AoI-Aware Scheduling Optimization for WPT-Enabled Space–Air–Ground Integrated Sensor Networks

The collection of timely and sustainable sensing data presents significant challenges for Internet of Things (IoT) systems deployed across remote and expansive geographical areas. Focusing on the space-air-ground integrated networks (SAGIN), this work investigates the age of information (AoI) minimization problem of sensing data, where the wireless power transfer (WPT) is employed to address energy constraints of sensing nodes (SNs) in persistent monitoring scenarios. To address the key challenges of hierarchical multi-hop architecture, temporal coupling between wireless energy supply and data uploading, dynamic topology of low-earth-orbit (LEO) satellite network, and time-varying channel states, we propose an AoI optimization scheme that employs alternating long-short time slots to decouple the system AoI optimization into two independent sub-problems: air-ground and air-space link AoI optimization. The maximum AoI utility scheduling (MAUS) algorithm is designed to minimize the AoI value of the air-ground link by jointly optimizing the network clustering, the unmanned aerial vehicle (UAV) flight trajectory, and the allocation strategy for SNs’ charging time and data uploading time. The matching game-based network resource allocation (MGNRA) algorithm with proportional fairness-based bandwidth allocation is designed to minimize the UAV data queue backlog, thereby reducing the system long-term average AoI. Simulation experiments demonstrate that our proposed scheme has significant advantages in reducing the long-term average AoI value and maintaining operational stability compared with benchmark schemes.