无线传感器网络中基于小波的自适应多模数据压缩算法  

Adaptive multiple-modalities data compression algorithm using wavelet for wireless sensor networks

作　　者：朱铁军[1] 林亚平[1,2] 周四望[2] 徐小龙[1] 

机构地区：[1]湖南大学计算机与通信学院,湖南长沙410082 [2]湖南大学软件学院,湖南长沙410082

出　　处：《通信学报》2009年第3期48-53,共6页Journal on Communications

基　　金：国家高技术研究发展计划("863"计划)基金资助项目(2006AA01Z227);湖南省科技厅科技计划基金资助项目(2007FJ4157)~~

摘　　要：基于数据的多模性,设计了一个基于小波的自适应多模数据压缩算法。在给定的相关度阈值的条件下,算法能自适应地对数据调整分类,对相关数据采用最小二乘估计进行拟合,把特征数据抽象成一个矩阵,利用小波变换去除数据的空间和时间相关性。理论分析和仿真实验表明,新算法能够有效地去除数据之间的多模相关性和同种数据的空间和时间相关性,新算法有效地提高了压缩比,降低了网络的能耗。Wireless sensor networks usually have limited resources, such as energy, bandwidth and processing and so on. And they can＇t match the transmission of a large number of data. So, it is necessary to perform in-network compression of the raw data sampled by sensors. The data sensor node collected normally have multiple-modalities pertinence. Multiple-modalities pertinence refers to the different types of data which the same node sampled have some correlation. A adaptive multiple-modalities data compression algorithm using wavelet was designed. In a given threshold of the correlation, the data can be adaptive classified using this algorithm, the relevant data can be estimated using the least square estimation. The characteristics data are abstracted as a matrix, then can be exploited the spatial and temporal corrections using wavelet transform. Theoretically and experimentally, the proposed algorithm can effectively exploit the correlation of the data, the compression ratio of the algorithm has improved. Effectively, it can provide a significant reduction in energy consumption.

关 键 词：无线传感器网络 小波变换 多模数据 数据压缩 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]