Cooperative and distributed algorithm for compressed sensing recovery in WSNs. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Azarnia, Ghanbar; Tinati, Mohammad Ali; Rezaii, Tohid Yousefi
• Is Part Of: IET signal processing. Volume 12:Issue 3(2018)
• Page Start: 346
• Page End: 357
• Publication Date: 2018-05-01
• Subjects: wireless sensor networks -- compressed sensing -- optimisation -- computational complexity; compressed sensing recovery -- WSN -- wireless sensor networks -- compressive sensing -- battery‐powered devices -- fusion sensors -- cooperative sparse recovery algorithm -- distributed sparse recovery algorithm -- sparse signal -- optimisation algorithm -- computational complexity -- recovery quality -- convergence rate; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2017.0093
• Languages: English
• ISSNs: 1751-9675

Abstract

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.