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

Umar Draz, Tariq Ali (), Sana Yasin (), Muhammad Hasanain Chaudary, Muhammad Ayaz, El-Hadi M. Aggoune and Isha Yasin
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Umar Draz: Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan
Tariq Ali: Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
Sana Yasin: Department of Computer Science, University of Okara, Okara 56300, Pakistan
Muhammad Hasanain Chaudary: Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 45550, Pakistan
Muhammad Ayaz: Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
El-Hadi M. Aggoune: Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia
Isha Yasin: Department of Computer Science, University of Okara, Okara 56300, Pakistan

Mathematics, 2024, vol. 12, issue 22, 1-29

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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