Sparse signal transmission under lossy wireless links based on double process of compressive sensing

In resource-limited wireless sensor networks, links with poor quality hinder its large-scale applications seriously. Thanks to the inherent sparse property of signals in WSN, the framework of sparse signal transmission based on double process of compressive sensing was proposed, providing an insight into a new way of real-time, accurate and energy-efficient sparse signal transmission. Firstly, the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS). Then, considering that a large packet was often adopted in WSN for higher transmission efficiency, a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss. Now, the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms. Furtherly, according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication, the minimum compression ratio and the maximum packet length allowed were obtained. Extensive simulations demonstrate that the reliability of data transmission and its accuracy, the data transmission volume, the transmission delay and energy consumption could be greatly optimized by means of proposed method.