Energy-efficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization

Abstract

In the past few decades, cutting-edge information and communication technology has been used in several ways to keep an eye on the marine environment. Underwater wireless sensor networks (UWSNs) can measure the amount of water and soil conditions, such as soil salinity, moisture, and movements, to predict landslides. UWSNs are made up of many wireless underwater sensor nodes (WSNs) that are spread out across the thalassic environment. These networks have several uses, including data collection, navigation, resource analysis, surveillance, disaster prediction, etc. Nowadays, energy efficiency becomes a complex issue to handle in the design of the UWSN due to the limited battery capacity and the challenges associated with changing or charging the integrated batteries. According to previous research, clustering and routing have already been effective methods of improving energy efficiency in the UWSN, as unreplaceable batteries and long-distance communication delays are particularly vulnerable. As a result, one of the UWSN’s critical issues is determining how to extend the network’s lifespan while balancing its energy consumption and shortening transmission distances. In UWSN clustering, the most important considerations are acquiring a suitable count of clusters, constituting the clusters, and picking the most satisfactory cluster head (CH) for each cluster. Based on several factors, such as residuary energy, total energy consumption, and other considerations, our proposed approach picks CHs and arranges them into clusters. Also, the proposed SS-GSO method constructs a fitness function by including various sources of information, like total energy, residual energy, and luciferin value. Several simulation runs were executed to test how much better the SS-GSO approach worked. The comparison results showed that while evaluating clustering time, our proposed SS-GSO technique performs 22.91%, 50.03%, 42.42%, 58.06% better, in case of Total energy consumption 27.02%,14%,33.76%,41.97% more energy efficient, in Cluster lifetime 9.2%,19.88%,35.91%,40.54% less and in Packet delivery rate 8.29%,14.05%,17.67%,23.97% better as compared with other heuristic techniques, such as ACO, GWO, MFO and LEACH.

A Novel 3D Distributed Energy Efficient Clustering Algorithm for Underwater Acoustic Wireless Sensor Network

“…Figure 1 An illustration of Underwater Acoustic Communication [15] Figure 2 UAWSN deployment algorithm [20] Figure 3 Structure of Underwater Acoustic Wireless Sensor Networks [23] Page 19/26 Clustering Structure of Underwater Acoustic Wireless Sensor Networks [23] Figure 5 3D clustering structure of Underwater Acoustic Wireless Sensor Networks…”

OEE-WCRD: Optimizing Energy Efficiency in Wireless Sensor Networks through Cluster Head Selection Using Residual Energy and Distance Metrics

“…Bahadur, L. et al [39] presents a novel method for optimizing energy consumption in wireless sensor networks using genetic algorithms, providing insights into efficient energy management. Bharany Salil et al [40] introduces an energyefficient clustering protocol for underwater wireless sensor networks using optimized glowworm swarm optimization, contributing to improved underwater sensor network performance. Reddy et al [41] focuses on energy-efficient cluster head selection in wireless sensor networks, utilizing an improved Grey Wolf Optimization Algorithm to enhance network efficiency.…”

Underwater Efficient Data Routing: Clustering-Travel Salesman Protocol (CTSP)

“…Their main job is to gather information underwater and send it to sites on land or other points. To reach this goal, nodes must be able to work together without any problems [28]. But underwater communication is hard because of things in the world, like how water significantly distorts signals, which is made worse by objects including rocks, reefs, and marine life [29].…”