Predictive Battery Life Modeling for LoRaWAN Sensors Using Real-World Deployment Data

This study presents a comprehensive analysis of LoRaWAN-based IoT communication in an agricultural monitoring context. The research is grounded in long-term experimental data collected from four environmental sensors deployed in the Czech Republic, focusing on temperature and humidity measurements. Beyond the environmental data, the study emphasizes the technical performance of the deployed devices, particularly in terms of signal quality and energy efficiency. We analyzed 14 key transmission parameters, including RSSI, SNR, Time on Air, and gateway reception metrics, to evaluate the communication reliability and network coverage. A significant contribution of this work is the development of a data-driven model for estimating battery life based on real-world usage patterns and spreading factor distributions. This model enables predictive maintenance planning and supports energy-efficient network design. The study builds on previous research and contributes to the growing body of literature on holistic performance evaluation in IoT systems.