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This paper presents a novel matrix completion algorithm to enable energy efficient data gathering in wireless sensor networks. The algorithm takes full advantage of both the low-rankness and the DCT compactness features in the sensory data to improve the recovery accuracy. The time complexity of the algorithm is analyzed, which indicates it has a low computational cost. Moreover, the recovery error is carefully analyzed and a theoretical upper bound is derived. The error bound is then validated by experimental results. Extensive experiments are conducted on three datasets collected from two testbeds. Experimental results show that the proposed algorithm outperforms state-of-the-art methods for low sampling rate and achieves a good recovery accuracy even if the sampling rate is very low.

A DCT Regularized Matrix Completion Algorithm for Energy Efficient Data Gathering in Wireless Sensor Networks