Unmanned aerial vehicles (UAVs) are envisioned to provide diverse services from the air. The service quality may rely on the wireless performance which is affected by the UAV's position. In this paper, we focus on the UAV placement problem in the Internet of Vehicles, where the UAV is deployed to monitor the road traffic and sends the monitored videos to vehicles. The studied problem is formulated as video resolution maximization by optimizing over the UAV's position. Moreover, we take into account the maximal transmission delay and impose a probabilistic constraint. To solve the formulated problem, we first leverage the techniques in extreme value theory (EVT) and Gaussian process regression (GPR) to characterize the influence of the UAV's position on the delay performance. Based on this characterization, we subsequently propose a proactive resolution selection and UAV placement approach, which adaptively places the UAV according to the geographic distribution of vehicles. Numerical results justify the joint usage of EVT and GPR for maximal delay characterization. Through investigating the maximal transmission delay, the proposed approach nearly achieves the optimal performance when vehicles are evenly distributed, and reduces 10% and 19% of the 999-th 1000-quantile over two baselines when vehicles are biased distributed.

翻译：无人机（UAV）被构想为从空中提供多样化服务的平台。其服务质量可能受制于无线性能，而后者又受无人机位置的影响。本文聚焦于车联网中的无人机部署问题，其中无人机用于监测道路交通，并将监测视频传输至车辆。我们将所研究的问题形式化为通过优化无人机位置来最大化视频分辨率。此外，我们还考虑了最大传输时延，并施加了概率约束。为求解该问题，我们首先利用极值理论（EVT）和高斯过程回归（GPR）技术来刻画无人机位置对时延性能的影响。基于此刻画，我们随后提出了一种主动式分辨率选择与无人机部署方法，该方法根据车辆的地理分布自适应地部署无人机。数值结果证明了EVT与GPR联合用于最大时延刻画的有效性。通过研究最大传输时延，所提方法在车辆均匀分布时接近最优性能；而在车辆偏态分布时，其较两种基线方法将999/1000分位数分别降低了10%和19%。