A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Radwa Ahmed Osman, Sherine Nagy Saleh, Yasmine N. M. Saleh and Mazen Nabil Elagamy
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Radwa Ahmed Osman: Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Sherine Nagy Saleh: Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Yasmine N. M. Saleh: Computer Science Department, College of Computing and Information Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt
Mazen Nabil Elagamy: Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria 1029, Egypt

IJERPH, 2021, vol. 18, issue 24, 1-20

Abstract: Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

Keywords: Internet of Things; Radio Frequency Identifier; deep learning; pandemic; COVID-19; Lagrange optimization; reliability; efficiency (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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