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：2022年飓风伊恩期间采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，并识别了该领域存在的差距。自2005年飓风卡特里娜以来，无人机在灾害响应中的应用日益增多，然而，将无人机采集的数据及时传递给整个事件指挥系统中的决策者始终存在问题。即便美国等国家已在无线基础设施、快速部署节点和商用卫星解决方案方面投入巨资，此类延迟问题依然持续存在。飓风伊恩为通信需求与能力之间的不匹配提供了案例研究。在响应行动的前四天，九个无人机团队在佛罗里达州FL-UAS1的指挥下执行了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且运行正常的应急行动中心，以便相关机构获取数据。基于这种不匹配的分析，本研究构建了灾害中"无人机数据到决策"的工作流模型，并通过五个因素量化了整个工作流中的无线网络通信需求。其中四个因素——可用性、带宽、突发性和空间分布——此前已在2017年飓风哈维和2018年飓风迈克尔的分析中得到识别。本研究将上传速率作为第五个属性纳入分析。该分析有望改进无人机设计与边缘计算方案，并为无线通信研发提供参考。