联合智能优化和分簇CS的WSNs稀疏数据采集

发布时间：2018-05-19 10:53

本文选题：无线传感器网络 + 数据采集　；参考：《计算机工程与应用》2017年24期

【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St OMP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St OMP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。
[Abstract]:In order to improve the data processing efficiency of Wireless Sensor Networks (WSNs) and reduce network energy consumption, a WSNs sparse data acquisition scheme based on adaptive intelligent optimization and clustering compression sensing is proposed. Firstly, a cluster WSNs sparse data communication model is established, and the network adaptive clustering results are given by quantitative analysis of the relationship between node density and the total hops of network data communication. The data acquisition of in-cluster observation matrix measurement and multi-hop communication between clusters are used to complete the WSNs compressed perceptual data acquisition. Secondly, the sparse signal reconstruction based on St OMP algorithm is used to deal with the unreliable link, such as network node data packet loss, etc. The correlation matrix transformation strategy is introduced to reduce the influence of error data transmission on data reconfiguration. Aiming at the unknown property of data sparsity and the difficulty in parameter configuration of St OMP algorithm, the proposed algorithm has the following advantages: 1. A new adaptive intelligent improved Adaptive Intelligent Optimization algorithm is applied to the CS reconstruction algorithm. Based on the theoretical analysis of the global optimization ability of IAIO, the reliable reconstruction of sparse data is realized. Finally, the simulation results show that the proposed scheme can accurately reconstruct sparse signals, reduce the total amount of network communication, and improve the network lifetime.
【作者单位】：内蒙古科技大学包头师范学院信息科学与技术学院;
【基金】：国家自然科学基金(No.61762071,No.61163025) 内蒙古自治区自然科学基金(No.2016MS0614) 内蒙古自治区高等学校科学研究基金(No.NJZY201,No.NJZY17287) 教育部“网络攻防联合实验室建设项目” 内蒙古科技大学包头师范学院教改项目(No.BSJG15Q002,No.BSJG15Z002)
【分类号】：TN929.5;TP212.9

【相似文献】