Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (24): 263-270.DOI: 10.3778/j.issn.1002-8331.1706-0228

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Sparse data acquisition scheme based on intelligent optimization and cluster compressed sensing for WSNs

CHEN Jing   

  1. School of Information Science and Technology, Baotou Teachers’ College, Baotou, Inner Mongolia 014030, China
  • Online:2017-12-15 Published:2018-01-09


陈  静   

  1. 内蒙古科技大学包头师范学院 信息科学与技术学院,内蒙古 包头 014030

Abstract: In order to improve the data processing efficiency and reduce network energy consumption for Wireless Sensor Networks(WSNs), a WSNs sparse data acquisition scheme based on adaptive intelligent optimization and cluster compressed sensing is proposed. Firstly, a clustering WSNs sparse data communication model is established. Based on the quantitative analysis of the relationship between node density and total number of hops, the adaptive clustering results are given. Also the data acquisition using observation matrix in cluster and multi hop communication between clusters is used to complete the WSNs compressed sensing data acquisition. Secondly, the StOMP algorithm is used to reconstruct sparse signal. For the network node data packet loss and other unreliable links, the correlation matrix transformation strategy is introduced to reduce the impact of error data transmission on data reconstruction. Aiming at the problem that the data sparsity is unknown and the parameters of StOMP algorithm are difficult to configure, an Improved Adaptive Intelligent Optimization(IAIO) algorithm is introduced to StOMP algorithm. On the basis of theoretical analysis of the global optimization ability of IAIO, reliable reconstruction of sparse data is realized. Finally, the simulation results show that, the scheme can realize the accurate reconstruction of sparse signals, reduce the total amount of network communications, and improve the network lifetime.

Key words: Wireless Sensor Networks(WSNs), data acquisition, Compressed Sensing(CS), intelligent optimization, sparse reconstruction algorithm

摘要: 为提高无线传感器网络(Wireless Sensor Networks,WSNs)数据处理效率和降低网络能耗,提出了一种基于自适应智能优化和分簇压缩感知的WSNs稀疏数据采集方案。首先,建立分簇WSNs稀疏数据通信模型,通过定量分析节点密度与网络数据通信总跳数的关系,给出网络自适应分簇结果,并采用簇内观测矩阵测量数据获取和簇间多跳通信方式完成WSNs压缩感知数据采集;其次,采用StOMP算法进行稀疏信号重构,针对网络节点数据包丢失等链路不可靠情况,引入相关性矩阵变换策略,以降低错误数据传输对数据重构的影响,针对数据稀疏度未知特性和StOMP算法参数配置难的缺陷,将一种新型自适应智能优化(Improved Adaptive Intelligent Optimization algorithm,IAIO)算法应用于CS重构算法中,在理论分析IAIO全局寻优能力的基础上,实现对稀疏数据的可靠重构。最后,仿真结果表明,该方案能够实现稀疏信号的精确重构,而且降低了网络通信总量,提高了网络生存时间。

关键词: 无线传感器网络, 数据采集, 压缩感知, 智能优化, 稀疏重构算法