Bandwidth Fair Share Using Reinforcement Learning for IoT Networks

The exponential growth of the Internet of Things (IoT) is driven by advancements in wireless communication, data analytics, and sensor technology, leading to a proliferation of smart devices and applications. To keep pace with the massive data volumes generated by IoT networks, dynamic features and resource constraints should be carefully considered compared with traditional networks, especially for traffic Congestion Control (CC). Reinforcement Learning (RL) based congestion control has shown high potential in dealing with network congestion. However, RL-based schemes mandate expensive resource requirements (at the sender side) and impaired fairness performance problems. This paper addresses this shortcoming by proposing a lightweight RL-based Congestion Control scheme that overcomes this problem. In particular, we propose an intelligent, RL-based, “Share by Allocating switch Buffer” CC scheme capable of overcoming congestion by minimizing intermediate switch buffer overflow. Our scheme termed SAB-RL has been extensively tested and shown to tackle congestion at its root, thereby outperforming other CC proposals.