NDLI: Cooperative and distributed algorithm for compressed sensing recovery in WSNs

Content Provider	IET Digital Library
Author	Azarnia, Ghanbar Tinati, Mohammad Ali Rezaii, Tohid Yousefi
Abstract	Wireless sensor networks (WSNs) could benefit a lot from compressive sensing (CS). Inherent physical structure of sensors of WSNs (battery-powered devices) demands computational-efficient algorithms with no heavy burden on a small subset of the sensors, i.e. fusion sensors. This could be achieved by distributed algorithms in which computation is distributed among all sensor nodes. On this basis, in this study, the authors have proposed a distributed and cooperative sparse recovery algorithm in which each sensor decodes a sparse signal by running a recovery algorithm with the cooperation of its neighbours. The proposed algorithm has a general structure and can be adapted to many optimisation algorithms in the context of the CS. This algorithm is completely distributed and requires an acceptable computational complexity that is suitable for WSNs. A detailed proof of convergence behaviour of the proposed algorithm is also presented. The superiority of the proposed algorithm compared with similar methods in terms of recovery quality and convergence rate is confirmed through simulation.
Starting Page	346
Ending Page	357
Page Count	12
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 3, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2017.0093
Journal	IET Signal Processing
Publisher Date	2017-10-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Battery-powered Device Compressed Sensing Compressed Sensing Recovery Compressive Sensing Computational Complexity Convergence Rate Cooperative Sparse Recovery Algorithm Distributed Sparse Recovery Algorithm Fusion Sonsor Optimisation Optimisation Algorithm Optimisation Technique Recovery Quality Signal Processing And Detection Sparse Signal Wireless Sensor Network WSN
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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