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Sensors 2016, 16(10), 1547; doi:10.3390/s16101547

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 30 May 2016 / Revised: 19 August 2016 / Accepted: 14 September 2016 / Published: 22 September 2016
(This article belongs to the Section Sensor Networks)
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Abstract

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. View Full-Text
Keywords: wireless sensor networks; sparse representation; compressive sensing; data gathering; online dictionary learning wireless sensor networks; sparse representation; compressive sensing; data gathering; online dictionary learning
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Wang, D.; Wan, J.; Chen, J.; Zhang, Q. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks. Sensors 2016, 16, 1547.

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