In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limitation in UAVs and autonomous object recognition (for abnormal behavior detection) deep learning performance in infrastructure/towers. To overcome the power limitation of UAVs, this paper proposes a novel aerial scheduling algorithm between multi-UAVs and multi-towers where the towers conduct wireless power transfer toward UAVs. In addition, to take care of the high-performance learning model training in towers, we also propose a data delivery scheme which makes UAVs deliver the training data to the towers fairly to prevent problems due to data imbalance (e.g., huge computation overhead caused by larger data delivery or overfitting from less data delivery). Therefore, this paper proposes a novel workload-aware scheduling algorithm between multi-towers and multi-UAVs for joint power-charging from towers to their associated UAVs and training data delivery from UAVs to their associated towers. To compute the workload-aware optimal scheduling decisions in each unit time, our solution approach for the given scheduling problem is designed based on Markov decision process (MDP) to deal with (i) time-varying low-complexity computation and (ii) pseudo-polynomial optimality. As shown in performance evaluation results, our proposed algorithm ensures (i) sufficient times for resource exchanges between towers and UAVs, (ii) the most even and uniform data collection during the processes compared to the other algorithms, and (iii) the performance of all towers convergence to optimal levels.

翻译：在现代网络研究中，基础设施辅助的无人自主飞行器（UAV）因其在三维空间中的自由机动与协调能力，被积极应用于基于实时学习的监控和空中数据投递任务。在该系统模型中，必须考虑无人机的功率限制以及基础设施/塔架上用于自主目标识别（异常行为检测）的深度学习性能。为解决无人机的功率限制问题，本文提出了一种多无人机与多塔架之间的新型空中调度算法，其中塔架向无人机进行无线电力传输。此外，为兼顾塔架中高性能学习模型的训练需求，我们还提出了一种数据投递方案，使无人机能够公平地向塔架传送训练数据，以避免因数据不平衡导致的问题（例如大量数据投递带来的巨大计算开销或少量数据投递导致的过拟合）。因此，本文提出了一种面向多塔架与多无人机的创新工作负载感知调度算法，实现从塔架到其关联无人机的联合电力充电与从无人机到其关联塔架的训练数据投递。为了在单位时间内计算出工作负载感知的最优调度决策，我们基于马尔可夫决策过程（MDP）设计了针对该调度问题的求解方法，以处理以下两个关键问题：（i）时变的低复杂度计算，以及（ii）伪多项式最优性。性能评估结果表明，我们提出的算法能够（i）确保塔架与无人机之间进行充分的资源交换时间，（ii）在数据收集过程中相比其他算法实现最均衡的数据采集，以及（iii）所有塔架的性能收敛至最优水平。