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

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Summary
This summary is machine-generated.

This study introduces a unified framework using IoT sensors and a Cloud-Fog-Edge architecture for effective COVID-19 management. The data-driven system enhances detection accuracy and reduces response delays, crucial for pandemic control.

Keywords:
COVID-19Cloud–Fog–Edge frameworkHealth data analysisHealth service provisioning

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Area of Science:

  • Public Health Informatics
  • Data Science and Analytics
  • Internet of Things (IoT) in Healthcare

Background:

  • The COVID-19 pandemic presents significant global challenges, impacting health, economy, and social structures.
  • Effective pandemic management requires more than pharmaceutical solutions, necessitating data-driven strategies for mobility, emergency services, and decision-making.
  • The proliferation of Internet of Things (IoT) devices, particularly smartphones with various sensors, offers a powerful tool for collecting health and mobility data.

Purpose of the Study:

  • To propose a unified framework for efficient data analytics and infrastructure to manage pandemic-related information.
  • To leverage IoT sensors and a Cloud-Fog-Edge architecture for real-time monitoring and decision support during pandemics.
  • To enhance the accuracy of detecting suspected COVID-19 cases and reduce response times.

Main Methods:

  • Development of a unified framework comprising Spatial Data Infrastructure (SDI), Cloud-Fog-Edge computing architecture, and data-driven emergency assistance.
  • Utilizing IoT sensors (health and movement) for spatio-temporal data collection on user health parameters and mobility.
  • Implementing mobility data analytics correlated with COVID-19 hotspots and a hierarchical Cloud-Fog-Edge architecture for localized data processing and healthcare service delivery.

Main Results:

  • The proposed framework demonstrated encouraging results in COVID-19 decision-making and user assistance.
  • Enhanced accuracy in detecting suspected infected individuals by 24%.
  • Reduced decision-making and response delay by 55% compared to a cloud-only system.

Conclusions:

  • A unified framework integrating Spatial Data Infrastructure and Cloud-Fog-Edge architecture is effective for managing pandemic-related data and operations.
  • The data-driven approach significantly improves the efficiency and accuracy of public health interventions during pandemics like COVID-19.
  • The system provides a scalable and efficient solution for real-time health monitoring, mobility analysis, and emergency support.