基于多维相空间重构的无线传感网数据压缩

Data compression technology of wireless sensor network based on multi-dimensional phase space reconstruction

为了提高无线传感网数据的优化存储能力,提出基于多维相空间重构的无线传感网数据动态压缩算法。构建无线传感网数据的分布式存储结构模型,采用模糊隶属度挖掘的方法进行无线传感网数据的知识图谱特征提取,构建无线传感网数据的动态压缩输出模型,利用知识图谱挖掘和模糊度检测分析的方法进行无线传感网数据分类处理,采用多维相空间重构方法实现数据的特征降维,实现无线传感网数据动态压缩。仿真结果表明,采用该方法进行无线传感网数据压缩的无损性较好,无线传感网数据输出的保真性较强,提高了无线传感网数据的优化存储能力。

In order to improve the optimal data storage capacity of wireless sensor networks,a dynamic data compression algorithm based on multidimensional phase space reconstruction is proposed.The distributed storage structure model of wireless sensor network data is constructed.The fuzzy membership mining method is used to extract the knowledge map features of wireless sensor network data,and the dynamic compression output model of wireless sensor network data is constructed.The methods of knowledge map mining and ambiguity detection and analysis are used to classify wireless sensor data.The multidimensional phase space reconstruction method is used to realize the feature dimension reduction of data and the dynamic compression of wireless sensor network data.The simulation results show that the data compression of wireless sensor network with this method is non-destructive,and the data output of wireless sensor network has strong fidelity,which improves the optimal storage capacity of wireless sensor network data.