WSN中基于压缩感知的高能效数据收集方案

可靠高效的数据收集是无线传感器网络（Wireless sensor networks，WSN）应用中的关键问题.然而，由于无线通信链路的高失效率、节点资源受限以及环境恶劣等原因，网络容易发生丢包问题，使得现有的数据收集方法无法同时满足高精度和低能耗的要求.为此，本文提出了一种基于压缩感知的高能效数据收集方案.该方案主要分为节点上的数据处理和数据收集路径优化两个步骤.首先设计了基于指数核函数的稀疏矩阵来对感知数据进行稀疏化处理，然后综合考虑了数据的传输能耗和可靠性等因素，采用分块矩阵的思路，将单位矩阵和准循环低密度奇偶校验（Low density parity check，LDPC）码的校验矩阵相结合构造了测量矩阵，并证明了它与稀疏矩阵之间满足限制等距性质（Restricted isometry property，RIP）.最后，将数据收集路径优化问题建模为哈密尔顿回路问题，并提出了基于树分解的路径优化算法进行求解.仿真结果表明，在网络存在丢包的情况下，本文方案仍然能够保证数据收集的高精确度，相比于其他数据收集方案而言，本文方案在数据重构误差和能耗方面的性能更优.

Energy-efficient Data Gathering Scheme Based on Compressive Sensing in Wireless Sensor Networks

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.