Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Online Environmental Field Reconstruction in Space and Time

Nonfiction, Science & Nature, Technology, Automation, Mathematics, Statistics
Cover of the book Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti ISBN: 9783319219219
Publisher: Springer International Publishing Publication: October 27, 2015
Imprint: Springer Language: English
Author: Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
ISBN: 9783319219219
Publisher: Springer International Publishing
Publication: October 27, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Nature, Tourism and Ethnicity as Drivers of (De)Marginalization by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book The Soybean Genome by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Resistance Behavior to National eHealth Implementation Programs by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book North-South University Research Partnerships in Latin America and the Caribbean by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Pervasive Computing by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Using Risk Analysis for Flood Protection Assessment by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Cooperative Cognitive Radio Networking by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Ubiquitous Networking by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Thermal-Hydraulic Analysis of Nuclear Reactors by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Is Corruption Curable? by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book International Conference on Oriental Thinking and Fuzzy Logic by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book From Financial Crisis to Social Change by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book English as Medium of Instruction in Japanese Higher Education by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Rheological and Seismic Properties of Solid-Melt Systems by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Cover of the book Resistance of Cancer Cells to CTL-Mediated Immunotherapy by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy