Comparison of Methods for Designing and Deploying an IoT-Based Forest Fire Warning System: A Case Study in An Giang, Vietnam

This paper presents the design and deployment of an IoT-based early warning system for forest fire detection, optimized for remote and infrastructure-scarce environments. The system integrates multiple environmental sensors including air temperature, air humidity, soil moisture, rainfall, wind speed, and wind direction interfaced with ESP32 microcontrollers for local data acquisition. Each sensor node operates autonomously using solar power, enhancing sustainability and long-term field operation. Data is transmitted using LoRaWAN for long-range, low-power communication, with 3G fallback for redundancy in critical zones. A centralized server collects and processes the data in real time, applying a rule-based threshold algorithm combined with temporal trend analysis to detect fire-prone conditions. A web-based dashboard provides monitoring and visualization, while alert notifications are dispatched via SMS and email when risk levels exceed defined thresholds. Field trials conducted in the An Giang region demonstrated reliable communication over distances exceeding 5 km, stable solar-powered operation, and alert latency of under 10 seconds. The results confirm the system’s feasibility and effectiveness in providing early warnings for wildfire prevention in rural forest areas.