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基于增强LEACH协议的无线传感器网络恶意节点检测模型

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基于增强LEACH协议的无线传感器网络恶意节点检测模型
Enhanced LEACH Protocol Based Wireless Sensor Network Malicious Node Detection Model
投稿时间:2017-09-12
DOI:10.15918/j.tbit1001-0645.2019.03.013
中文关键词:无线传感器网络恶意节点信誉值簇集群判定
English Keywords:wireless sensor networkmalicious nodereputation valueclusterdetermine
基金项目:国家科技重大专项项目(2012ZX03002002);国家自然科学基金资助项目(U1833107);中国民航科技项目(MHRD201009,MHRD201205);中央高校基本科研业务费专项项目(3122014D033)
作者单位
杨宏宇中国民航大学 计算机科学与技术学院, 天津 300300
程放中国民航大学 计算机科学与技术学院, 天津 300300
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中文摘要:
针对现有无线传感器网络恶意节点检测方法效率较低的不足,提出一种基于增强低功耗自适应集簇分层(enhanced low energy adaptive clustering hierarchy,enhanced LEACH)路由协议信誉机制的恶意节点检测(malicious node detection based on enhanced LEACH with reputation,MNDELR)模型.在无线传感器网络中使用增强LEACH路由协议选取簇首节点,其余节点选择对应簇首形成各簇集群并确定网络数据包传递路径.节点在数据包内添加节点编号、信誉评价等信息并按传递路径将数据包发送至汇聚节点;汇聚节点解析获取数据包内节点编号并与源节点编号比较判定,形成可疑节点列表;计算节点信誉值并与阈值比较判定网络中的恶意节点.实验结果表明,与其他方法相比,MNDELR模型在无线传感器网络中对恶意节点的检测效果较为显著.
English Summary:
To solve the efficiency problem of the existing malicious node detection methods in wireless sensor networks (WSN),a malicious node detection model was proposed based on enhanced low energy adaptive clustering hierarchy (LEACH) routing protocol with reputation (MNDELR).Firstly,in wireless sensor network,the enhanced LEACH routing protocol was used to select the cluster-head nodes and make other nodes to be corresponding cluster-head nodes to form the clusters and determine the packets delivery paths in the network.Then,some information,including the node numbers and the reputation evaluation,were added to the data packets of nodes,and the data packets were sent to the sink node according to the delivery paths.The node numbers in the packets were parsed and compared with the source node numbers in the convergent node to form a list of suspicious nodes.Finally,the reputation values of the nodes were calculated and compared with the threshold to determine the malicious nodes in the network.The experiment results show that,compared with other methods,MNDELR model can detect malicious nodes in WSN more effectively.
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