传感网中基于压缩感知的丢包匹配数据收集算法

为了提高传感网中数据重构精度以及降低不可靠链路丢包对压缩感知（Compressive Sensing,CS） 数据收集的影响,本文提出了一种基于压缩感知丢包匹配数据收集算法（Packet Loss Matching Data Gathering Algorithm Based on Compressive Sensing,CS-MDGA）.本文算法通过压缩感知技术构建了全网数据间的"关联效应",并设计了基于丢包匹配的稀疏观测矩阵（Sparse Observation Matrix Based on Packet Loss Matching,SPLM）,证明了该观测矩阵概率趋近于"1"时,满足的等距约束条件（Restricted Isometry Property,RIP）,完成了节点间多路径路由数据的可靠交付.仿真实验结果表明,本文算法在链路丢包率为60%情况下,相对重构误差仍小于5%,验证了本文算法不仅具有较高的重构精度,而且还可以有效缓解不可靠链路丢包对CS数据收集的影响.

CS-MDGA:A Packet Loss Matching Data Gathering Algorithm in Sensor Networks Based on Compressive Sensing

In order to improve the data reconstruction accuracy and alleviate the influence of packet loss over unreliable links on the Compressive Sensing (CS) data gathering in sensor networks,we propose a Packet Loss Matching Data Gathering Algorithm Based on Compressive Sensing (CS-MDGA) in this paper.This proposed algorithm establishes the correlation effect of the network data with the CS technique.We further design the Sparse Observation Matrix based on Packet Loss Matching (SPLM) in this paper.In addition,we prove that the designed observation matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1,which can guarantee the reliable delivery of the multi-path routing data among different nodes.The simulation results show that the relative reconstruction error of this proposed algorithm is still lower than 5% even when the packet loss rate of the link is as high as 60%.Therefore,it is verified that this proposed algorithm not only exhibits high reconstruction accuracy,but also effectively alleviates the influence of packet losses over unreliable links on the CS-based data collection.