Kumar Adarsh, Sharma Kriti, Singh Harvinder, Naugriya Sagar Gupta, Gill Sukhpal Singh, Buyya Rajkumar
Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Future Gener Comput Syst. 2021 Feb;115:1-19. doi: 10.1016/j.future.2020.08.046. Epub 2020 Sep 3.
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push-pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 min approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted.
冠状病毒病(COVID-19)是一种由新发现的冠状病毒引起的传染病。它与流感病毒相似,其传播速度和严重程度令人担忧,导致全球范围内的疫情持续蔓延。在八个月内(截至2020年8月),全球有2400万人感染,超过82.4万人死亡。无人机或无人驾驶飞行器(UAV)在应对COVID-19疫情方面非常有帮助。这项工作研究了基于无人机的系统、COVID-19疫情情况,并提出了一种在不同场景下使用实时和基于模拟的场景来应对疫情的架构。所提出的架构使用可穿戴传感器,通过推拉式数据获取机制在人体区域网络(BAN)中记录观测数据。所提出的架构在偏远和高度拥挤的疫情地区很有用,在这些地区,无线或互联网连接是一个主要问题,或者COVID-19传播的可能性很高。它在规定的时间内收集和存储大量数据,并有助于在需要时采取适当行动。在基于无人机的实时医疗系统用于COVID-19操作的实施中,观察到通过空中路线可以在短时间内(大约10分钟内覆盖2公里)大面积地进行消毒、热图像采集和患者识别。在模拟中,观察到相同的统计数据,并且增加了成功应用于室内和室外医疗操作的防撞策略。此外,还确定了开放挑战并突出了有前景的研究方向。