Addressing Mobility Challenges in LoRaWAN through Adaptive Data Rate: A Statistical Median-Based Approach

The Internet of Things has revolutionized interconnected computing systems, with applications spanning healthcare, transportation, agriculture, and smart meters. In this context, LoRaWAN technology plays a key role in connecting IoT devices over extended distances with minimal energy consumption, particularly with the assistance of ADR, a mechanism that aims to optimize network performance. However, standard ADR, available in the LoRaWAN specification, faces limitations when the channel presents variable conditions, like in scenarios involving mobility, impacting transmission reliability. In response, this paper proposes an alternative approach, named Median-based ADR (MB-ADR), leveraging statistical techniques, specifically the median, to adjust the link quality indicator in the ADR algorithm. The evaluation of the proposed solution involves simulations considering device mobility and multiple gateways. The results demonstrate the superior performance of MB-ADR compared to ADR and other methods proposed in the literature, with an enhancement in average PDR of 15.61% over standard ADR, 16.67% over M-ADR, and 21.87% over ADR+ in scenarios with multiple gateways and high packet traffic. Additionally, MB-ADR shows better energy efficiency when compared to other ADR methods under high scalability conditions. Overall, this study establishes MB-ADR as a robust and efficient alternative for LoRaWAN networks, particularly in dynamic and high-traffic IoT scenarios, contributing to reduced transmission delays.