Evaluating scalability of median-based ADR under different mobility conditions

The LoRaWAN protocol is widely used in Internet of Things (IoT) applications due to its ability to provide long-range, low-power communication. The Adaptive Data Rate (ADR) mechanism dynamically adjusts transmission parameters to optimize energy consumption. However, ADR is primarily designed for static devices, which limits its effectiveness in mobile environments, where fluctuating signal conditions can degrade performance. To address this limitation, the Median-Based ADR (MB-ADR) scheme was introduced, leveraging statistical measures to improve ADR adaptability to changing channel conditions. This study evaluates the scalability of MB-ADR in networks with up to 1,000 end devices and node speeds of up to 20 m/s, considering mobility models such as Random Walk and Gauss–Markov. The results show that MB-ADR demonstrates superior performance in scenarios with realistic mobility patterns, particularly under the Steady-State Random Waypoint model, resulting in improvements of up to 15% in Packet Delivery Ratio (PDR) and 55% in energy efficiency compared to a Kalman filter-based scheme under the same mobility model. Additionally, the analysis demonstrates the effectiveness of MB-ADR in improving throughput and reducing collisions by promoting an efficient distribution of spreading factors. Overall, the study confirms the potential of MB-ADR to enhance communication reliability and energy efficiency in mobile IoT networks, making it a viable solution for large-scale, high-density IoT deployments with variable mobility.