Adaptive Mobility Control in LoRaWAN via Reinforcement Learning

Low-Power Wide Area Networks (LPWANs), such as LoRaWAN, are pivotal for large-scale IoT deployments. However, traditional stationary gateways (GWs) impose scalability and cost constraints. We propose an AI-driven mobile GW architecture that leverages reinforcement learning (RL) to dynamically adapt GW mobility based on real-time network conditions. Our custom mobility module, integrated into OMNeT++ with FLoRa and TensorFlow Lite Micro, enables on-the-fly decision-making to optimize Packet Delivery Ratio (PDR) and fairness. The results of the simulations show that the introduced RL-GW achieves up to 99.94% PDR in high-fidelity simulations, outperforming static and heuristic mobile strategies. The RL policy generalizes robustly across network sizes and scenarios, offering a scalable, low-overhead solution for adaptive LPWAN infrastructure.