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

—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.

[1]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[2]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[3]  E. Candès.  The restricted isometry property and its implications for compressed sensing , 2008 .

[4]  Victor C. M. Leung,et al.  Secure Resource Allocation for OFDMA Two-Way Relay Wireless Sensor Networks Without and With Cooperative Jamming , 2016, IEEE Transactions on Industrial Informatics.

[5]  Marian Codreanu,et al.  Sequential Compressed Sensing With Progressive Signal Reconstruction in Wireless Sensor Networks , 2015, IEEE Transactions on Wireless Communications.

[6]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[7]  Emmanuel J. Candès,et al.  Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..

[8]  Jean-Luc Starck,et al.  Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit , 2012, IEEE Transactions on Information Theory.

[9]  Xinhao Liu,et al.  Noise level estimation using weak textured patches of a single noisy image , 2012, 2012 19th IEEE International Conference on Image Processing.

[10]  Khaldoun Al Agha,et al.  Compressed Sensing Based Protocol for Interfering Data Recovery in Multi-Hop Sensor Networks , 2014, IEEE Communications Letters.

[11]  Emmanuel J. Candès,et al.  Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.

[12]  Rakesh Kumar,et al.  A Survey on Data Aggregation And Clustering Schemes in Underwater Sensor Networks , 2014 .

[13]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression , 2007, J. Mach. Learn. Res..

[14]  Shaojie Tang,et al.  Data gathering in wireless sensor networks through intelligent compressive sensing , 2012, 2012 Proceedings IEEE INFOCOM.

[15]  Mikael Skoglund,et al.  Distributed greedy pursuit algorithms , 2013, Signal Process..

[16]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.