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

Wang Fei,Wang Jianjun,Li Deyu,Bai Hexiang

doi:10.1109/access.2020.2997066

Abstract

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.

Highlights

Being self-organized and -deployed, underwater sensor network (UWSN) is suitable for target detection, target tracking and parameter monitoring in large-area waters [1]
OUR CONTRIBUTIONS 1) To the best of our knowledge, this is the first work that applies the hybrid method with fuzzy c means (FCM) and MFO algorithms to improve the performance of Underwater sensor networks (UWSN). 2) We use FCM to cluster the network, and use MFO to elect the optimal cluster heads
We use elbow method to determine the optimal number of the clusters, and implement FCM to decide the locations of the initial cluster heads.. 3) We construct the fitness function after taking into consideration of the following: the distances between nodes and candidate cluster heads, candidate cluster heads, sink node, and energy consumption of the network

Summary

INTRODUCTION

Being self-organized and -deployed, underwater sensor network (UWSN) is suitable for target detection, target tracking and parameter monitoring in large-area waters [1]. We propose a hybrid method for clustering and selecting of cluster heads based on FCM and MFO. This method can effectively reduce the energy consumption of the networks and balance the load of the sensor nodes. In literature [13], Bhatti proposed fuzzy c-means clustering and energy-efficient CH selection for cooperative sensor networks to save energy, in which clusters are formed using FCM method and CHs is selected based on the parameters like location, SNR and residual energy of the nodes. The clustering algorithm aims at reducing the communication distances and energy consumption, so we should determine the optimal locations of the cluster heads. We will construct the objective function first, and resolve the optimization problem through MFO method

ENERGY MODEL IN UWSN

SELECTION OF THE CLUSTER HEADS BY MFO

Proposed Method to Select the Cluster Heads

Findings

CONCLUSION