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New K-means algorithm for clustering in wireless sensor networks

机译:无线传感器网络中用于聚类的新K-means算法

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Energy consumption is the main issue that researchers have dealt with to design any routing application. To design comprehensive reliable wireless sensors networks (WSNs), it is essential to consider node failure and energy constraint as inevitable phenomena. In this paper, a new method for optimal clustering is proposed, that accurately localizes sensors while minimizing their power consumption. It also splits the task of Cluster head between sensor nodes to send data to the base station (BS). The proposed scheme is based on K-means method to selected Cluster-Head. The selection of CH starts with the closest nodes to the centroid point. In the second step, we selected nodes with high power compared to its neighbors, after repeating this phenomenon until the energy of all the nodes is drained. This method can equilibrate the role of Cluster-Head between all the network sensors. Simulation results showed that our clustering mechanism seemed to effectively improve the network lifetime over Low Energy Adaptive Clustering Hierarchy (LEACH) and Energy efficient clustering algorithm for maximizing the lifetime of wireless sensor networks.
机译:能耗是研究人员设计任何路由应用程序时要解决的主要问题。为了设计全面可靠的无线传感器网络(WSN),必须将节点故障和能量约束视为不可避免的现象。在本文中,提出了一种新的最佳聚类方法,该方法可以在最小化传感器功耗的同时准确地定位传感器。它还将传感器节点之间的簇头任务分开,以将数据发送到基站(BS)。提出的方案是基于K-means方法选择簇头的。 CH的选择从最接近质心点的节点开始。在第二步中,在重复这种现象直到所有节点的能量耗尽之前,我们选择了与其邻居相比具有较高功率的节点。此方法可以平衡所有网络传感器之间的簇头功能。仿真结果表明,我们的聚类机制似乎比低能耗自适应聚类层次结构(LEACH)和节能聚类算法有效地提高了网络寿命,从而最大化了无线传感器网络的寿命。

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