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 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.

Automated summary

This study explores the impact of Digital Twins in Unmanned Aerial Vehicles on providing medical resources during COVID-19 prevention and control, introducing deep learning algorithms and proposing a UAV DTs information forecasting model. The model shows better performance in terms of transmission delays, energy consumption, task completion time, and resource utilization rate compared to other state-of-art models as end-users and task proportion increase.