基于时空相关性的传感器网络数据压缩算法  

Data Compression Algorithms for Sensor Networks Based on Spatio-Temporal Correlation

作　　者：王林景 高志宇[1] 姚鹏帅 WANG Linjing;GAO Zhiyu;YAO Pengshuai(School of Information Technology,Henan University of Chinese Medicine,Zhengzhou 450046,China)

机构地区：[1]河南中医药大学信息技术学院,郑州450046

出　　处：《吉林大学学报（理学版）》2020年第2期337-342,共6页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:81703946);河南省科技厅科技计划项目(批准号:172102310536);河南省高等学校重点科研项目(批准号:17b520017).

摘　　要：针对当前传感器网络数据压缩算法存在压缩比率低、数据变形严重等缺陷,为提高传感器网络数据传输的实时性,提出一种基于时空相关性的传感器网络数据压缩算法。首先采集传感器网络原始数据,采用空间变换技术从空间上分析传感器网络数据之间的相关性,进行去噪处理,减少噪声所占的空间资源;然后根据传感器网络数据的时间相关性,引入压缩感知算法对空间系数进行压缩处理,减少传感器网络数据冗余;最后通过仿真实验分析传感器网络数据压缩算法的性能。仿真实验结果表明,相对于其他传感器网络数据压缩算法,该算法可在不损失传感器网络数据信息的条件下,提高传感器网络数据压缩比率,同时获得更快的传感器网络数据压缩速度,减少传感器网络通信压力。Aiming at the problem that the current data compression algorithm of sensor network had the defects of low compression ratio and serious data deformation,in order to improve the real-time performance of data transmission of sensor networks,we proposed a data compression algorithm based on spatio-temporal correlation.Firstly,the original data of sensor network was collected,and the spatial transformation technology was used to analyze the correlation between the data of sensor network in space,and then the noise was removed to reduce the spatial resource occupied by noise.Secondly,according to the temporal correlation of the data of sensor network,the compression sensing algorithm was introduced to compress the spatial coefficient to reduce the data redundancy of sensor network.Finally,the performance of data compression algorithm of sensor network was analyzed by simulation experiment.The simulation results show that compared with other sensor network data compression algorithms,the proposed algorithm can improve the data compression ratio of sensor network without losing the data information of sensor network,obtain faster data compression speed of sensor network and reduce the communication pressure of sensor network.

关 键 词：传感器网络数据 时间相关性 空间相关性 空间变换技术 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]