Compressive Data Gathering Method Based on Probabilistic Sparse Random Matrices

Designing measurement matrix is one of the key points of applying Compressed Sensing (CS) to solve practical issue. In this paper, a kind of probabilistic sparse random matrix is designed for compressive data gathering in Wireless Sensor Networks (WSNs). Besides cutting the number of projection calculating nodes, the probabilistic sparse random matrices also make their location centralized, which leads a further reduction of communication overhead. Then, an optimization method for probabilistic sparse random matrices is also proposed to enhance the accuracy of network data reconstruction. Compared with the existing data gathering method using sparse random matrices and sparse Toeplitz matrices, the proposed method can reduce significantly not only the energy consumption, but also the reconstruction error.