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Bayesian prediction and adaptive sam...

Xu, Yunfei.

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Bayesian prediction and adaptive sampling algorithms for mobile sensor networks = online environmental field reconstruction in space and time /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian prediction and adaptive sampling algorithms for mobile sensor networks/ by Yunfei Xu ... [et al.].
Reminder of title:	online environmental field reconstruction in space and time /
other author:	Xu, Yunfei.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xii, 115 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion.
Contained By:	Springer eBooks
Subject:	Sensor networks. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-21921-9
ISBN:	9783319219219$q(electronic bk.)

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks = online environmental field reconstruction in space and time /
Bayesian prediction and adaptive sampling algorithms for mobile sensor networksonline environmental field reconstruction in space and time /[electronic resource] :by Yunfei Xu ... [et al.]. - Cham :Springer International Publishing :2016. - xii, 115 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering. Control, automation and robotics,2191-8112. - SpringerBriefs in electrical and computer engineering.Control, automation and robotics..

Introduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion.

This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.

ISBN: 9783319219219$q(electronic bk.)

Standard No.: 10.1007/978-3-319-21921-9doiSubjects--Topical Terms:

581965
Sensor networks.

LC Class. No.: TK7872.D48 / X8 2016

Dewey Class. No.: 681.2

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks = online environmental field reconstruction in space and time /
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245 0 0 $a Bayesian prediction and adaptive sampling algorithms for mobile sensor networks $h [electronic resource] : $b online environmental field reconstruction in space and time / $c by Yunfei Xu ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xii, 115 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering. Control, automation and robotics, $x 2191-8112
505 0 $a Introduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion.
520 $a This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
650 0 $a Sensor networks. $3 581965
650 0 $a Bayesian statistical decision theory. $3 551404
650 1 4 $a Engineering. $3 586835
650 2 4 $a Control, Robotics, Mechatronics. $3 1002220
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Communications Engineering, Networks. $3 891094
700 1 $a Xu, Yunfei. $3 2179910
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $p Control, automation and robotics. $3 1622177
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21921-9
950 $a Engineering (Springer-11647)