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
27195-27207
Chen, Jue
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Wang, Tsang Yi
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Wu, Jwo Yuh
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Li, Chih Peng
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Ng, Soon Xin
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Maunder, Robert
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
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1 December 2021
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), .
(doi:10.1109/JSEN.2021.3123209).
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
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Accepted/In Press date: 2021
Published date: 1 December 2021
Additional Information:
Funding Information:
The work of Tsang-Yi Wang was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant 110-2221-E-110-021. The work of Jwo-Yuh Wu was supported in part by the MOST of Taiwan under Grant MOST 108-2221-E-009-025 MY3 and Grant MOST 110-2634-F-009-025, in part by the Higher Education Sprout Project of the National Yang Ming Chiao Tung University and the Ministry of Education of Taiwan, and in part by the MOST Joint Research Center for AI Technology and All Vista Healthcare. The work of Chih-Peng Li was supported by the MOST, Taiwan, under Grant MOST 108-2218-E-110-014 and Grant MOST 109-2218-E-110-006. The work of Lajos Hanzo was supported in part by the Engineering and Physical Sciences Research Council under Project EP/P034284/1 and Project EP/P003990/1 (COALESCE) and in part by the European Research Council?s Advanced Fellow Grant QuantCom under Grant 789028.
Publisher Copyright:
© 2001-2012 IEEE.
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
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Date deposited: 11 Dec 2021 11:25
Last modified: 18 Mar 2024 03:09
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Contributors
Author:
Jue Chen
Author:
Tsang Yi Wang
Author:
Jwo Yuh Wu
Author:
Chih Peng Li
Author:
Soon Xin Ng
Author:
Robert Maunder
Author:
Lajos Hanzo
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