IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v88y2025i3d10.1007_s11235-025-01344-5.html
   My bibliography  Save this article

An AI-driven multi-stage routing protocol for energy-efficient IoT networks

Author

Listed:
  • Amin Nazari

    (Bu-Ali Sina University, Hamedan)

  • Seyedeh Shabnam Jazaeri

    (North Tehran Branch, Islamic Azad University)

  • Abolfazl Omidi

    (University of Lorstan)

  • Muharram Mansoorizadeh

    (Bu-Ali Sina University, Hamedan)

Abstract

The Internet of Things (IoT) has transformed data acquisition and decision-making across sectors, yet the limited energy of sensor nodes poses challenges for network longevity and efficiency. This paper proposes a multi-stage, multi-objective routing protocol using a hybrid of Ladybug Optimization (LBO), Butterfly Optimization Algorithm (BOA), and Q-learning. Virtual cluster heads are initially selected based on centrality and load balancing, followed by predictive energy-aware clustering to extend network life. Q-learning then enables dynamic, energy-efficient multi-hop routing based on energy levels and proximity. Simulation results show the method reduces energy consumption by up to 43% compared to FIAVOA in specific scenarios and extends network lifetime by up to 47% over GA-SDN. It also increases the number of alive nodes by up to 76% and delays the first node death time by up to 60%, enhancing network stability and coverage. These results underscore the approach’s effectiveness in sustaining IoT networks while ensuring efficient data transmission.

Suggested Citation

  • Amin Nazari & Seyedeh Shabnam Jazaeri & Abolfazl Omidi & Muharram Mansoorizadeh, 2025. "An AI-driven multi-stage routing protocol for energy-efficient IoT networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(3), pages 1-18, September.
    • Handle: RePEc:spr:telsys:v:88:y:2025:i:3:d:10.1007_s11235-025-01344-5
    DOI: 10.1007/s11235-025-01344-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-025-01344-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-025-01344-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:telsys:v:88:y:2025:i:3:d:10.1007_s11235-025-01344-5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.