Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks
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
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Yunfei Xu Jongeun Choi Sarat Dass Tapabrata Maiti
Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks Online Environmental Field Reconstruction in Space and Time 123
SpringerBriefs in Electrical and Computer Engineering Control, Automation and Robotics
Series editors Tamer Başar Antonio Bicchi Miroslav Krstic
More information about this series at http://www.springer.com/series/10198
Yunfei Xu Jongeun Choi Sarat Dass Tapabrata Maiti •
•
Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks Online Environmental Field Reconstruction in Space and Time
123
Yunfei Xu Michigan State University East Lansing, MI USA Jongeun Choi Michigan State University East Lansing, MI USA
Sarat Dass Department of Statistics Michigan State University East Lansing, MI USA Tapabrata Maiti Department of Statistics Michigan State University East Lansing, MI USA
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISSN 2192-6786 ISSN 2192-6794 (electronic) SpringerBriefs in Control, Automation and Robotics ISBN 978-3-319-21920-2 ISBN 978-3-319-21921-9 (eBook) DOI 10.1007/978-3-319-21921-9 Library of Congress Control Number: 2015950872 Springer Cham Heidelberg New York Dordrecht London © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
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Preface
We have witnessed a surge of applications using static or mobile sensor networks interacting with uncertain environments. To treat a variety of useful tasks such as environmental monitoring, adaptive sampling, surveillance, and exploration, this book introduces a class of problems and efficient spatio-temporal models when scalar fields need to be predicted from noisy observations collected by mobile sensor networks. The book discusses how to make inference fr
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