Fire Twin: Digital Twin-assisted UAV Surveillance for Efficient Forest Fire Management

Forest fires are among the most devastating catastrophes in ecology. Massive, long-term, unstoppable forest fires cause destructive damage to both living beings and the natural environment. To fight and prevent this environmental crisis, environmental sensing applications, especially aerial intervention and monitoring with unmanned aerial vehicles (UAVs), have become widespread. However, monitoring and prevention have evolved into problems due to weather conditions, mountainous terrain, fire size, and battery conditions of UAVs. Real-time analysis of the fire zone is critical to prevent fire spread because of extreme conditions. Therefore, digital twin (DT) integrated UAV monitoring systems can be utilized to monitor forest fires. To address all the mentioned issues, we propose a DT-assisted centralized forest fire monitoring system employing an elliptical fire model, temperature, wind, and fuel moisture content (FMC) data. Secondly, we provide trajectory and coverage management to enhance the monitoring of fire zones thanks to the RL-based Q-Learning method. Additionally, we employ a software-defined networking(SDN) approach between UAVs and the ground controller. According to the results, our proposed method outperformed the traditional simulation model by 50%.