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

Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)


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 uncertain hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The main advantages of the proposed algorithm are: (1) the computational efficiency due to the sparse structure of the precision matrix, and (2) the scalability as the number of measurements increases. Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented 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 numerical experiments.
Original languageEnglish
Pages (from-to)3520-3530
Number of pages11
Issue number12
Publication statusPublished - Dec 2013


  • Gaussian Markov random fields
  • Mobile sensor networks


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