Compressive data gathering with low-rank constraints for Wireless Sensor networks

In this paper, a novel compressive data gathering with low-rank constraints is proposed for efficient data gathering and accurate recovery in wireless sensor networks. The proposed method utilizes both the low-rank feature of the data matrix by introducing the historical data and the sparsity feature based on compressive sensing. A reconstruction algorithm based on the alternating direction method of multipliers is described to efficiently solve the resultant optimization problem. Experimental results show the proposed method can significantly improve the recovery accuracy compared with the state-of-the-art methods.