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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
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    References listed on IDEAS

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    1. Shashank Singh & Veena Anand, 2023. "Load balancing clustering and routing for IoT‐enabled wireless sensor networks," International Journal of Network Management, John Wiley & Sons, vol. 33(5), September.
    2. Nelofar Aslam & Kewen Xia & Muhammad Tafseer Haider & Muhammad Usman Hadi, 2017. "Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks," Future Internet, MDPI, vol. 9(4), pages 1-21, September.
    3. Michaelraj Kingston Roberts & Poonkodi Ramasamy, 2023. "An improved high performance clustering based routing protocol for wireless sensor networks in IoT," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(1), pages 45-59, January.
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