Employing Digital Twin to Forest Fire Management Systems

Real-time data-driven simulations have become possible with Digital Twins. The increasing average temperature of the world is fueling the risk of forest fires, s o i tis important to have forest fire detection systems. Internet o f T hings (IoT) networks are widely used for early detection of fires. Owing to the sophisticated and varying structure of these networks, optimization of the system is highly complicated. Thanks to the real-time accurate simulation capability of the Digital Twins, it is possible to test different strategies to find t he o ptimal one. However, the communication requirements of Digital Twins can be troublesome for Internet of Things networks. This work proposes a network state forecaster to facilitate the integration of Digital Twins into IoT Networks. First, a forest fire detection IoT network was simulated based on a real forest. The forecaster and the digital twin were trained with the packets from the simulation environment. The estimations of the integrated forecaster and the digital twin were evaluated with the ground-truth results obtained from the simulation. The initial results are encouraging, especially for small-scale networks.