基于层次混合的高效概率包标记WSNs节点定位算法  

An Efficient Probabilistic Packet Marking Node Localization Algorithm Based on Layers-mixed in WSNs

作　　者：周先存[1] 黎明曦[2] 陈振伟[1] 徐英来 熊焰[2] 李瑞霞[1] 

机构地区：[1]皖西学院信息工程学院,六安237012 [2]中国科学技术大学计算机科学与技术学院,合肥230026 [3]解放军陆军军官学院六系,合肥230031

出　　处：《电子与信息学报》2014年第2期384-389,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金青年科学基金(61303209;61302179);安徽省高等学校省级自然科学研究重点项目(KJ2013A255);六安市定向委托皖西学院产学研合作项目(2012LWA015)资助课题

摘　　要：在利用概率包标记技术对无线传感器网络(WSN)恶意节点的追踪定位中,标记概率的确定是关键,直接影响到算法的收敛性,最弱链,节点负担等方面。该文分析并指出了基本概率包标记(BPPM)和等概率包标记(EPPM)方法的缺点,提出了一种层次式混合概率包标记(LMPPM)算法,可以克服以上算法的不足。该算法对无线传感器网络进行分簇,将每个簇看成一个大的"簇节点",整个网络由一些大的"簇节点"构成,每个"簇节点"内部又包含一定数量的传感器节点。在"簇节点"之间采用等概率包标记法,在"簇节点"内部采用基本概率包标记法。实验分析表明,该算法在收敛性、最弱链方面优于BPPM算法,在节点计算与存储负担方面优于EPPM算法,是在资源约束条件下的一种整体优化。When the probabilistic packet marking technique for traceback and localization of malicious nodes in Wireless Sensor Networks （WSNs）, the determination of marking probability is the key to influence the convergence, the weakest link, and the node burden of the algorithm. First, the disadvantages of the Basic Probabilistic Packet Marking （BPPM） algorithm and the Equal Probabilistic Packet Marking （EPPM） algorithm is analyzed. Then, a Layered Mixed Probabilistic Packet Marking （LMPPM） algorithm is proposed to overcome the defects of the above algorithms. In the proposed algorithm, WSN is clustered, and each cluster is considered as a big “cluster nodes”, therefore, the whole network consists of some big “cluster nodes”. Correspondingly, each“cluster nodes” internal contains a certain number of sensor nodes. The EPPM algorithm is used between the“cluster nodes”, and the BPPM algorithm is used in the“cluster nodes”. Experiments show that LMPPM is better than BPPM in convergence and the weakest link, and the node storage burden of the proposed algorithm is lower than that of the EPPM algorithm. The experiments confirm that the proposed algorithm is a kind of whole optimization under the conditions of resource constraint.

关 键 词：无线传感器网络(WSN) 概率包标记(PPM) 溯源定位 分簇 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]