Reinforcement Learning for Adaptive Congestion Control in IoT Networks: A Deep Q-Network Approach
Keywords:
Congestion control, Deep Q-Network (DQN), Internet of Things (IoT), Reinforcement Learning (RL), Transmission Control Protocol (TCP)Abstract
This study presents a Reinforcement Learning (RL)-based approach for congestion control in Internet of Things (IoT) networks, addressing the limitations of traditional methods such as Transmission Control Protocol (TCP). The proposed system leverages a Deep Q-Network (DQN) model to dynamically adapt transmission strategies based on real-time network conditions. A simulation framework was developed using Python, integrating PyTorch for the RL model, NumPy for numerical computations, Matplotlib for visualization, and PyQt5 for an interactive dashboard. The performance of the RL-based approach was evaluated against TCP using key metrics, including throughput, latency, packet loss, and energy consumption. The results demonstrate that the RL model significantly enhances network performance, achieving higher throughput while reducing latency and packet loss. Additionally, the approach shows improved energy efficiency, making it suitable for resource-constrained IoT environments. The findings confirm that RL-based congestion control provides a more adaptive, efficient, and intelligent solution compared to conventional techniques. This work contributes to the development of next-generation IoT systems by integrating artificial intelligence into network management, offering promising applications in smart cities, healthcare, and intelligent transportation systems.
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