Energy efficient metaheuristic cluster-based routing protocol for underwater sensor networks

Abstract

Recently, underwater wireless sensor networks (UWSNs) have been employed in the marine environment to forecast landslides by determining the amount of water and soil conditions, such as soil salinity, wetness, and movement. It is used in a variety of applications, including data collecting, disaster prediction, resource inquiry, marine surveillance, and so on. Energy efficiency becomes a challenge in UWSN since the nodes function on inherent energy and it can be difficult to exchange the power supply. This study focuses on developing an energy-efficient metaheuristic cluster-based routing protocol for UWSN, known as the EEMCBR-UWSN approach. The primary goal of the EEMCBR-UWSN technology is to improve energy efficiency through clustering and routing procedures. EEMCBR-UWSN technique uses a two-stage process. Initially, the spotted hyena optimization algorithm-based clustering (SHOA-C) method was used to arrange nodes in UWSN and pick appropriate cluster heads. The SHOA-C approach creates a fitness function for selecting CHs based on energy usage. Furthermore, the tumbleweed optimization algorithm-based routing (TWOA-R) approach was employed in the second stage to find the best routes in the UWSN. For route selection, the TWOA-R approach creates a fitness function based on energy, distance, and node degree. The simulation results were suggested that the EEMCBR-UWSN method outperforms traditional techniques by effectively enhancing the cluster head selection and routing processes.