自动化学报2016，Vol.42Issue(11)：1648-1656,9.DOI:10.16383/j.aas.2016.c160258
WSN中基于压缩感知的高能效数据收集方案
Energy-efficient Data Gathering Scheme Based on Compressive Sensing in Wireless Sensor Networks
摘要
Abstract
Reliable and energy-efficient data gathering is a key problem in the application of wireless sensor networks (WSN). However, due to the high failure rate of wireless communication link, limited resource and severe environment, the network easily generates the packet loss problem, which makes the existing data gathering methods fail to meet the requirements of high-accuracy and low-energy consumption at the same time. To solve this problem, an energy-efficient data gathering scheme based on compressive sensing is proposed in this paper. It is divided into the two steps: the data processing of nodes and the data gathering path optimization. The sparse matrix based on the exponential kernel function is firstly designed for sparse processing of sensed data. Then considering both the energy consumption and reliability of data transmission, a measurement matrix is constructed by using the idea of block matrix, which combines the unit matrix and the check matrix of quasi cyclic low density parity check (LDPC) code. It is proved that the restricted isometry property (RIP) is satisfied between the sparse matrix and the measurement matrix. Finally, the data gathering path-optimization problem is modeled as the Hamilton loop problem, and a path optimization algorithm based on the tree decomposition is proposed to solve this problem. Simulation results show that the proposed scheme can still guarantee the high-accuracy of data gathering in the case of packet losses. Compared with the other data gathering schemes, the proposed scheme has better performance in terms of the data reconstruction error and energy consumption.关键词
无线传感器网络/数据收集/压缩感知/树分解/重构误差/能耗Key words
Wireless sensor networks (WSN)/data gathering/compressive sensing/tree decomposition/reconstruction error/energy consumption引用本文复制引用
李鹏,王建新,丁长松..WSN中基于压缩感知的高能效数据收集方案[J].自动化学报,2016,42(11):1648-1656,9.基金项目
国家自然科学基金(61472449,61173169,61402542)资助Supported by National Natural Science Foundation of China (61472449,61173169,61402542) （61472449,61173169,61402542）
