ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (9): 2000-2008.doi: 10.7544/issn1000-1239.2016.20150459

• 网络技术 • 上一篇    下一篇

基于CS的无线传感器网络动态分簇数据收集算法

张策,张霞,李鸥,王冲,张大龙   

  1. (中国人民解放军信息工程大学信息系统工程学院 郑州 450001) (cezhang@foxmail.com)
  • 出版日期: 2016-09-01
  • 基金资助: 
    国家科技重大专项基金项目(2014zx03006003)

Data Gathering Using Dynamic Clustering Based on WSNs Compressive Sensing Algorithm

Zhang Ce, Zhang Xia, Li Ou, Wang Chong, Zhang Dalong   

  1. (School of Information Systems Engineering, PLA Information Engineering University, Zhengzhou 450001)
  • Online: 2016-09-01

摘要: 降低能耗、实现网络的能量均衡和延长网络寿命,是设计无线传感器网络(wireless sensor networks, WSNs)数据收集算法所面临的主要挑战之一.针对现有无线传感器网络分簇数据收集算法不考虑网络中事件源的发生对数据空间相关性的影响的情况,提出了一种基于压缩感知的以事件源为中心的动态分簇(CS-based dynamic clustering centred on event source, CS-DCES)算法.该算法利用欧氏距离空间相关性模型和第一联合稀疏模型,将受同一个事件源影响的节点分在一个簇中,并以簇为单位进行数据重构,以此增加簇内节点感知数据的空间相关性,减小每簇数据观测量;利用压缩感知收集数据,计算事件源位置,根据事件源位置变化实行动态分簇.并通过实验分析了影响该算法性能的3个因素,即事件的衰减系数、事件源之间的距离和事件源个数,最后给出了算法的适用条件.仿真分析表明,相对于已有算法,CS-DCES在满足同一重构精度的前提下,有效减小了数据传输量,节省网络能耗,延长网络寿命.

关键词: 无线传感器网络, 数据收集, 事件源, 空间相关性, 动态分簇

Abstract: One of the major challenges of designing wireless sensor networks data gathering algorithm is to reduce energy consumption, achieve better balanced consumption and prolong the lifetime of sensor network. For the current clustering algorithms of data gathering in wireless sensor networks neglecting the impact of event sources on data spatial correlation, a CS-based dynamic clustering algorithm centred on event source (CS-DCES) is proposed in this paper. Utilizing the model of Euclidean distance spatial correlation and joint sparsity model-1, the nodes that affected by the same event source are clustered together and reconstruct those nodes readings within cluster in order to increase the spatial correlation of data and reduce the number of each cluster measurement. The algorithm exploits the compressive data to calculate the location of event source and clusters dynamically. Moreover, according to simulation, we analyze the three factors that influence the algorithm performance, and they are attenuation coefficient of event sources, distance between event sources and the number of event sources, and finally the application condition of CS-DCES is given. The simulations show that CS-DCES outperforms the existing data gathering algorithms in decreasing communication cost, saving the network energy consumption and extending the network survival time under the same accuracy.

Key words: wireless sensor networks(WSNs), data gather, event source, spatial correlation, dynamic clustering

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