Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. The physical spatial field of interest is discretized and modeled by a Gaussian Markov random field (GMRF) with unknown hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and also is scalable to be usable for the mobile sensor networks with limited resources. An adaptive sampling strategy is also designed for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. The effectiveness of the proposed algorithms is illustrated by a numerical experiment.

Original languageEnglish
Title of host publication2012 American Control Conference, ACC 2012
Pages2171-2176
Number of pages6
Publication statusPublished - 2012 Nov 26
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: 2012 Jun 272012 Jun 29

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2012 American Control Conference, ACC 2012
CountryCanada
CityMontreal, QC
Period12/6/2712/6/29

Fingerprint

Sensor networks
Wireless networks
Sampling
Experiments
Uncertainty

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Xu, Y., Choi, J., Dass, S., & Maiti, T. (2012). Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. In 2012 American Control Conference, ACC 2012 (pp. 2171-2176). [6315013] (Proceedings of the American Control Conference).
Xu, Yunfei ; Choi, Jongeun ; Dass, Sarat ; Maiti, Tapabrata. / Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. 2012 American Control Conference, ACC 2012. 2012. pp. 2171-2176 (Proceedings of the American Control Conference).
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Xu, Y, Choi, J, Dass, S & Maiti, T 2012, Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. in 2012 American Control Conference, ACC 2012., 6315013, Proceedings of the American Control Conference, pp. 2171-2176, 2012 American Control Conference, ACC 2012, Montreal, QC, Canada, 12/6/27.

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. / Xu, Yunfei; Choi, Jongeun; Dass, Sarat; Maiti, Tapabrata.

2012 American Control Conference, ACC 2012. 2012. p. 2171-2176 6315013 (Proceedings of the American Control Conference).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Xu Y, Choi J, Dass S, Maiti T. Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. In 2012 American Control Conference, ACC 2012. 2012. p. 2171-2176. 6315013. (Proceedings of the American Control Conference).