Paper Reading AI Learner

Wireless Network Demands of Data Products from Small Uncrewed Aerial Systems at Hurricane Ian

2023-03-22 22:38:34
Thomas Manzini, Robin Murphy, David Merrick, Justin Adams


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.

Abstract (translated)

在2022年的飓风伊万(Ian)收集的数据量化了无人机在网络安全基础设施上的要求,并发现了实地中的缺口。自飓风卡特里娜( Katrina)以来,无人机越来越常用于灾难响应,然而,及时将无人机数据发送给适当的决策制定者在整个事件指挥过程中一直是一个问题。这些延迟即使在像美国等国家对无线基础设施、可以快速部署节点和商业卫星解决方案的大规模投资仍然存在。Ian飓风用作通信需求和能力不匹配的案例分析。在响应的前四天中,九架无人机团队执行了34次任务,由佛罗里达州FL-UAS1号州指导,生成了636GB的数据。团队可以访问六个不同的无线通信网络,但不得不采取物理方式将数据转移到最近的完整紧急行动中心,以便将数据提供给相关机构。该分析有助于构建无人机在灾害中数据-决策工作流程模型,并量化在整个工作流程中的无线网络安全需求。四个因素-可用性、带宽、爆发性和空间分布-从飓风哈瓦那( Harvey)和迈克尔(Michael)的分析中已确定。该工作还增加了上传速率作为第五个属性。该分析预计可以改善无人机设计和边缘计算方案,并通知无线通信研究和发展。



3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot