Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in Epidemics

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-12, Vol.23 (12), p.25106-25114
Main Authors: Lv, Zhihan, Chen, Dongliang, Feng, Hailin, Zhu, Hu, Lv, Haibin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Autonomous aerial vehicles
Completion time
Coronaviruses
COVID-19
COVID-19 prevention and control
Data transmission
Deep learning
Digital twins
Disease prevention
Disease transmission
Energy consumption
epidemic
Epidemics
Forecasting
Frames per second
Inspection
Machine learning
Mathematical models
Medical research
medical resource
Medical services
Model accuracy
Pandemics
Predictive models
Resource utilization
Signal to noise ratio
Transmission error
Unmanned aerial vehicles
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Summary:The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.
ISSN:1524-9050
1558-0016
1558-0016
DOI:10.1109/TITS.2021.3113787