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Title Iomt-Assisted Medical Vehicle Routing Based On Uav-Borne Human Crowd Sensing And Deep Learning In Smart Cities
ID_Doc 33566
Authors Rezaee K.; Khosravi M.R.; Attar H.; Menon V.G.; Khan M.A.; Issa H.; Qi L.
Year 2023
Published IEEE Internet of Things Journal, 10, 21
DOI http://dx.doi.org/10.1109/JIOT.2023.3284056
Abstract An emergency medical vehicle can save the patient's life if it arrives at his location as quickly as possible. Unmanned aerial vehicles (UAVs) offer wide visibility and mobility, making them a viable choice for smart cities and intelligent transportation systems (ITSs) as edge devices for the Internet of Things (IoT). Based on population behavior and overcrowding, video surveillance through the Internet of multimedia things (IoMT) and public safety in smart cities can help determine the most efficient routes for emergency medical vehicles. This study investigates UAV overcrowding and abnormal population activity patterns, which affect the flow of emergency medical vehicles and traffic flow. Moreover, the purpose of this article is to analyze received video frames from UAVs in order to identify the most efficient route for emergency medical vehicles in smart cities to transfer patients in the event of abnormalities or overcrowding. In order to detect overcrowding on the streets, a hybrid Cascade-ResNet is utilized, which detects congestion based on many data points. Based on our proposed approach, we achieve a 2.5% improvement over similar methods because it is effective, flexible, and accurate. UAV video frames can be used to communicate with emergency response vehicles, to monitor traffic congestion, and to monitor other aspects of smart city life. © 2023 IEEE.
Author Keywords Crowd behavior monitoring; deep transfer learning (DTL); emergency medical vehicles; human - vehicle interaction; Internet of Things (IoT); unmanned aerial vehicles (UAVs)


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