RL-Based MAC Protocol For Multi-Goal Industrial Wireless Networks

Industrial environments feature heterogeneous data sources with evolving and often conflicting communication requirements. This dynamic, multi-goal setting poses significant challenges, particularly due to the strict and variable demands on key performance metrics, such as latency and reliability, crucial for wireless networks enabling Industrial Internet of Things (IIoT) applications. Indeed, some sensors require low latency and high success probability, while others can tolerate delays, and these priorities can change over time as industrial processes evolve. Optimizing Medium Access Control (MAC) protocols in such conditions requires an accurate understanding of the temporal and spatial variability of sensor needs, something that traditional MAC designs are not well equipped to handle. To address this, we propose a Reinforcement Learning (RL)-based approach for MAC optimization in industrial networks. The proposed model builds upon a Carrier Sense Multiple Access (CSMA) MAC protocol, adaptively assigning Contention Window (CW) values to individual sensors to reflect the evolving communication demands of industrial processes. Its performance is evaluated against traditional benchmark approaches that rely on fixed CW configurations. Results demonstrate that the proposed solution dynamically adjusts network behavior to meet multi-goal requirements, satisfying both latency and reliability constraints by aligning CW assignments with sensor-specific needs in real time.