observations; sequential Bayesian prediction algorithms; spatio-temporal Gaussian process regression; Bayesian methods; Correlation; Gaussian processes; Mobile communication; Noise; Prediction algorithms; Sensors; Adaptive sampling; Bayesian prediction; Gaussian processes; mobile sensor networks;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.2011.2179430
Filename :
6099560
Link To Document :
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