期刊文献+

无线传感器网络中敏感数据分布密度控制方法研究 被引量:1

Research on distribution density control method for sensitive data in wireless sensor networks
下载PDF
导出
摘要 为了解决传统基于参数可变遗传方法对无线传感网络敏感数据分布密度控制不健全,导致敏感数据分布密度失衡,且能量消耗过高的问题。提出基于改进差分蜂群算法的无线传感器网络节点分布密度控制方法,其综合考虑节点信息感知和信息传递的能耗,对圆形区域和带状区域的节点能耗进行分析,从理论上分别给出适用于这两种场景的节点非均匀分布方法,在此基础上采用改进差分蜂群算法,通过以网络覆盖率为目标函数对覆盖区域的节点进行分布密度优化控制,实现节点中敏感数据分布密度的均衡控制。实验结果说明,所提方法可实现节点中敏感数据分布密度的均衡控制,降低能量消耗。 The traditional genetic method based on parameter variation has poor distribution density control for sensitive data in wireless sensor networks,which leads to the distribution density imbalance of the sensitive data and excessive energy consump-tion.Therefore,an improved differential bee colony algorithm based distribution density control method of wireless sensor net-work node is proposed,in which the energy consumptions of node information perception and information transfer are considered comprehensively.The node energy consumption of the circular area and zonal area is analyzed.The node nonuniform distribution method suitable for the above scenes is given in theory.On this basis,the improved differential bee colony algorithm is adopted,and the network coverage rate is taken as the target function to perform the distribution density optimization control for the node within the coverage area,and realize the distribution density balance control of sensitive data in the node.The experimental re-sults show that the proposed method can realize the balance control of the distribution density of sensitive data in the node,and reduce the energy consumption.
作者 蒋传健 唐祯蔚 JIANG Chuanjian;TANG Zhenwei(Chongqing Normal University Foreign Trade and Business College,Chongqing 401520,China)
出处 《现代电子技术》 北大核心 2018年第7期80-84,共5页 Modern Electronics Technique
关键词 无线传感网络 节点 敏感数据 分布密度 控制方法 节点能耗 蜂群算法 wireless sensor network node sensitive data distribution density control method node energy consump-tion bee colony algorithm
  • 相关文献

参考文献9

二级参考文献106

共引文献88

同被引文献11

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部