Abstract: The application of Unmanned Aerial Vehicles (UAVs) to forest fire monitoring can improve the efficiency of firefighting and rescue. However, in the complex environment of forest fires, the deployment of UAVs faces problems such as high energy consumption, low offloading efficiency, and dynamic changes in the environment. Therefore, an air-ground-assisted edge computing framework is investigated, in which the UAV collects fire scene data at the fire scene and provides edge computing services, and the command center provides edge computing services with high computational power. In order to provide efficient computing services, a UAV location deployment scheme based on multi-agent reinforcement learning is designed, which first determines the area that needs UAV to provide computing services based on the fire spreading speed and distance, and then designs an autonomous deployment strategy based on multi-agent reinforcement learning that minimizes the system cost to obtain the optimal location of the UAV in the designated task area. The final simulation results demonstrate that the proposed scheme can effectively reduce the total cost of UAV deployment.

Key words: unmanned aerial vehicle, edge computing, location deployment, multi-agent reinforcement learning