RLIOT: REINFORCEMENT LEARNING - BASED NETWORK RESOURCE OPTIMIZATION USING IOT SENSOR DATA
The fast rise of wireless communication networks, including 6G, Internet of Things (IoT), and edge com puting, has created unprecedented demand for spectrum and energy resources.become a significant challenge in modern IoT networks due to heterogeneous devices, dynamic traffic patterns, and diverse QoS requirements. This study proposes a Deep Reinforcement Learning (DRL)–basedframework for optimizing network resource allocation in IoT environments using real-world sensor data. The proposed framework differs from existing studies that typically assess reinforcement learning methodologies under simplified wireless network assumptions and idealized conditions. Our method functions on heterogeneous IoT traffic produced by various device types, including sensors, actuators, and cameras, each possessing distinct Quality of Service (QoS) requirements. To ensure practical applicability, a realistic IoT simulation environment is developed, incorporating dynamic bandwidth release and queue-aware resource management to emulate real-world network behavior. Furthermore, a Deep Q-Network (DQN) agent with an enhanced exploration strategy is designed to improve learning stability and convergence performance, enabling more efficient and adaptive resource allocation in dynamic IoT scenarios. Experimental results show that the proposed DQN agent achieves a 26.7% improvement in cumulative reward compared to a random policy and consistently outperforms conventional heuristic approaches. This significant gain indicates that the agent effectively learns a structured resource allocation strategy rather than making uninformed decisions. These results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.