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A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks

机译:基于能量信息和簇头期望的无线传感器网络聚类算法

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A new method is proposed in this paper to improve Low Energy Adaptive Clustering Hierarchy (LEACH) by electing cluster heads according to the residual energy of the nodes dynamically. A sliding window is set up to adjust the electing probability and keep stable the expected number of the cluster heads using two parameters in this method, one is the initial energy information of the nodes and the other is the average energy information of those that have not already been cluster heads in the network. Meanwhile, the number of cluster heads which is fixed in the entire network lifetime in LEACH is modified to be a variable according to the number of the living nodes. Simulations show that the improvement for First Node Dies (FND) and Half of the Nodes Alive (HNA) is 41% and 36%, respectively over LEACH, 17% and 26% for Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection (LEACH-DCHS), 22% and 21% for Advanced Low Energy Adaptive Clustering Hierarchy (ALEACH).
机译:本文提出了一种新方法,通过根据节点的剩余能量动态选举簇头来改进低能量自适应簇层次结构(LEACH)。在该方法中,使用两个参数设置滑动窗口以调整选举概率并保持簇头的预期数量稳定,一个参数是节点的初始能量信息,另一个是那些节点的平均能量信息。已经是网络中的簇头。同时,将LEACH的整个网络生命周期中固定的簇头的数量修改为根据活动节点的数量而变化的变量。仿真显示,与具有确定性簇头选择功能的低能耗自适应簇层次结构相比,第一节点管芯(FND)和一半节点有效(HNA)的改进分别为LEACH的41%和36%,低能自适应群集层次结构的改进为(17%和26%)( LEACH-DCHS),高级低能耗自适应集群体系(ALEACH)的22%和21%。

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