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Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.63-69

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


Hristijan Slavkoski, Simeon Trendov, Eduard Siemens and Marija Kalendar


Abstract: Mobile LoRaWAN links suffer from rapid RSSI/SNR fluctuations due to motion, obstacles, and interference, degrading reliability and wasting energy. This work evaluates lightweight signal-strength prediction combined with adaptive control on resource-constrained hardware. A Kalman filter is applied to smooth per-packet RSSI/SNR and to trigger parameter updates to transmit power, spreading factor, and coding rate only when persistent degradation is detected. The approach is implemented on an Arduino sender and a Raspberry Pi receiver and tested in urban, rural, park, and free-field environments. Results show variance reductions in RSSI of about one third and SNR of about one fifth, translating into energy savings of 15–27% without loss of reliability. Compared with a reactive baseline and the principles of LoRaWAN ADR, the method responds faster to recovery and avoids prolonged high-power operation in mobility. The findings indicate that simple predictive filtering is an effective building block for robust and energy-efficient mobile LoRaWAN systems.

Keywords: Adaptive Control, Adaptive Data Rate (ADR), Coding Rate (CR), Energy Efficiency, Internet of Things (IoT), Kalman Filter, LoRaWAN, Mobile Communication, Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Signal Strength Prediction, Spreading Factor (SF), Transmit Power (TP).

DOI: 10.25673/121721.2

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