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

Abstract

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.