Sequential Compressive Sensing in Wireless Sensor Networks

Compressive sensing (CS) is a new signal acquisition framework, which allows for a signal recovery from far fewer samples than what is required by traditional sampling methods. In this paper we propose new strategies for adaptively adjusting the number of CS samples in wireless sensor networks (WSNs). Additionally, in the signal reconstruction procedure we apply homotopy algorithm to update the reconstructed signals. The reduction of CS samples and the homotopy update reduce the computational complexity and save processing time and energy for both the fusion centre and wireless sensors. The proposed techniques are investigated numerically in various WSN scenarios.