STROVE: spatial data infrastructure enabled cloud-fog-edge computing framework for combating COVID-19 pandemic

被引:11
作者
Ghosh, Shreya [1 ,2 ]
Mukherjee, Anwesha [3 ]
机构
[1] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, India
[2] Penn State Univ, Coll Informat Sci & Technol, State Coll, PA 16802 USA
[3] Mahishadal Raj Coll, Dept Comp Sci, Mahishadal, West Bengal, India
关键词
Health service provisioning; Health data analysis; Cloud-Fog-Edge framework; COVID-19; MOBILE; MODEL;
D O I
10.1007/s11334-022-00458-2
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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 similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}24% and reducing delay by similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}55% compared to cloud-only system.
引用
收藏
页码:727 / 743
页数:17
相关论文
共 41 条
  • [1] Ahmed I., 2021, SUSTAIN CITIES SOC, V65, P102571, DOI [10.1016/j.scs.2020.102571, DOI 10.1016/J.SCS.2020.102571]
  • [2] Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring
    Alhussein, Musaed
    Muhammad, Ghulam
    Hossain, M. Shamim
    Amin, Syed Umar
    [J]. MOBILE NETWORKS & APPLICATIONS, 2018, 23 (06) : 1624 - 1635
  • [3] Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study
    Badr, Hamada S.
    Du, Hongru
    Marshall, Maximilian
    Dong, Ensheng
    Squire, Marietta M.
    Gardner, Lauren M.
    [J]. LANCET INFECTIOUS DISEASES, 2020, 20 (11) : 1247 - 1254
  • [4] An architecture for emergency event prediction using LSTM recurrent neural networks
    Cortez, Bitzel
    Carrera, Berny
    Kim, Young-Jin
    Jung, Jae-Yoon
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2018, 97 : 315 - 324
  • [5] Das J, SOFTWARE PRACT EXPER
  • [6] Internet of Spatial Things: A New Reference Model With Insight Analysis
    Eldrandaly, Khalid A.
    Abdel-Basset, Mohamed
    Shawky, Laila A.
    [J]. IEEE ACCESS, 2019, 7 : 19653 - 19669
  • [7] Ghosh Shreya, 2022, Proceedings of International Conference on Advanced Computing Applications: ICACA 2021. Advances in Intelligent Systems and Computing (1406), P247, DOI 10.1007/978-981-16-5207-3_22
  • [8] Ghosh S., 2019, IEEE T NETW SCI ENG
  • [9] CLAWER: Context-aware Cloud-Fog based Workflow Management Framework for Health Emergency Services
    Ghosh, Shreya
    Das, Jaydeep
    Ghosh, Soumya K.
    Buyya, Rajkumar
    [J]. 2020 20TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2020), 2020, : 810 - 817
  • [10] MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories
    Ghosh, Shreya
    Ghosh, Soumya K.
    Buyya, Rajkumar
    [J]. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2020, 164