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

Ajay Singh, Vaibhav Jindal, Rajinder Sandhu, Victor Chang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
155 Downloads (Pure)


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.
Original languageEnglish
Article numbere12704
JournalExpert Systems
Issue number3
Early online date6 May 2021
Publication statusE-pub ahead of print - 6 May 2021

Bibliographical note

Funding Information:
This work is partly supported by VC Research (VCR 0000072) for Prof. Chang.

Publisher Copyright:
© 2021 The Authors. Expert Systems published by John Wiley & Sons Ltd.


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