AoI-aware Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Networks

In this paper, we employ multiple unmanned aerial vehicles (UAVs) to assist sensing data transmission from the ground users (GUs) to the remote base station (BS). Each UAV can first cache the sensing data and then report the cached data to the BS. We consider a time-slotted protocol to coordinate the UAVs' data collection and reporting. Only one UAV is allowed to forward its data to the BS in each time slot. We formulate a multi-stage stochastic optimization problem to minimize the longterm age-of-information (AoI) by jointly optimizing the UAVs' trajectories and scheduling strategies. To simplify this problem, we model the dynamics of the UAVs' data buffer and AoI statuses by queueing systems, and propose a novel AoI-aware Adaptation scheme. This scheme allows us to transform the multistage dynamic programming problem into per-slot scheduling and trajectory planning sub-problems by using the Lyapunov optimization framework. Then, in each time slot, we can update the UAVs' scheduling and flying strategies in an iterative manner according to the instant buffer and AoI statuses. Simulation results show that the proposed scheme outperforms baseline schemes in terms of reducing AoI while stabilizing and balancing the UAVs' data queues.