Energy-Efficient Cluster Head Selection via Genetic Algorithm

Section: Research Paper
Issue
Vol. 29 No. 1 (2024): Volume 29 Issue 1
Published
Mar 1, 2024
Pages
12-25

Abstract

Environmental monitoring and industrial automation use WSNs extensively. Since sensor nodes have limited batteries, WSNs must be energy efficient. LEACH helps WSNs capture energy-efficient data. Cluster heads affect LEACH protocol energy consumption and network lifespan. This paper improves LEACH protocol cluster head selection with the genetic algorithm Algorithm. The program chooses cluster heads that maximize network energy efficiency. Cluster heads represent solutions in the Genetic Algorithm's genetic model. Energy efficiency measures fitness, selection, crossover, and mutation boost fitness. We extensively simulated to test our proposed strategy. We compared LEACH-GA, the original LEACH protocol, and various optimization methods. This article shows 100% network lifespan improvement compared to various routing protocols including; LEACH-C, FIGWO, GA-LEACH, PSO, ABC-SD, CGTABC2& ACO, LEACH, I-LEACH, I-LEACH. Whereas it gives 54% compared to ED-LEACH, and 28% compared to GADA-LEACH. The LEACH-GA algorithm outperforms the baseline LEACH algorithm and other algorithms in energy in terms of energy efficiency, network lifetime, and data aggregation. Our paper introduces a novel cluster head selection strategy for the LEACH protocol, which advances WSNs as Genetic Algorithms are integrated. The LEACH-GA algorithm increases energy efficiency and network longevity. Thus, it offers a feasible solution for energy-constrained WSN applications to help build and deploy effective WSN protocols, improving sensor network sustainability and dependability.

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Authors

Noor Raad Saadallah

Computer and Information Department, College of Electronics Engineering, Ninevah University, Mosul, Iraq

ORCID
Salah Abdulghani Alabady

Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq

ORCID
Fadi Al-Turjman

Artificial Intelligence, Software, and Information Systems Engineering Departments, AI and Robotics Institute, Near East University, Nicosia, Mersin10, Turkey

ORCID

Identifiers

https://doi.org/10.33899/rengj.2023.143955.1293
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How to Cite

[1]
N. Raad Saadallah, S. Abdulghani Alabady, and F. Al-Turjman, “Energy-Efficient Cluster Head Selection via Genetic Algorithm”, AREJ, vol. 29, no. 1, pp. 12–25, Mar. 2024.
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