An IoT-Driven Reinforcement Learning Framework for Optimized Flow Management in Autonomous Systems

In this paper, we introduce a novel framework designed specifically for federated reinforcement learning in IoT-driven networks, focusing on flow management in autonomous systems. Our framework optimizes flow table matching by monitoring IoT network traffic and ensuring efficient flow management across connected devices. By considering the specific flow requirements of IoT traffic, our framework enables an intelligent agent to make informed decisions regarding flow table entries, thereby improving the performance and management of autonomous systems. To gather essential data for decision-making, our framework utilizes an IoT-based SDN module that collects traffic statistics and relevant information from the network’s data plane. By leveraging SDN, our system enhances the learning and decision-making capabilities of IoT devices within autonomous systems. We introduce an optimization model called Software-Defined Network Assisted Federated Reinforcement Learning (SORE), based on the Markov decision process. SORE capitalizes on the advantages of SDN to boost the overall performance of IoT-based autonomous systems. By applying reinforcement learning techniques, our framework demonstrates significant improvements over existing models. Extensive simulations validate the effectiveness of our proposed system, showcasing superior performance and efficiency in IoT-enabled wireless networks within autonomous systems. The results underscore the potential of our approach in real-world IoT deployments.