Data recovery in heterogeneous wireless sensor networks based on low-rank tensors

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 paper, we propose a novel data recovery method based on low-rank tensors for the heterogeneous wireless sensor networks with various sensor types. The proposed method represents the collected high-dimensional data as low-rank tensors to effectively exploit the spatiotemporal correlation that exists in the various data. Furthermore, an algorithm based on the alternating direction method of multipliers is developed to solve the resultant optimization problem efficiently. Experimental results demonstrate that the proposed method significantly outperforms the sparsity constraint method and matrix completion method for each type of signals.