A Climate-Aware Modeling Framework for Reliable IoT Connectivity: Environmental Effects on LoRaWAN Signal-to-Noise Ratio

The performance of large-scale Long-Range Wide Area Network (LoRaWAN) deployments is typically evaluated using conventional link metrics such as distance, transmit power, and spreading factor. However, the role of environmental variables has remained largely unexplored, despite their potential to significantly alter signal quality. In this paper, we introduce aclimate-aware modeling frameworkthat quantifies and predicts how temperature and humidity affect the signal-to-noise ratio (SNR) across heterogeneous IoT deployment scenarios. Using a city-scale dataset from more than 30,000 sensors, the proposed framework integrates statistical characterization (PDF/CDF and outage modeling) with a predictive learning component based on gradient-boosted decision trees, forming a unified modeling pipeline that links environmental variability, SNR distributional behavior, and predictive response. The framework is evaluated across three representative topologies—Free, Covered, and Underground—providing a structured understanding of environment-conditioned reliability in LoRaWAN networks. Our results show that temperature and humidity induce measurable shifts in SNR distributions, and that incorporating them into the predictive model yields consistent accuracy gains, improving test-set performance by up to Δr2= 0.445 over signal-only baselines (depending on topology and SF). Beyond prediction accuracy, the model’s outputs can be directly integrated into Adaptive Data Rate (ADR) management and network planning, demonstrating its applicability for system-level IoT optimization. These findings demonstrate that climate-aware modeling is not only relevant but essential for reliable Internet of Things (IoT) connectivity, offering new directions for adaptive LoRaWAN network planning and deployment in diverse urban environments.