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Compressed Sensing Based Adaptive-Resolution Data Recovery in Underwater Sensor Networks

Wenjing Kang, Gongliang Liu, and Bin Hu
School of Information and Electrical Engineering, Harbin Institute of Technology, Weihai 264209, P.R.China
Abstract—With marine development thriving today, underwater acoustic sensor networks have become a vital method in exploring and monitoring the ocean. In this paper, a data recovery scheme with adaptive resolution based on compressed sensing theory is proposed, aiming at acclimating to the atrocious conditions under the water. The fundamental thought of the scheme is to achieve better quality of recovered data at the sacrifice of data resolution. Data resolution adjusting method is raised firstly, then a recovered data quality evaluation algorithm and an adaptive resolution selecting strategy are proposed. The experimental results show that schemes presented are able to evaluate the quality of the recovered data accurately and adaptively select the resolution. Accordingly, recovery resolution is modified triumphantly to achieve higher accuracy on the premise of acceptable data resolution.

Index Terms—Underwater sensor networks, compressed sensing, data recovery, adaptive resolution

Cite: Wenjing Kang, Gongliang Liu, and Bin Hu, “Compressed Sensing Based Adaptive-Resolution Data Recovery in Underwater Sensor Networks," Journal of Communications, vol. 11, no. 3, pp. 317-324, 2016. Doi: 10.12720/jcm.11.3.317-324
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