A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing


A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

Publication DOI: https://doi.org/10.1111/exsy.12704
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Funding Information: This work is partly supported by VC Research (VCR 0000072) for Prof. Chang.
Additional Information: © 2021 The Authors. Expert Systems published by John Wiley & Sons Ltd This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Funding: This work is partly supported by VC Research (VCR 0000072) for Prof. Chang.
Uncontrolled Keywords: cloud computing,corona,COVID,deep neural networks,fog computing,pandemic,Control and Systems Engineering,Theoretical Computer Science,Computational Theory and Mathematics,Artificial Intelligence
Publication ISSN: 1468-0394
Last Modified: 22 Jul 2024 07:28
Date Deposited: 31 May 2022 12:14
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://onlinel ... 1111/exsy.12704 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-03-01
Published Online Date: 2021-05-06
Accepted Date: 2021-03-30
Authors: Singh, Ajay
Jindal, Vaibhav
Sandhu, Rajinder
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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