Ghosh Shreya, Mukherjee Anwesha
Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
College of Information Sciences and Technology, The Pennsylvania State University, State College, USA.
Innov Syst Softw Eng. 2022 Jun 2:1-17. doi: 10.1007/s11334-022-00458-2.
The outbreak of 2019 novel coronavirus (COVID-19) has triggered unprecedented challenges and put the whole world in a parlous condition. The impacts of COVID-19 is a matter of grave concern in terms of fatality rate, socio-economical condition, health infrastructure. It is obvious that only pharmaceutical solutions (vaccine) cannot eradicate this pandemic completely, and effective strategies regarding lockdown measures, restricted mobility, emergency services to users-in brief data-driven decision system is of utmost importance. This necessitates an efficient data analytics framework, data infrastructure to store, manage pandemic related information, and distributed computing platform to support such data-driven operations. In the past few decades, Internet of Things-based devices and applications have emerged significantly in various sectors including healthcare and time-critical applications. To be specific, health-sensors help to accumulate health-related parameters at different time-instances of a day, the movement sensors keep track of mobility traces of the user, and helps to assist them in varied conditions. The smartphones are equipped with several such sensors and the ability of low-cost connected sensors to cover large areas makes it the most useful component to combat pandemics such as COVID-19. However, analysing and managing the huge amount of data generated by these sensors is a big challenge. In this paper we have proposed a unified framework which has three major components: (i) Spatial Data Infrastructure to manage, store, analyse and share spatio-temporal information with stakeholders efficiently, (ii) Cloud-Fog-Edge-based hierarchical architecture to support preliminary diagnosis, monitoring patients' mobility, health parameters and activities while they are in quarantine or home-based treatment, and (iii) Assisting users in varied emergency situation leveraging efficient data-driven techniques at low-latency and energy consumption. The mobility data analytics along with SDI is required to interpret the movement dynamics of the region and correlate with COVID-19 hotspots. Further, Cloud-Fog-Edge-based system architecture is required to provision healthcare services efficiently and in timely manner. The proposed framework yields encouraging results in taking decisions based on the COVID-19 context and assisting users effectively by enhancing accuracy of detecting suspected infected people by 24% and reducing delay by 55% compared to cloud-only system.
2019年新型冠状病毒（COVID-19）的爆发引发了前所未有的挑战，使整个世界陷入危险境地。COVID-19在死亡率、社会经济状况、卫生基础设施方面的影响令人深感担忧。显然，仅靠药物解决方案（疫苗）无法完全根除这一疫情，而关于封锁措施、限制流动、为用户提供应急服务等有效的策略——简而言之，数据驱动的决策系统至关重要。这就需要一个高效的数据分析框架、用于存储和管理与疫情相关信息的数据基础设施，以及支持此类数据驱动操作的分布式计算平台。在过去几十年中，基于物联网的设备和应用在包括医疗保健和对时间要求严格的应用在内的各个领域显著涌现。具体而言，健康传感器有助于在一天中的不同时间点累积与健康相关的参数，运动传感器跟踪用户的移动轨迹，并在各种情况下为用户提供帮助。智能手机配备了多种此类传感器，低成本连接传感器覆盖大面积的能力使其成为抗击COVID-19等疫情最有用的组件。然而，分析和管理这些传感器产生的大量数据是一项巨大挑战。在本文中，我们提出了一个统一框架，它有三个主要组件：（i）空间数据基础设施，用于高效地与利益相关者管理、存储、分析和共享时空信息；（ii）基于云-雾-边缘的分层架构，用于在患者隔离或居家治疗时支持初步诊断、监测患者的移动性、健康参数和活动；（iii）利用高效的数据驱动技术在低延迟和低能耗情况下在各种紧急情况下协助用户。移动性数据分析与空间数据基础设施一起用于解读该地区的移动动态并与COVID-19热点相关联。此外，需要基于云-雾-边缘的系统架构来高效、及时地提供医疗服务。与仅基于云的系统相比，所提出的框架在基于COVID-19背景进行决策以及通过将检测疑似感染者的准确率提高24%和将延迟降低55%来有效协助用户方面产生了令人鼓舞的结果。
