Enhancing LoRaWAN Communication for Mobile Nodes with Techniques for Predicting Signal Strength

Abstract

The Internet of Things (IoT) has enabled a wide range of applications that depend on efficient and reliable communication between devices, even in remote or mobile scenarios. However, LoRaWAN communication faces significant challenges when nodes are mobile. This study investigates methods for predicting and improving the reliability and energy efficiency of mobile LoRaWAN communication. The Kalman filter, a lightweight yet robust algorithm, is applied to smooth noisy signal measurements and enhance decision-making. Field experiments in urban, rural, park, and free-field environments demonstrate that predictive filtering can effectively stabilize the highly variable RSSI and SNR signals typical of mobile devices, providing a more dependable basis for transmission parameter control. The performance is benchmarked against a naive reactive control strategy and further contextualized within the framework of standard LoRaWAN Adaptive Data Rate (ADR) mechanisms. Results indicate that traditional ADR, designed for stationary devices, remains overly conservative in mobile situations, maintaining higher transmission power than necessary. By contrast, predictive filtering achieves tangible energy savings without compromising reliability. This work represents a meaningful step toward more resilient and efficient LoRaWAN systems capable of supporting the mobile and dynamic applications of tomorrow’s IoT landscape.