Autonomous Long-Range Climate Monitoring: Synergizing LoRaTM Connectivity and Edge Deep Learning

Abstract

This study presents the design and implementation of an autonomous, IoT-enabled weather station engineered for real-time climate monitoring and high-precision forecasting. Addressing the need for localized meteorological tools in agriculture, urban planning, and environmental risk management, the system integrates diverse hardware and software technologies into a cohesive, portable unit. Data acquisition of key variables—including temperature, humidity, atmospheric pressure, and wind dynamics—is managed by ArduinoTM-based embedded systems, while a Raspberry® Pi facilitates localized edge computing. Central to its predictive capability is a multivariate Long Short-Term Memory (LSTM) deep neural network, trained to identify non-linear temporal patterns within climatic datasets. Experimental validation confirmed high operational reliability in data transmission and storage. The LSTM model achieved exceptional predictive accuracy, maintaining a Mean Squared Error (MSE) below 5%, thereby demonstrating its capacity to anticipate complex environmental trends. By synthesizing LoRaTM connectivity with edge-deployed Deep Learning, this research provides a low-uncertainty solution for hyper-local climate prediction. This architecture represents a significant advancement for data-driven decision-making, offering a scalable and efficient tool for sustainable resource management and smart city initiatives in climate-sensitive applications.