Compressed Sensing for Distributed Systems
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are rela
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Giulio Coluccia Chiara Ravazzi Enrico Magli
Compressed Sensing for Distributed Systems 123
SpringerBriefs in Electrical and Computer Engineering Signal Processing
Series editors Woon-Seng Gan, Singapore, Singapore C.-C. Jay Kuo, Los Angeles, USA Thomas Fang Zheng, Beijing, China Mauro Barni, Siena, Italy
More information about this series at http://www.springer.com/series/11560
Giulio Coluccia Chiara Ravazzi Enrico Magli •
Compressed Sensing for Distributed Systems
123
Giulio Coluccia Department of Electronics and Telecommunications Polytechnic University of Turin Turin Italy
Enrico Magli Department of Electronics and Telecommunications Polytechnic University of Turin Turin Italy
Chiara Ravazzi Department of Electronics and Telecommunications Polytechnic University of Turin Turin Italy
ISSN 2191-8112 ISSN 2191-8120 (electronic) SpringerBriefs in Electrical and Computer Engineering ISSN 2196-4076 ISSN 2196-4084 (electronic) SpringerBriefs in Signal Processing ISBN 978-981-287-389-7 ISBN 978-981-287-390-3 (eBook) DOI 10.1007/978-981-287-390-3 Library of Congress Control Number: 2015939233 Springer Singapore Heidelberg New York Dordrecht London © The Author(s) 2015 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 Science+Business Media Singapore Pte Ltd. is part of Springer Science+Business Media (www.springer.com)
Preface
Compressed sensing is a new technique for nonadaptive compressed acquisition, which takes advantage of signal sparsity and allows signal recovery starting from few linear measurements. Distributed scenarios commonly arise in many applications, where data are inherently scattered across a large geographical area. This applies, for example, to sparse event detection in wireless networks, distributed indoor localization, and distributed tracking in sensor networks. Also distributed sources naturally arise in wireless sensor networks, where sensors may acquire over time several readings of the same natural quantity,
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