Distributed correlated data gathering in wireless sensor networks via compressed sensing

This paper considers energy efficient distributed data gathering methods for correlated data field monitoring in multi-hop wireless sensor networks (WSNs) via compressed sensing (CS). This signal compression and acquisition method can be used to exploit the inherent temporal and spatial correlation of the sensor signals. Many existing CS-based data gathering methods exploit correlation structures only in one signal dimension in WSNs. Thus, we propose a distributed method which utilizes the joint correlation pattern of a multi-dimensional WSN signal ensemble via Kronecker sparsity basis. Numerical results how that the proposed method can significantly reduce the data traffic in correlated data gathering multi-hop WSNs.