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Factor graphs for support identification in compressive sensing aided wireless sensor networks

Factor graphs for support identification in compressive sensing aided wireless sensor networks
Factor graphs for support identification in compressive sensing aided wireless sensor networks
A new support identification technique based on factor graphs and belief propagation is proposed for compressive sensing (CS) aided wireless sensor networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an signal to noise ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the orthogonal matching pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the fusion center (FC), while maintaining high-accuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.
Complexity theory, Compressive sensing, Matching pursuit algorithms, Sensors, Signal processing algorithms, Signal reconstruction, Sparse matrices, Wireless sensor networks, noise reduction, sparse sensing matrix, support identification, wireless sensor networks
1530-437X
27195-27207
Chen, Jue
14b8e7c8-7f5e-4e68-a250-fd0989e1567b
Wang, Tsang Yi
7f1c0642-9107-4096-b255-799aff0b3176
Wu, Jwo Yuh
1c95bdaf-16e4-4c34-85b7-2df0eb2a1c0e
Li, Chih Peng
aa5cdbec-f67a-41c7-8b87-037db1ae69e3
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Jue
14b8e7c8-7f5e-4e68-a250-fd0989e1567b
Wang, Tsang Yi
7f1c0642-9107-4096-b255-799aff0b3176
Wu, Jwo Yuh
1c95bdaf-16e4-4c34-85b7-2df0eb2a1c0e
Li, Chih Peng
aa5cdbec-f67a-41c7-8b87-037db1ae69e3
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, Jue, Wang, Tsang Yi, Wu, Jwo Yuh, Li, Chih Peng, Ng, Soon Xin, Maunder, Robert and Hanzo, Lajos (2021) Factor graphs for support identification in compressive sensing aided wireless sensor networks. IEEE Sensors Journal, 21 (23), 27195-27207. (doi:10.1109/JSEN.2021.3123209).

Record type: Article

Abstract

A new support identification technique based on factor graphs and belief propagation is proposed for compressive sensing (CS) aided wireless sensor networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an signal to noise ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the orthogonal matching pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the fusion center (FC), while maintaining high-accuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.

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Factor Graphs for Support Identification in Compressive Sensing Aided WSNs - Accepted Manuscript
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Accepted/In Press date: 2021
Published date: 26 October 2021
Additional Information: Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Complexity theory, Compressive sensing, Matching pursuit algorithms, Sensors, Signal processing algorithms, Signal reconstruction, Sparse matrices, Wireless sensor networks, noise reduction, sparse sensing matrix, support identification, wireless sensor networks

Identifiers

Local EPrints ID: 452511
URI: http://eprints.soton.ac.uk/id/eprint/452511
ISSN: 1530-437X
PURE UUID: 4ac04371-8a41-4833-a10c-a2590928fa9b
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194
ORCID for Robert Maunder: ORCID iD orcid.org/0000-0002-7944-2615
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 11 Dec 2021 11:25
Last modified: 10 Jan 2022 02:53

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Contributors

Author: Jue Chen
Author: Tsang Yi Wang
Author: Jwo Yuh Wu
Author: Chih Peng Li
Author: Soon Xin Ng ORCID iD
Author: Robert Maunder ORCID iD
Author: Lajos Hanzo ORCID iD

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