Stochastically Enhanced Multi-Agent Federated Reinforcement Learning for Energy-Efficient and Quality-of-Service-Aware IoT Routing

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.