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Article

Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks

by
Umar Draz
1,
Tariq Ali
2,*,
Sana Yasin
3,*,
Muhammad Hasanain Chaudary
4,
Muhammad Ayaz
2,
El-Hadi M. Aggoune
2 and
Isha Yasin
3
1
Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan
2
Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
3
Department of Computer Science, University of Okara, Okara 56300, Pakistan
4
Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 45550, Pakistan
*
Authors to whom correspondence should be addressed.
Mathematics 2024, 12(22), 3447; https://doi.org/10.3390/math12223447
Submission received: 27 September 2024 / Revised: 30 October 2024 / Accepted: 31 October 2024 / Published: 5 November 2024
(This article belongs to the Special Issue Innovations in Optimization and Operations Research)
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Abstract

This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network.
Keywords: bio-inspired; genetic algorithm; beacon node localization; energy reduction; metaheuristic algorithms bio-inspired; genetic algorithm; beacon node localization; energy reduction; metaheuristic algorithms

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MDPI and ACS Style

Draz, U.; Ali, T.; Yasin, S.; Chaudary, M.H.; Ayaz, M.; Aggoune, E.-H.M.; Yasin, I. Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks. Mathematics 2024, 12, 3447. https://doi.org/10.3390/math12223447

AMA Style

Draz U, Ali T, Yasin S, Chaudary MH, Ayaz M, Aggoune E-HM, Yasin I. Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks. Mathematics. 2024; 12(22):3447. https://doi.org/10.3390/math12223447

Chicago/Turabian Style

Draz, Umar, Tariq Ali, Sana Yasin, Muhammad Hasanain Chaudary, Muhammad Ayaz, El-Hadi M. Aggoune, and Isha Yasin. 2024. "Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks" Mathematics 12, no. 22: 3447. https://doi.org/10.3390/math12223447

APA Style

Draz, U., Ali, T., Yasin, S., Chaudary, M. H., Ayaz, M., Aggoune, E.-H. M., & Yasin, I. (2024). Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks. Mathematics, 12(22), 3447. https://doi.org/10.3390/math12223447

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