Drones Combat COVID-19 Epidemic: Innovating and Monitoring Approach

Kumar, Adarsh; Elsersy, Mohamed; Darwsih, Ashraf; Hassanien, Aboul Ella

doi:10.1007/978-3-030-63307-3_11

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Regarding the increasing number of newly diagnosed and suspected COVID-19 cases worldwide, Kumar et al explored the possibilities and opportunities of UAVs to fight against the disease. They put forward some suggestions that could help healthcare sectors to suppress the virus from spreading [16].…”

Section: B Influences Of Uavs Data Collection On Covid-19 Prevention ...mentioning

confidence: 99%

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

Chen

Feng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

103

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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.

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Section: B Influences Of Uavs Data Collection On Covid-19 Prevention ...mentioning

confidence: 99%

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

Chen

Feng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

103

View full text Add to dashboard Cite

show abstract

“…The high-tech equipment and reliable internet connection needs for robots and drones utilization may hinder implementation in the region. For instance, advanced thermal cameras are accurate in temperature measurement, but high-end thermal cameras are prohibitively expensive thus unrealistic to be massively deployed [51] . Secondly, drones can only be used to service a limited area with a package of limited dimensions and weight.…”

Section: Results Analysismentioning

confidence: 99%

Unbundling the significance of cognitive robots and drones deployed to tackle COVID-19 pandemic: A rapid review to unpack emerging opportunities to improve healthcare in sub-Saharan Africa

2021

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“…Smart city drone applications and the associated privacy problems were covered by Nguyen et al [30]. Kumar et al [31] and Aydin et al [32] addressed drone networks' limits and future directions, and cybersecurity risks. In addition to problems and solutions, Aloqaily et al [2] noted security vulnerabilities associated with commercial and industrial drones.…”

Section: A Drone Security Threatsmentioning

confidence: 99%

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

Ashraf

Manickam

Zia

et al. 2023

Preprint

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The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ UNSW-NB15, CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD. The system attains precision values of 89.10% and 90.16%, accuracy rates of 91.00% and 91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.

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Drones Combat COVID-19 Epidemic: Innovating and Monitoring Approach

Cited by 11 publications

References 15 publications

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

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

Unbundling the significance of cognitive robots and drones deployed to tackle COVID-19 pandemic: A rapid review to unpack emerging opportunities to improve healthcare in sub-Saharan Africa

IoT Empowered Smart Cybersecurity Framework for Intrusion Detection in Internet of Drones

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