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

Xu, Yunfei; Choi, Jongeun; Dass, Sarat C.; Maiti, Tapabrata

doi:10.1016/j.automatica.2013.09.008

Cited by 49 publications

(64 citation statements)

References 24 publications

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“…In dynamic topology evolution process, the transmission delay of multimedia data can be obtained by the formula (9). The delay includes the following three parts: (1) the prediction delay with opportunistic Markov chain model, (2) cooperative transmission delay of mobile node, and (3) opportunistic cloud computing delay.…”

Section: Mobility Prediction Schemementioning

confidence: 99%

“…So, Xu Y.F. et al [9] proposed a prediction scheme, which could predict a large-scale spatial field using successive noisy measurements obtained by mobile-sensing agents. The correlation between some available design measurements and class stability over versions was investigated in article [10], which proposed a stability prediction model using such available measurements.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Zhou

Dong

2015

J Wireless Com Network

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According to the loss network problems caused by limited resources and environment interference factors, we presented a survivability mechanism based on mobility prediction and based on the topological evolution of cloud computing wireless multimedia sensor network (WMSN). First, based on cooperative neural network and opportunity Markov chain model, the multimedia mobile sensor node state and state transition probability could be predicted. Then, the opportunity computational scheme was given based on mobility prediction of dynamic topology evolution mechanism. Finally, using the network topology reconfiguration and opportunities for cloud computing, an enhanced WMSN survivability and end-to-end quality of service guarantee mechanism was proposed. Experimental results show that compared with the static scheme for WMSNs, the proposed survivability mechanism has the obvious advantage in the node protection, data communication, network life cycle, and decodable frame rate and other aspects, and can effectively improve the invulnerability of WMSNs.

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Section: Mobility Prediction Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Zhou

Dong

2015

J Wireless Com Network

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“…However, the estimation of a spatiotemporal field has been a topic of interest in its own right, especially with an increasing exploitation of mobile robot sensor networks interacting with uncertain environments [14][15][16][17][18][19][20][21]. To tackle a variety of tasks such as exploration, estimation, prediction, and maximum seeking of a scalar field, it is essential to deal with spatial models of various physical fields.…”

Section: Introductionmentioning

confidence: 99%

“…Computationally demanding, physics-based field models (e.g., atmospheric modeling [22]) have been developed. Recently, phenomenological and statistical modeling techniques such as kriging, kernel regression, Gaussian process regression, and Gaussian Markov random fields (GMRFs) have gained much attention for resource-constrained mobile robots [14][15][16][17][18][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

Choi

Milutinović

2014

Journal of Dynamic Systems, Measurement, and Control

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This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.

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“…Although the GMRF was used to design an adaptive sampling strategy for mobile sensor networks in [29], Xu et al restricted their solutions to a regular lattice in which the hyperparameters were chosen a priori from a discrete support set. Hence, in this work we consider the GMRF model represented on an irregular lattice, where the model parameters are learned from all available observations.…”

Section: Introductionmentioning

confidence: 99%

Spatial Sensor Selection via Gaussian Markov Random Fields

Nguyen

Kodagoda

Ranasinghe

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-The paper addresses the problem of selecting the most informative sensor locations out of all possible sensing positions in prediction of spatial phenomena by using a wireless sensor network. The spatial field is modelled by Gaussian Markov random fields (GMRF), where sparsity of the precision matrix enables the network to benefit from computation. A new spatial sensor selection criterion is proposed based on mutual information between random variables at a selected locations and those at unselected locations and interested but unlikely sensor placed positions, which enhances resulting prediction. The GMRF based optimality criterion is then proved to be very computationally efficiently resolved, especially in a large-scale sensor network, by a polynomial time approximation algorithm. More importantly, with demonstrations of monotonicity and submodularity properties of the mutual information set function in the proposed selection criterion, our near-optimal solution is also guaranteed by at least within (1 − 1/e) of the optimal performance. The effectiveness of the proposed approach is compared and illustrated using two real-life large data sets with promising results.

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

Cited by 49 publications

References 24 publications

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

RETRACTED ARTICLE: Invulnerability mechanism based on mobility prediction and opportunistic cloud computing with topological evolution for wireless multimedia sensor networks

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

Spatial Sensor Selection via Gaussian Markov Random Fields

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