DocumentCode :
87558
Title :
Compressive Sensing Optimization for Signal Ensembles in WSNs
Author :
Caione, Carlo ; Brunelli, Davide ; Benini, Luca
Author_Institution :
Dept. of Electr., Electron., & Inf. Eng. (DEI), Univ. of Bologna, Bologna, Italy
Volume :
10
Issue :
1
fYear :
2014
fDate :
Feb. 2014
Firstpage :
382
Lastpage :
392
Abstract :
Compressive sensing (CS) is a new approach to simultaneous sensing and compressing that is highly promising for fully distributed compression in wireless sensor networks (WSNs). While a wide investigation has been performed about theory and practice of CS for individual signals, real and practical cases, in general, involve multiple signals, extending the problem of compression from 1-D single-sensor to 2-D multiple-sensors data. In this paper the two most prominent frameworks on sparsity and compressibility of multidimensional signals and signal ensembles, Distributed compressed sensing (DCS) and Kronecker compressive sensing (KCS), are investigated. In this paper we compare these two frameworks against a common set of artificial signals properly built to embody the main characteristics of natural signals. We further investigate how, in a real deployment, DCS can be used to reduce the power consumption and to prolong lifetime. In particular an extensive analysis is performed using real commercial off-the-shelf (COTS) hardware evaluating how different kind of compression matrices can affect the jointly reconstruction, trying to achieve the better tradeoff between quality and energy expenditure.
Keywords :
compressed sensing; matrix algebra; signal reconstruction; wireless sensor networks; 1D single-sensor; 2D multiple-sensor data; COTS hardware; DCS; KCS; Kronecker compressive sensing; WSN; artificial signals; compression matrices; compressive sensing optimization; distributed compressed sensing; fully-distributed compression; joint reconstruction; multidimensional signal compressibility; multidimensional signal sparsity; natural signals; power consumption reduction; quality-energy expenditure tradeoff; real commercial off-the-shelf hardware; signal ensembles; simultaneous sensing; wireless sensor networks; Compressed sensing; data compression; embedded software; low-power electronics; wireless sensor networks;
fLanguage :
English
Journal_Title :
Industrial Informatics, IEEE Transactions on
Publisher :
ieee
ISSN :
1551-3203
Type :
jour
DOI :
10.1109/TII.2013.2266097
Filename :
6523111
Link To Document :
بازگشت