Maintaining freshness of data collection in Internet-of-Things (IoT) networks
has attracted increasing attention. By taking into account age-of-information
(AoI), we investigate the trajectory planning problem of an unmanned aerial
vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization
problem is formulated to minimize the total AoI of the collected data by the
UAV from the ground IoT network. Since the total AoI of the IoT network depends
on the flight time of the UAV and the data collection time at hovering points,
we jointly optimize the selection of hovering points and the visiting order to
these points. We exploit the state-of-the-art transformer and the weighted A*,
which is a path search algorithm, to design a machine learning algorithm to
solve the formulated problem. The whole UAV-IoT system is fed into the encoder
network of the proposed algorithm, and the algorithm's decoder network outputs
the visiting order to ground clusters. Then, the weighted A* is used to find
the hovering point for each cluster in the ground IoT network. Simulation
results show that the trained model by the proposed algorithm has a good
generalization ability to generate solutions for IoT networks with different
numbers of ground clusters, without the need to retrain the model. Furthermore,
results show that our proposed algorithm can find better UAV trajectories with
the minimum total AoI when compared to other algorithms.

通过考虑信息时代，本文研究了一种用于辅助集群式物联网网络的无人机（UAV）的轨迹规划问题，构建了一个优化问题，通过无人机从地面物联网网络中收集的数据的总信息时代的最小化来解决问题。利用状态良好的转换器和加权 A * 算法，设计了一种机器学习算法来求解所制定的问题，并证明该算法在 IoT 网络中具有不同数量的地面集群时具有很好的泛化能力，不需要重新训练模型。此外，结果表明，与其他算法相比，我们的算法可以找到具有最小总信息时代的更好的无人机轨迹。