Data ferries based compressive data gathering for wireless sensor networks

The latest research progress of the theory of compressive sensing (CS) over graphs makes it possible that the advantage of CS can be utilized by data ferries to gather data for wireless sensor networks. In this paper, we leverage the non-uniform distribution of the sensing data field to significantly reduce the required number of data ferries, yet ensuring the recovered data quality. Specially, we propose an intelligent compressive data gathering scheme consisting of an efficient stopping criterion and a novel learning strategy. The proposed stopping criterion is based only on the gathered data, without relying on the priori knowledge on the sparsity of unknown sensing data. Our learning strategy minimizes the number of data ferries while guaranteeing the data quality by learning the statistical distribution of the gathered data. Simulation results show that the proposed scheme improves the reconstruction accuracy and stability compared to the existing ones.