摘要
现有的无线传感器网络(WSNs)数据收集方法无法在耗费较低开销的同时保证数据收集的可靠性。基于压缩感知(CS)理论,设计了基于指数核函数的稀疏矩阵和基于准循环低密度奇偶校验(LDPC)码的测量矩阵来用于节点的数据采集,以最大化网络生命周期为目标,将测量值传输问题建模为汉密尔顿回路问题,并提出了一种基于树分解的数据收集路径优化算法。仿真实验结果表明:所提方案在数据重构误差和能耗方面的性能要优于目前典型的数据收集方法。
The existing wireless sensor networks （WSNs） data gathering methods cannot be ensure reliability of data gathering with low cost. To solve this problem, Based on the theory of compressive sensing（CS） , sparse matrix based on exponential kernel function and measurement matrix based on quasicyclic low density parity check （LDPC） code are designed to be used for data acquisition of node. Maximization of network lifetime is target, the measurement value transmission problem is modeled as the Hamilton loop problem, and a data gathering path optimization algorithm based on tree decomposition is proposed. Simulation results show that the performance of the proposed scheme is better than the typical data gathering methods in terms of data reconstruction error and energy consumption.
出处
《传感器与微系统》
CSCD
2016年第6期24-26,30,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61472449
61173169
61402542)
关键词
无线传感器网络
数据收集
压缩感知
稀疏矩阵
测量矩阵
树分解
wireless sensor networks （ WSNs ）
data gathering
compressive sensing （CS）
sparse matrix
measurement matrix
tree decomposition
