University of Oulu

Ghanbar Azarnia, Mohammad Ali Tinati, Abbas Ali Sharifi, Hamid Shiri, Incremental and diffusion compressive sensing strategies over distributed networks, Digital Signal Processing, Volume 101, 2020, 102732, ISSN 1051-2004, https://doi.org/10.1016/j.dsp.2020.102732

Incremental and diffusion compressive sensing strategies over distributed networks

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Author: Azarnia, Ghanbar1; Tinati, Mohammad Ali2; Sharifi, Abbas Ali3;
Organizations: 1ngineering Faculty of Khoy, Urmia University, Urmia, Iran
2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
3Department of Electrical Engineering, University of Bonab, Bonab, Iran
4Center for Wireless Communications, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2020061242900
Language: English
Published: Elsevier, 2020
Publish Date: 2022-03-30
Description:

Abstract

Compressive sensing (CS) has been widely used in wireless sensor networks (WSNs). In WSNs, the sensors are battery-powered and hence their communication and processing powers are limited. One of the dominant features of the CS is its complex recovery phase. Thus, great care should be taken into account when designing the CS recovery algorithm for WSNs. In this paper, we propose a distributed and cooperative recovery algorithm for two different cooperation modes of sensor networks including incremental and diffusion. The theoretical performance analysis of the proposed algorithms in both exact and noisy measurements is investigated. The obtained results show the superiority of the proposed method in terms of convergence rate and steady-state error compared with the non-cooperative scenario and the well-known distributed least absolute shrinkage and selection operator (D-LASSO) approach. Furthermore, the proposed structure requires much fewer measurements for exact recovery.

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Series: Digital signal processing
ISSN: 1051-2004
ISSN-E: 1095-4333
ISSN-L: 1051-2004
Volume: 101
Article number: 102732
DOI: 10.1016/j.dsp.2020.102732
OADOI: https://oadoi.org/10.1016/j.dsp.2020.102732
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Copyright information: © 2020 Elsevier Inc. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/