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

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 pharmace...

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Autores principales: Ghosh, Shreya, Mukherjee, Anwesha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162382/
https://www.ncbi.nlm.nih.gov/pubmed/35677629
http://dx.doi.org/10.1007/s11334-022-00458-2
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author Ghosh, Shreya
Mukherjee, Anwesha
author_facet Ghosh, Shreya
Mukherjee, Anwesha
author_sort Ghosh, Shreya
collection PubMed
description 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 [Formula: see text] 24% and reducing delay by [Formula: see text] 55% compared to cloud-only system.
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spelling pubmed-91623822022-06-04 STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic Ghosh, Shreya Mukherjee, Anwesha Innov Syst Softw Eng S.I. : Multifaceted Intelligent Computing Systems (MICS) 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 [Formula: see text] 24% and reducing delay by [Formula: see text] 55% compared to cloud-only system. Springer London 2022-06-02 /pmc/articles/PMC9162382/ /pubmed/35677629 http://dx.doi.org/10.1007/s11334-022-00458-2 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle S.I. : Multifaceted Intelligent Computing Systems (MICS)
Ghosh, Shreya
Mukherjee, Anwesha
STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title_full STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title_fullStr STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title_full_unstemmed STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title_short STROVE: spatial data infrastructure enabled cloud–fog–edge computing framework for combating COVID-19 pandemic
title_sort strove: spatial data infrastructure enabled cloud–fog–edge computing framework for combating covid-19 pandemic
topic S.I. : Multifaceted Intelligent Computing Systems (MICS)
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9162382/
https://www.ncbi.nlm.nih.gov/pubmed/35677629
http://dx.doi.org/10.1007/s11334-022-00458-2
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