IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i22p3447-d1514142.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/22/3447/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/22/3447/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Zhe & Song, Xiaoling & Gong, Xue & Yin, Yong & Lev, Benjamin & Zhou, Xiaoyang, 2024. "Coordinated seru scheduling and distribution operation problems with DeJong’s learning effects," European Journal of Operational Research, Elsevier, vol. 313(2), pages 452-464.
    2. Eduardo Queiroga & Anand Subramanian & Rosa Figueiredo & Yuri Frota, 2021. "Integer programming formulations and efficient local search for relaxed correlation clustering," Journal of Global Optimization, Springer, vol. 81(4), pages 919-966, December.
    3. Rivera, Juan Carlos & Murat Afsar, H. & Prins, Christian, 2016. "Mathematical formulations and exact algorithm for the multitrip cumulative capacitated single-vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 249(1), pages 93-104.
    4. Morais, Rafael & Bulhões, Teobaldo & Subramanian, Anand, 2024. "Exact and heuristic algorithms for minimizing the makespan on a single machine scheduling problem with sequence-dependent setup times and release dates," European Journal of Operational Research, Elsevier, vol. 315(2), pages 442-453.
    5. Olympia Roeva & Dafina Zoteva & Gergana Roeva & Maya Ignatova & Velislava Lyubenova, 2024. "An Effective Hybrid Metaheuristic Approach Based on the Genetic Algorithm," Mathematics, MDPI, vol. 12(23), pages 1-16, December.
    6. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1085-1099.
    7. F. Angel-Bello & Y. Cardona-Valdés & A. Álvarez, 2019. "Mixed integer formulations for the multiple minimum latency problem," Operational Research, Springer, vol. 19(2), pages 369-398, June.
    8. Samuel Nucamendi-Guillén & Iris Martínez-Salazar & Francisco Angel-Bello & J Marcos Moreno-Vega, 2016. "A mixed integer formulation and an efficient metaheuristic procedure for the k-Travelling Repairmen Problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(8), pages 1121-1134, August.
    9. Shuxin Liu & Jing Xu & Chaojian Xing & Yang Liu & Ersheng Tian & Jia Cui & Junzhu Wei, 2023. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    10. Lysgaard, Jens & Wøhlk, Sanne, 2014. "A branch-and-cut-and-price algorithm for the cumulative capacitated vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 236(3), pages 800-810.
    11. Ajam, Meraj & Akbari, Vahid & Salman, F. Sibel, 2022. "Routing multiple work teams to minimize latency in post-disaster road network restoration," European Journal of Operational Research, Elsevier, vol. 300(1), pages 237-254.
    12. Mateusz Malarczyk & Grzegorz Kaczmarczyk & Jaroslaw Szrek & Marcin Kaminski, 2023. "Internet of Robotic Things (IoRT) and Metaheuristic Optimization Techniques Applied for Wheel-Legged Robot," Future Internet, MDPI, vol. 15(9), pages 1-19, September.
    13. Moshref-Javadi, Mohammad & Lee, Seokcheon & Winkenbach, Matthias, 2020. "Design and evaluation of a multi-trip delivery model with truck and drones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    14. Naveed, Muhammad Hamza & Khan, Muhammad Nouman Aslam & Mukarram, Muhammad & Naqvi, Salman Raza & Abdullah, Abdullah & Haq, Zeeshan Ul & Ullah, Hafeez & Mohamadi, Hamad Al, 2024. "Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    15. Jan Mikula & Miroslav Kulich, 2022. "Solving the traveling delivery person problem with limited computational time," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(4), pages 1451-1481, December.
    16. Yasir Adil Mukhlif & Nehad T. A. Ramaha & Alaa Ali Hameed & Mohammad Salman & Dong Keon Yon & Norma Latif Fitriyani & Muhammad Syafrudin & Seung Won Lee, 2024. "Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review," Mathematics, MDPI, vol. 12(7), pages 1-29, March.
    17. Mohammed Qasim Taha & Sefer Kurnaz, 2023. "Droop Control Optimization for Improved Power Sharing in AC Islanded Microgrids Based on Centripetal Force Gravity Search Algorithm," Energies, MDPI, vol. 16(24), pages 1-20, December.
    18. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).
    19. Bruni, M.E. & Khodaparasti, S. & Beraldi, P., 2020. "The selective minimum latency problem under travel time variability: An application to post-disaster assessment operations," Omega, Elsevier, vol. 92(C).
    20. Ha-Bang Ban, 2021. "A metaheuristic for the delivery man problem with time windows," Journal of Combinatorial Optimization, Springer, vol. 41(4), pages 794-816, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3447-:d:1514142. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.