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HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks

机译：HQCA-WSN：高质量聚类算法和无线传感器网络中模糊逻辑的最佳簇头选择

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摘要

Reducing the consumption of energy and the network lifetime are the main challenges that affect wireless sensor networks (WSNs). High-quality clustering is one of the most important approaches for reducing the energy consumption in WSNs. Various criteria can be used to assess the quality of the clusters and considering all of these criteria can lead to high-quality clustering. In this study, we propose a method called the high-quality clustering algorithm (HQCA) for generating high-quality clusters. The HQCA method uses a criterion for measuring the cluster quality, which can improve the inter-cluster and intra-cluster distances as well as reducing the error rate during clustering. The optimal cluster head (CH) is selected based on fuzzy logic and according to various criteria such as the residual energy, the minimum and maximum energy in each cluster, and the minimum and maximum distances between the nodes in each cluster and the base station. The main advantages of this method are its high reliability, low error rate during the clustering process, the independence of key CHs, better scalability, and good performance in large-scale networks with a high number of nodes. The validity of the clustering quality is also measured based on external and internal criteria. Simulation results demonstrated that the HQCA-WSN method can significantly improve the energy consumption and network lifetime. The proposed method also significantly enhances the first node dies and last node dies metrics compared with similar methods. (C) 2019 Elsevier B.V. All rights reserved.
机译：降低能量消耗和网络寿命是影响无线传感器网络（WSN）的主要挑战。高质量的聚类是降低WSN中能源消耗的最重要的方法之一。各种标准可用于评估群集的质量，并考虑所有这些标准可能导致高质量的聚类。在本研究中，我们提出了一种称为高质量聚类算法（HQCA）的方法，用于产生高质量的集群。 HQCA方法使用标准来测量群集质量，这可以改善群集间和群集间距离以及降低群集期间的错误率。基于模糊逻辑选择最佳簇头（CH），并且根据每个簇中的诸如剩余能量，最小和最大能量的各种标准，以及每个簇和基站的节点之间的最小和最大距离。这种方法的主要优点是其高可靠性，群集过程中的低差错率，关键CHS的独立性，更好的可扩展性，以及具有大量节点的大型网络中的良好性能。还基于外部和内部标准来测量聚类质量的有效性。仿真结果表明，HQCA-WSN方法可以显着提高能量消耗和网络寿命。所提出的方法还显着增强了与类似方法相比的第一节点模具和最后一节点测量。 （c）2019 Elsevier B.v.保留所有权利。

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