无线传感器网络数据自适应稀疏变换

Adaptive Sparse Transform for Wireless Sensor Network Data

针对无线传感器网络节点位置移动及传输干扰等因素可导致数据稀疏结构改变的问题,提出一种基于字典学习的无线传感器网络自适应稀疏变换方法。根据网络数据稀疏结构变化,自适应构建最优稀疏变换基,同时在字典学习问题中引入无线传感器网络数据稀疏基的可压缩约束,以满足无线传感器网络中大规模数据处理特点及稀疏变换的高实时性要求。理论分析和仿真结果表明,所提算法可有效提高无线传感器网络数据稀疏变换算法的顽健性,同时具有良好的实时性。

Aiming at the change of sparse structure introduced by mobility of the wireless sensor network （WSN） nodes and noise in data transmission, an adaptive sparse transform method based on dictionary learning （DL） for WSN data was proposed. The optimum sparse basis can be adaptively constructed according to the change of sparse structure, and the compressibility of WSN data basis was introduced to DL to satisfy the real time requirement for large-scale data processing. Analysis and experimental resuhs demonstrate that the proposed algorithm can significantly improve the robustness and the real time performance of WSN data sparse transform.