中文摘要：

在资源受限的无线传感器网络中,低质量的无线链路严重限制了其大规模应用。基于WSN监测信号普遍具有的稀疏特性,提出了基于双层压缩感知（double process of compressive sensing）的有损无线链路稀疏信号传输架构,探索低质量无线链路下实时、高精度和高能效的稀疏信号传输方法。首先,将稀疏信号传输过程中的随机分组丢失现象建模为压缩感知的线性降维观测过程（被动CS过程）。然后,针对WSN为提高传输效率而采用的长数据分组,数据发送前在发送端引入线性随机降维投影——简易的信源编码操作（主动CS过程）,避免块状数据丢失的发生。最后,接收端根据收到的有损数据利用压缩感知的方法重构原始信号。进一步根据压缩感知重构和无线通信的相关原理,推导出允许使用的发送端最小压缩率和最大分组长度。大量仿真实验表明,所提方法不仅可以保证数据的可靠准确传输,还能减小发送数据量,降低传输时延和节点能耗。

英文摘要：

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.