Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 24 references
Medicine
TL;DR
Simulation results show that the proposed MARL-BA-ABC framework outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA), and Artificial Bee Colony (ABC), achieving a First Node Death of 2200 rounds.
Abstract
Energy efficiency remains a major challenge in IoT-enabled wireless sensor networks because sensor nodes operate with limited battery capacity and are often deployed in environments where battery replacement is impractical. Existing clustering and routing protocols frequently optimize cluster-head selection and routing separately, leading to uneven energy consumption, premature node failure, increased routing overhead, and reduced network reliability. This paper proposes an Adaptive Multi-Agent Reinforcement Learning-Assisted Hybrid Bat-Artificial Bee Colony (MARL-BA-ABC) framework for joint cluster-head selection and routing optimization in IoT-enabled wireless sensor networks. The proposed framework combines the Bat Algorithm for local exploitation, the Artificial Bee Colony algorithm for global exploration, and Multi-Agent Reinforcement Learning for adaptive routing. Cluster-head selection and routing are jointly optimized using residual energy, communication distance, traffic load, node density, and link quality. A multi-objective optimization model is formulated to minimize energy consumption, end-to-end delay, routing overhead, and load imbalance while improving packet delivery ratio, residual energy preservation, and network lifetime. Simulation results show that the proposed MARL-BA-ABC framework outperforms Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed Clustering (HEED), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA), and Artificial Bee Colony (ABC), achieving a First Node Death of 2200 rounds, residual energy of 1.32 J, packet delivery ratio of 98.1%, throughput of 410 kbps, and average end-to-end delay of 7.4 ms.
ThGCDTR-RP is proposed, an energy-efficient clustering and routing protocol that integrates Grey Wolf Optimizer, Cheetah Optimizer, and Differential Evolution for cluster-head (CH) selection that consistently outperforms LEACH, LPSO, LGWO, WOA-P, and LACO.
Xuan Yang, Jiaqi Yan, Desheng Wang et al.· Journal of King Saud Univers...· 0 citations
Simulations conducted in MATLAB R2019a validate that the proposed MOPSO outperforms existing algorithms such as LEACH, LEACH-FL, LEACH-FC, KM-PSO, EECHS-ARO, HSWO, and EECHIGWO by mitigating premature convergence and enhancing CH selection accuracy.
Monisha Gupta, Chandrasekar Vadivelraju· Review of Computer Engineeri...· 0 citations
This study proposes a Fault-Tolerant Backup Cluster Head with Ant Colony Optimization (FT-BKCH-ACO) approach, offering a scalable and energy-efficient solution for large-scale sensor networks.
Manisha Chandrakar, A. Hasan· International Journal of Wir...· 0 citations
This study suggests an intelligent clustering protocol in Wireless Sensor Networks, called RL-ILEACH, which incorporates Reinforcement Learning (RL) into the ILEACH (Improved Low-Energy Adaptive Clustering Hierarchy) framework for adaptive and energy-aware CH selection in order to overcome these drawbacks. An RL agent is used in the suggested RL-ILEACH protocol to learn the best CH selection strategies depending on current network conditions, such as residual energy, node density, and communication distance. RL-ILEACH reduces energy dissipation and improves load balancing during intra-cluster and inter-cluster communication phases by dynamically adjusting CH selection options. To assess RL-ILEACH’s performance against traditional procedures, such as LEACH, ILEACH, and other cutting-edge clustering techniques, extensive simulation tests are carried out. According to simulation studies, RL-ILEACH performs noticeably better than current protocols, resulting in reduced total energy consumption, a longer stability period, a greater packet delivery ratio, and a longer network lifetime. In particular, RL-ILEACH minimizes early node failures brought on by uneven energy depletion and sustains a larger number of active nodes throughout subsequent rounds. RL-ILEACH is a reliable and scalable solution for energy-efficient clustering in dynamic Wireless Sensor Network settings, as these enhancements demonstrate that the incorporation of Reinforcement Learning permits intelligent, adaptive decision-making. RL-ILEACH prolonged the network lifetime by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:784\:\:$$\end{document}% compare to LEACH. RL-ILEACH outperformed ILEACH by increasing the network lifetime by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:130$$\end{document}%. Moreover, relative to NNMH-LEACH, RL-ILEACH achieved a further \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:108$$\end{document}% imporvment in network lifetime.
H. Elsayed, Elham M. Abd-Elgaber, Shereen K. Refaay· Scientific Reports· 0 citations
This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.
Mehdi Hosseinzadeh, Parisa Khoshvaght, Amir Masoud Rahmani et al.· Cluster Computing· 0 citations
This WBOAICM scheme uses a fitness function formulated using delay, energy, distance, jitter, and packet forwarding potential to facilitate energy and trustful nodes to be selected as CHs in the clustering process, which minimize energy utilization to extend network lifetime.
J. Rani, D. Santhakumar· International Journal of Com...· 0 citations
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