基于马尔可夫决策过程的多无人机监视网络负载感知调度
Workload-Aware Scheduling using Markov Decision Process for Infrastructure-Assisted Learning-Based Multi-UAV Surveillance Networks
摘要：
在现代网络研究中，基础设施辅助的无人驾驶自动驾驶车辆（UAV）被积极考虑用于在意外的3D自由移动和协调下进行基于学习的实时监视和空中数据传输。在该系统模型中，必须考虑无人机的功率限制和基础设施/塔中的自主对象识别（用于异常行为检测）深度学习性能。针对无人机功率受限的问题，提出了一种多无人机与多塔台之间的空中调度算法，由塔台向无人机进行无线功率传输。此外，为了照顾塔中的高性能学习模型训练，我们还提出了一种数据交付方案，该方案使无人机公平地将训练数据交付给塔，以防止由于数据不平衡而导致的问题（例如，由较大的数据传递或较少的数据传递的过拟合引起的巨大计算开销）。为此，提出了一种新的多塔多无人机负载感知调度算法，实现了多塔多无人机联合充电和多无人机联合训练数据传输。为了在单位时间内计算负载感知的最优调度决策，我们基于马尔可夫决策过程（MDP）设计了给定调度问题的求解方法，以处理（i）时变低复杂度计算和（ii）伪多项式最优性。如性能评估结果所示，我们提出的算法确保（i）塔和无人机之间的资源交换有足够的时间，（ii）与其他算法相比，在过程中最均匀和均匀的数据汇集，以及（iii）所有塔的性能收敛到最佳水平。
英文摘要：
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.
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