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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Umar Draz & Tariq Ali & Sana Yasin & Muhammad Hasanain Chaudary & Muhammad Ayaz & El-Hadi M. Aggoune & Isha Yasin, 2024. "Hybridization and Optimization of Bio and Nature-Inspired Metaheuristic Techniques of Beacon Nodes Scheduling for Localization in Underwater IoT Networks," Mathematics, MDPI, vol. 12(22), pages 1-29, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3447-:d:1514142
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    References listed on IDEAS

    as
    1. Silva, Marcos Melo & Subramanian, Anand & Vidal, Thibaut & Ochi, Luiz Satoru, 2012. "A simple and effective metaheuristic for the Minimum Latency Problem," European Journal of Operational Research, Elsevier, vol. 221(3), pages 513-520.
    2. Ghassan Husnain & Shahzad Anwar & Gulbadan Sikander & Armughan Ali & Sangsoon Lim, 2023. "A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)," Energies, MDPI, vol. 16(3), pages 1-20, February.
    3. Kwang-il Hwang & Sung-wook Nam, 2014. "Analysis and Enhancement of IEEE 802.15.4e DSME Beacon Scheduling Model," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-15, May.
    4. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    5. Marco Dorigo & Thomas Stützle, 2019. "Ant Colony Optimization: Overview and Recent Advances," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 311-351, Springer.
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