Hierarchical Clustering-Based Trajectory Planning for Multiple UAVs in UAV-Assisted Edge Computing Systems

In this paper, we propose a hierarchical clustering-based trajectory planning method for multiple Unmanned Aerial Vehicles (UAVs) in UAV-assisted edge computing systems. Edge computing systems have been developed to efficiently process data collected from client nodes at edge servers deployed near the network edge. Recently, increasing attention has been focused on UAV-assisted edge computing, where lightweight edge servers are mounted on UAVs to actively accommodate requests from client nodes, i.e., the UAVs approach the client nodes to perform data collection and processing tasks. In the UAV-assisted edge computing systems, trajectory optimization of UAVs with respect to data processing time and the number of deployed UAVs is a key challenge. To address this, the proposed method applies hierarchical clustering to select the optimal waypoints for the UAVs, utilizing minimum bounding circles as the clustering criterion. It then solves the Multiple Traveling Salesman Problem to determine the trajectories of UAVs, while reducing the number of deployed UAVs. Numerical experiments demonstrate the effectiveness of the proposed trajectory planning method.