AI-Based 3D UAV Coverage Deployment for Internet of Vehicles in the Complex Mountain Environment

Unmanned aerial vehicle (UAV) has been widely applied to collect data because of its rapid deployment, high flexibility and strong adaptability characteristics. However, due to the data capacity constraints of UAVs, how to effectively deploy UAVs in complex mountain roads to achieve vehicle coverage has become an important research problem. Thus, we propose an optimization method for the coverage deployment of UAVs in the internet of vehicles (IoV) based on complex mountain environments. The method combines the K-means clustering algorithm and the genetic algorithm (GA) with the objective of maximizing the total number of covered vehicles while considering the vehicle data rate requirements and the capacity constraints of UAVs. Firstly, the K-means clustering algorithm was used to cluster the vehicles, and the cluster center was used as the initial UAV position to generate the initial population. Then, the GA is introduced to find the optimal solution that could maximize the number of vehicles covered by the UAVs while meeting the communication requirements. The experimental results show that the proposed method can effectively optimize the coverage deployment scheme of IoV and UAVs in complex mountain environments. This method not only improves the vehicle coverage rate, but also makes full use of the data capacity of the UAV to ensure the effectiveness and stability of data transmission.