飓风伊恩期间小型无人航空系统数据产品的无线网络需求
Wireless Network Demands of Data Products from Small Uncrewed Aerial Systems at Hurricane Ian
AI总结：
本研究基于飓风伊恩的无人机数据，量化了灾害响应中无线网络需求，提出五因素模型，为无人机设计和通信发展提供指导。
摘要：
在飓风伊恩（2022年）期间收集的数据量化了小型无人航空系统（UAS）或无人机对网络通信基础设施的需求，并识别了现场存在的差距。自飓风卡特里娜（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递给事件指挥系统中的适当决策者一直存在问题。即使在美国等国家已在无线基础设施、快速部署节点以及商业卫星解决方案方面进行了大量投资，这些延迟仍然存在。飓风伊恩作为通信需求与能力不匹配的案例研究。在响应行动的前四天，九个无人机团队在佛罗里达州FL-UAS1的指导下执行了34次任务，生成了636GB的数据。这些团队可使用六种不同的无线通信网络，但不得不诉诸于将数据物理传输到最近的完好应急行动中心，以便使相关机构能够获取数据。对不匹配的分析贡献了一个灾害中无人机数据到决策工作流程的模型，并通过五个因素量化了整个工作流程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——先前已通过对飓风哈维（2017年）和迈克尔（2018年）的分析确定。这项工作增加了上传速率作为第五个属性。该分析预计将改进无人机设计和边缘计算方案，并为无线通信研究与发展提供信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
