Energy-Efficient Clustering Algorithm in Underwater Sensor Networks Based on Fuzzy C Means and Moth-Flame Optimization Method

作     者：Wang Fei Bai Hexiang Li Deyu Wang Jianjun 

作者机构：Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Grain & Oils Informat Ctr Jiangsu Prov Nanjing 210008 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：97474-97484页

核心收录：

基　　金：National Natural Science Foundation of China [61672331, 41871286] Key R&D program of Shanxi Province [201903D421041] 

主　　题：Clustering algorithms Energy consumption Energy efficiency Clustering methods Wireless sensor networks Voting Optimization methods UWSN clustering algorithm fuzzy C means moth-flame optimization 

摘      要：Underwater sensor networks (UWSN) often suffers from the irreplaceable batteries and high delay of long-distance communications, thus one of the most important issues on UWSN is how to extend the lifespan of the network and balance the energy consumption of each node by reducing the transmission distances. Actually, clustering method is one of the main methods to resolve the problem. In the clustered UWSN, the major concerns are obtaining appropriate number of clusters, forming the clusters and selecting an optimal cluster head(CH) with each cluster. This paper proposes a novel hybrid clustering method based on fuzzy c means (FCM) and moth-flame optimization method (MFO) to improve the performance of the network(FCMMFO). The idea is to form energy-efficient clusters by using FCM and then use an optimization algorithm MFO to select the optimal CH within each cluster. The simulation results validate the energy-efficient performance of FCMMFO in comparison with the other existing algorithms. The results clearly show the significant impact of FCMMFO on energy-efficiency in UWSN.