Data collected at Hurricane Ian (2022) quantifies the demands that small uncrewed aerial systems (UAS), or drones, place on the network communication infrastructure and identifies gaps in the field. Drones have been increasingly used since Hurricane Katrina (2005) for disaster response, however getting the data from the drone to the appropriate decision makers throughout incident command in a timely fashion has been problematic. These delays have persisted even as countries such as the USA have made significant investments in wireless infrastructure, rapidly deployable nodes, and an increase in commercial satellite solutions. Hurricane Ian serves as a case study of the mismatch between communications needs and capabilities. In the first four days of the response, nine drone teams flew 34 missions under the direction of the State of Florida FL-UAS1, generating 636GB of data. The teams had access to six different wireless communications networks but had to resort to physically transferring data to the nearest intact emergency operations center in order to make the data available to the relevant agencies. The analysis of the mismatch contributes a model of the drone data-to-decision workflow in a disaster and quantifies wireless network communication requirements throughout the workflow in five factors. Four of the factors-availability, bandwidth, burstiness, and spatial distribution-were previously identified from analyses of Hurricanes Harvey (2017) and Michael (2018). This work adds upload rate as a fifth attribute. The analysis is expected to improve drone design and edge computing schemes as well as inform wireless communication research and development.

翻译：在伊恩飓风（2022年）期间收集的数据量化了小型无人航空系统（UAS，即无人机）对网络通信基础设施的需求，并识别了该领域的差距。自卡特里娜飓风（2005年）以来，无人机在灾害响应中的应用日益增多，然而将数据从无人机及时传输至事故指挥体系中的相应决策者一直存在问题。即使在美国等国家大力投资无线基础设施、快速部署节点并增加商用卫星解决方案的情况下，这些延迟依然持续存在。伊恩飓风作为案例研究，揭示了通信需求与能力之间的不匹配。在响应行动的前四天，佛罗里达州FL-UAS1指挥下的九个无人机团队执行了34次任务，生成了636GB数据。这些团队可接入六种不同的无线通信网络，但不得不采用物理传输方式将数据送达最近的完好应急行动中心，以便相关机构获取数据。通过对不匹配问题的分析，本文提出了一种灾难中“无人机数据到决策”工作流模型，并以五个因素量化了该工作流全过程的无线网络通信需求。其中四个因素——可用性、带宽、突发性与空间分布——此前已通过对哈维飓风（2017年）和迈克尔飓风（2018年）的分析得以识别。本研究新增上传速率作为第五个属性。该分析预计将改进无人机设计与边缘计算方案，并为无线通信研究与开发提供参考。