Multi Objective Energy Based Improved Jellyfish Swarm Optimization for Effective Cluster Head Discovery in UWSN

Abstract

Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN.

Energy Optimized Cluster Head Selection Based on Multi-Objective Sand Cat Swarm Optimization in Under Water Wireless Sensor Networks

“…Gowda and Ramalingappa [18] have introduced multi-objective energy-based improved jellyfish swarm optimization (MOEIJSO) to perform CH selection and routing UWSN. The multi-hop routing was created by employing ant colony optimization (ACO) to distribute data packets.…”

“…In this section, outcomes of proposed method are estimated with existing approaches like EPO-Q [16], MOEIJSO-ACO [18] and CEPOC [21]. The performance of the proposed MSCSO and the existing EPO-Q is evaluated based on PDR, PLR and throughput for different node counts ranging from 100 to 500.…”