Collaborative Trajectory and Resource Optimization for AoI Minimization in Multi-UAV Networks

Wireless-powered Internet of Things networks face critical challenges due to the limited energy of sensor nodes and the difficulty of maintaining data freshness. To address these issues, this paper proposes a multi-uncrewed aerial vehicle (UAV)-assisted data collection framework to minimize the age of information (AoI). A hierarchical multiple decision strategy (HMDS) is developed to optimize UAV hovering time by dynamically selecting non-orthogonal multiple access or orthogonal frequency division multiple access for intra-cluster transmission according to energy constraints and channel conditions. In addition, a multi-collaborative trajectory optimization (MCTO) scheme is proposed to reduce UAV flight time through joint clustering, partitioning, and trajectory planning. Specifically, radius-adaptive K-means++ clustering, load-balanced spectral partitioning, and a hybrid K-nearest neighbors and elite ant strategy are employed. Simulation results show that the proposed HMDS and MCTO significantly reduce hovering and flight times, thereby effectively improving AoI performance.