面向有损链路的传感网压缩感知数据收集算法

基于压缩感知的数据收集算法在能量受限、数据冗余的无线传感网中有巨大应用潜力，现有研究大多假定无线链路理想.通过实验说明有损链路丢包会严重影响压缩感知数据收集算法的数据重构质量；提出了一种基于重传与时间序列相关性预测（CS data gathering based on retransmission and time series correlation prediction，简称CS-RTSC）的数据收集算法，将有损链路上的丢包建模为随机丢包和块状丢包，设计了基于滑动窗统计的丢包类型预判算法，在检测到链路丢包时判断丢包类型，对随机丢包采用重传恢复，对块状丢包设计了基于时间序列相关性预测算法恢复.仿真结果表明该算法能有效降低有损链路丢包对CS数据收集的影响，在网络丢包率达到30%时，CS数据重构的相对误差仅比理想链路下的CS相对重构误差高0.1%.

Data Gathering Algorithm Based on Compressive Sensing Under Lossy WSN

Data gathering algorithm based on compressive sensing (CS) has enormous application potential in wireless sensor network (WSN) in which there is limited energy and a lot of redundant data. However, most existing studies assume that network is based on ideal link. This paper illustrates a situation by experiment that existing CS reconstruction quality will be seriously affected by lossy link and proposes a CS data gathering algorithm based on retransmission and time series correlation prediction (CS-RTSC). The type of packet loss is modeled as element random loss (ERL) and block random loss (BRL). The prediction algorithm of loss type based on the sliding window statistic is designed to determine the type of packet loss when links happen packer loss. As for ERL, retransmission recovery is applied; as for BRL, time series correlation prediction algorithm is designed to recover the loss. The simulation result indicates that the proposed algorithm can effectively reduce the impact of lossy link in the CS data gathering. When the packet loss ratio is up to 30%, the relative error of the CS reconstruction signal is only 0.1% higher than that of the CS reconstruction signal in the ideal link.