Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-6· 0 citations· 10 references
Abstract
This work addresses the challenges of energy efficiency and Quality of Service (QoS) in dynamic Internet of Things (IoT) networks, where traditional routing protocols fail to adapt to changing conditions. To overcome these limitations, a Stochastically Enhanced Multi-Agent Federated Reinforcement Learning (SE-MA-FRL) framework is proposed. The method integrates multi-agent reinforcement learning with federated learning and stochastic policy exploration to enable distributed, privacy-preserving, and adaptive routing decisions. The model uses parameters such as residual energy, link quality, queue length, and distance to optimize routing. Simulation results demonstrate that the proposed approach achieves a Packet Delivery Ratio (PDR) of 96.96%, outperforming conventional and existing RL-based methods, while also reducing delay, packet loss, and energy consumption. In conclusion, SE-MA-FRL provides a scalable, reliable, and energy-efficient routing solution suitable for largescale and dynamic IoT environments with strict QoS requirements.
Simulation results validate the holistic integration with federated multi-agent learning, with emphasis on contention awareness and energy balancing is essential for scalable and efficient routing in next-generation dense FANETs.
H. Khujamatov, Elyanora Jolimbetova, Khaleel Ahmad et al.· Journal of Computer Science· 0 citations
A federated reinforcement learning framework for adaptive load balancing in the edge-fog-cloud continuum that optimizes energy efficiency and supports diverse quality of service requirements and uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning.
Si Liu, Midhun Chakkaravarthy· Future Technology· 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
The rapid advancement of technology like Internet of Things (IoT) and Cloud computing (CC)based heterogeneous environment required dynamic resource management system. Thecomplexity of IoT-Cloud is increasing due to abundance of dynamic data dissemination thatcreate poor performance like high energy consumption (EC), workload imbalancing, pooradaptability, and fail to handle SLA violations. The two primary contributions of the proposedwork are the Optimized Priority-Aware Hierarchical Multi-Agent Deep Q Network (OPHMDQN)and the Adaptive Multi-Objective Dung Beetle Optimization Algorithm (ADBOA). Themulti-agent strategies increase the scalability and adaptability of resource managementthrough local and global hierarchies. The approach integrates information-based decisionmakingand priority-aware allocation while accounting for SLA requirements, systemconstraints, and job complexity to optimise resource generation, utilisation, allocation, andtask scheduling. In comparison to existing optimization and Reinforcement Learning (RL)techniques, experimental results show that the proposed OPHM-DQN-ADBOA frameworkconsistently reduces EC (up to 30 % lower), execution delay, and SLA violations whileimproving resource utilisation and LB. The ADBOA enhances the proposed model throughoptimal multi-objective training, reducing EC, SLA violation, and cost while improvingresource utilization and scheduling efficiency. As a results, the model achieves high scalabilityand adaptability in heterogeneous IoT-Cloud resource management.
A. Ali, A. R. Mohamed Shanavas· THE SCIENTIFIC TEMPER· 0 citations
The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.
Elahe Eslami, S. A. Shahzadeh Fazeli, J. Abouei et al.· Cluster Computing· 0 citations
This study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs that leverages game-theoretic incentives and Q-learning to adaptively select strategies.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
