Evaluating LoRaWAN Network Performance in Smart City Environments Using Machine Learning

Long Range Wide Area Network (LoRaWAN) has emerged as a key enabler for Internet of Things (IoT) applications in smart cities, offering long-range connectivity with low power consumption. This study evaluates the performance of a large-scale operational LoRaWAN network deployed in Pafos, Cyprus, consisting of over 30,000 smart water meters. Leveraging twelve months of real-world data, we apply regression-based machine learning models (Decision Trees and XGBoost) to predict Estimated Signal Power (ESP), enabling enhanced network reliability and efficiency. Unlike prior studies that primarily rely on simulations or small-scale testbeds, this work is the first to analyze large-scale real-world LoRaWAN data, uncovering key environmental factors influencing signal propagation. Furthermore, we introduce a novel software tool that integrates machine learning-driven ESP predictions, allowing for automated network optimization. Unlike conventional models, this tool dynamically refines signal power predictions based on empirical data, making it a valuable asset for smart city planning and large-scale IoT deployments. To the best of our knowledge, this study represents the first comprehensive analysis focused on the features influencing and predicting ESP. The results demonstrate that our approach effectively tailors propagation predictions to urban environments, achieving high predictive accuracy across diverse conditions. This work contributes to the optimization of large-scale IoT deployments, paving the way for scalable and reliable smart city applications.