An Efficient Data Collection Method by UAV Based on Data Recovery

As a crucial component of crowdsensing networks, unmanned aerial vehicles (UAVs) typically serve as data collectors. Utilizing UAVs for data acquisition in designated areas holds significant potential for military and industrial applications. To effectively reduce the time and cost of UAV-based data collection, this invention proposes a UAV data collection and completion method based on the importance of data. First, reinforcement learning is employed to identify the importance of data regions using historical data, enabling the recovery of data for the entire region by collecting data from only these critical areas, thereby reducing costs. Subsequently, an algorithm for optimizing UAV trajectories is introduced to minimize the flight distance while ensuring coverage of all important regions. Finally, data completion methods are utilized to obtain data for the entire region based on the data collected by the UAVs. The proposed method in this invention aims to achieve reduced collection costs while ensuring data accuracy.