摘要
考虑到无线传感器网络WSNs能量、通信带宽、计算能力及成本有限,不适合大规模数据传输,同时存在数据冗余,需要进行数据压缩处理,提出一种新的基于遗传算法的压缩感知CS(Compressive Sensing)重构方法,应用于无线传感器网络数据压缩中。详细阐述分布式WSNs数据压缩特点,压缩感知基本理论,基于遗传算法的CS重构新方法以及在WSNs数据压缩中的应用。通过实验仿真证明,从压缩比、节点平均能耗、网络生存时间和网络时延四个方面,与DCCM算法及CCS算法的WSNs数据压缩算法进行比较,提出的算法具有较高的压缩比,提高了采集数据的重构精度,降低了数据冗余度和网络通信量,提高了网络效率。
In view of that the wireless sensor networks（ WSNs） are limited in energy,communication bandwidth,computing capability and cost,they are not suitable for large-scale data transmission,and that there is the presence of data redundancy meanwhile and has the need of data compression,we put forward a new genetic algorithm-based compressed sensing（ CS） reconstruction method,and applied it to wireless sensor network data compression. In this paper we expatiate on the features of distributed WSNs data compression,the basic theory of compressed sensing,as well as the genetic algorithm-based new CS reconstruction method and its application in WSNs data compression. It was proved through experimental simulation that compared with WSNs data compression algorithms using DCCM algorithm and CCS algorithm in four aspects of compression ratio,average nodes energy consumption,network lifecycle and network delay,the proposed algorithm had higher compression ratio,improved the reconstruction accuracy of the collected data,reduced the data redundancy and network traffic,and improved the network efficiency.
出处
《计算机应用与软件》
CSCD
2016年第4期129-133,共5页
Computer Applications and Software
基金
国家自然科学基金项目(61202248)
河南省科技发展计划科技攻关重点项目(122102210412)
关键词
无线传感器网络
压缩感知
遗传算法
压缩比
生命周期
Wireless sensor networks
Compressive sensing
Genetic algorithm
Compression ratio
Lifecycle
