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

Amin Nazari (), Seyedeh Shabnam Jazaeri (), Abolfazl Omidi () and Muharram Mansoorizadeh ()
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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

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 3, No 33, 18 pages

Abstract: 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.

Keywords: Internet of Things; Energy-efficient Routing; Ladybug Optimization; Butterfly Optimization Algorithm; Q-learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-025-01344-5

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