An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications.

Autor: Gökhan Nalbant K; Faculty of Engineering Architecture, Department of Software Engineering, Istanbul Beykent University, Sariyer, Istanbul, Turkey., Alsuhibany SA; Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia., Hassan Alshehri A; Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia., Hatira M; Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen.; Islamic University Centre for Scientific Research, The Islamic University, Najaf, Iraq., Choi BJ; School of Computer Science and Engineering, Soongsil University, Seoul, South Korea.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Jun 20; Vol. 19 (6), pp. e0301078. Date of Electronic Publication: 2024 Jun 20 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0301078
Abstrakt: Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Gökhan Nalbant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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