A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is...

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Autores principales:	Ayoub Alsarhan, Islam Almalkawi, Yousef Kilani
Formato:	article
Lenguaje:	EN
Publicado:	International Association of Online Engineering (IAOE) 2021
Materias:	contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/8345c842c5f24ca08bedd275625c111a
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id	oai:doaj.org-article:8345c842c5f24ca08bedd275625c111a
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spelling	oai:doaj.org-article:8345c842c5f24ca08bedd275625c111a2021-12-02T19:46:58ZA New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network1865-792310.3991/ijim.v15i22.22623https://doaj.org/article/8345c842c5f24ca08bedd275625c111a2021-11-01T00:00:00Zhttps://online-journals.org/index.php/i-jim/article/view/22623https://doaj.org/toc/1865-7923<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>Ayoub AlsarhanIslam AlmalkawiYousef KilaniInternational Association of Online Engineering (IAOE)articlecontact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learningTelecommunicationTK5101-6720ENInternational Journal of Interactive Mobile Technologies, Vol 15, Iss 22, Pp 111-126 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication TK5101-6720
spellingShingle	contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication TK5101-6720 Ayoub Alsarhan Islam Almalkawi Yousef Kilani A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
description	<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>
format	article
author	Ayoub Alsarhan Islam Almalkawi Yousef Kilani
author_facet	Ayoub Alsarhan Islam Almalkawi Yousef Kilani
author_sort	Ayoub Alsarhan
title	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
title_short	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
title_full	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
title_fullStr	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
title_full_unstemmed	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
title_sort	new covid-19 tracing approach using machine learning and drones enabled wireless network
publisher	International Association of Online Engineering (IAOE)
publishDate	2021
url	https://doaj.org/article/8345c842c5f24ca08bedd275625c111a
work_keys_str_mv	AT ayoubalsarhan anewcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork AT islamalmalkawi anewcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork AT yousefkilani anewcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork AT ayoubalsarhan newcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork AT islamalmalkawi newcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork AT yousefkilani newcovid19tracingapproachusingmachinelearninganddronesenabledwirelessnetwork
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A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

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