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Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions

Yousef Jaradat*, Mohammad Masoud, Ismael Jannoud, Dema Zeidan

Department of Electrical Engineering, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan

* Corresponding Author: Yousef Jaradat. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 791-808. https://doi.org/10.32604/iasc.2022.024218

Abstract

Optimal node clustering in wireless sensor networks (WSNs) is a major issue in reducing energy consumption and extending network node life time and reliability measures. Many techniques for optimizing the node clustering process in WSN have been proposed in the literature. The metaheuristic algorithms are a subset of these techniques. Genetic algorithm (GA) is an evolutionary metaheuristic technique utilized to improve the network reliability and extending the network life time by optimizing the clustering process in the network. The GA dynamic clustering (GA-DC) algorithm is proposed in this paper to extend the network reliability and node life time of three dimensional (3D) WSN. The GA-DC algorithm made use of an improved fitness function that takes into account a variety of metrics such as energy expenditure per protocol round, clustering distance, and the number of long-distance wireless connections. There have been two types of simulation scenarios run. First, simulation results show that the GA-DC algorithm increases network life time by 80% and network throughput by 55% when compared to the well-known LEACH protocol. Second, simulation results show that the uniform node distribution outperforms the normal and exponential distributions in terms of network life time by 5.7% and 7%, network reliability by 4.2% and 76%, and data throughput by 10.85% and 19.54%, respectively

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APA Style
Jaradat, Y., Masoud, M., Jannoud, I., Zeidan, D. (2022). Genetic algorithm energy optimization in 3D wsns with different node distributions. Intelligent Automation & Soft Computing, 33(2), 791-808. https://doi.org/10.32604/iasc.2022.024218
Vancouver Style
Jaradat Y, Masoud M, Jannoud I, Zeidan D. Genetic algorithm energy optimization in 3D wsns with different node distributions. Intell Automat Soft Comput . 2022;33(2):791-808 https://doi.org/10.32604/iasc.2022.024218
IEEE Style
Y. Jaradat, M. Masoud, I. Jannoud, and D. Zeidan "Genetic Algorithm Energy Optimization in 3D WSNs with Different Node Distributions," Intell. Automat. Soft Comput. , vol. 33, no. 2, pp. 791-808. 2022. https://doi.org/10.32604/iasc.2022.024218



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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