Mobility Adaptive Data Rate Based on Kalman Filter for LoRa-Empowered IoT Applications

LoRaWAN is a low-power wide-area network technology that has become the de-facto for the Internet of Things (IoT) due to its low cost, ultra-low energy consumption, longrange, and support for the massive end devices (EDs). Adaptive data rate (ADR) is the most widely adopted approach for resource assignment with spreading factor (SF) and transmission power (TP) to massive EDs in the LoRaWAN network, recommended for static IoT applications such as metering. However, in a mobile IoT environment, ADR fails to adjust the resources due to dramatic changes in the signal strength owing to the underlying dynamic environment, resulting in massive packet loss and retransmissions. To assign suitable SF and TP parameters to mobile IoT EDs, we propose mobility adaptive data rate (M-ADR) using Kalman Filter. The proposed M-ADR determines the ED status (i.e., either static or mobile) by finding the distance between the previous and current positions of the ED at the NS. When the ED status is determined as mobile, we propose utilizing Kalman Filter to estimate the signal-to-noise ratio (SNR) to accurately determine SF, TP, or both, as these parameters are primarily dependent on SNR. When the Kalman Filter decides the current estimate of the system, the proposed M-ADR further finds the best possible configuration of the SF and TP. Simulation results show that the proposed M-ADR enhanced the packet success ratio by 16.88% compared with the state-of-the-art ADR of LoRaWAN.