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Sensors 2018, 18(8), 2568; https://doi.org/10.3390/s18082568

Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

1
Harbin Institute of Technology, Weihai 264209, China
2
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Received: 20 May 2018 / Revised: 31 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Abstract

Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme. View Full-Text
Keywords: Bayesian estimation; compressed sensing; network lifetime; underwater sensor network Bayesian estimation; compressed sensing; network lifetime; underwater sensor network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, R.; Liu, G.; Kang, W.; Li, B.; Ma, R.; Zhu, C. Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks. Sensors 2018, 18, 2568.

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