Data Recovery in Wireless Sensor Networks With Joint Matrix Completion and Sparsity Constraints

Abstract

An effective way to reduce the energy consumption of energy constrained wireless sensor networks is reducing the number of collected data, which causes the recovery problem. In this letter, we propose a new data recovery method with joint matrix completion and sparsity constraints to recover the signal from undersampled measurements. Utilizing both the low-rank and temporal sparsity feature, the proposed method fully exploits spatiotemporal sparsity of the signal in networks. An algorithm is developed to efficiently solve the formulation incorporating the matrix completion and sparsity constraints terms. The results of experiments indicate that the proposed method outperforms the state-of-the-art methods for different types of signal in the network.