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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Autor*in:	Ayoub Alsarhan [verfasserIn] Islam Almalkawi [verfasserIn] Yousef Kilani [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning

Übergeordnetes Werk:	In: International Journal of Interactive Mobile Technologies - International Association of Online Engineering (IAOE), 2018, 15(2021), 22, Seite 111-126
Übergeordnetes Werk:	volume:15 ; year:2021 ; number:22 ; pages:111-126

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3991/ijim.v15i22.22623

Katalog-ID:	DOAJ04830610X

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spelling	10.3991/ijim.v15i22.22623 doi (DE-627)DOAJ04830610X (DE-599)DOAJ8345c842c5f24ca08bedd275625c111a DE-627 ger DE-627 rakwb eng TK5101-6720 Ayoub Alsarhan verfasserin aut A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <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< contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication Islam Almalkawi verfasserin aut Yousef Kilani verfasserin aut In International Journal of Interactive Mobile Technologies International Association of Online Engineering (IAOE), 2018 15(2021), 22, Seite 111-126 (DE-627)558042449 (DE-600)2406982-6 18657923 nnns volume:15 year:2021 number:22 pages:111-126 https://doi.org/10.3991/ijim.v15i22.22623 kostenfrei https://doaj.org/article/8345c842c5f24ca08bedd275625c111a kostenfrei https://online-journals.org/index.php/i-jim/article/view/22623 kostenfrei https://doaj.org/toc/1865-7923 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2086 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2021 22 111-126
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allfieldsGer	10.3991/ijim.v15i22.22623 doi (DE-627)DOAJ04830610X (DE-599)DOAJ8345c842c5f24ca08bedd275625c111a DE-627 ger DE-627 rakwb eng TK5101-6720 Ayoub Alsarhan verfasserin aut A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <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< contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication Islam Almalkawi verfasserin aut Yousef Kilani verfasserin aut In International Journal of Interactive Mobile Technologies International Association of Online Engineering (IAOE), 2018 15(2021), 22, Seite 111-126 (DE-627)558042449 (DE-600)2406982-6 18657923 nnns volume:15 year:2021 number:22 pages:111-126 https://doi.org/10.3991/ijim.v15i22.22623 kostenfrei https://doaj.org/article/8345c842c5f24ca08bedd275625c111a kostenfrei https://online-journals.org/index.php/i-jim/article/view/22623 kostenfrei https://doaj.org/toc/1865-7923 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2086 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2021 22 111-126
allfieldsSound	10.3991/ijim.v15i22.22623 doi (DE-627)DOAJ04830610X (DE-599)DOAJ8345c842c5f24ca08bedd275625c111a DE-627 ger DE-627 rakwb eng TK5101-6720 Ayoub Alsarhan verfasserin aut A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <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< contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning Telecommunication Islam Almalkawi verfasserin aut Yousef Kilani verfasserin aut In International Journal of Interactive Mobile Technologies International Association of Online Engineering (IAOE), 2018 15(2021), 22, Seite 111-126 (DE-627)558042449 (DE-600)2406982-6 18657923 nnns volume:15 year:2021 number:22 pages:111-126 https://doi.org/10.3991/ijim.v15i22.22623 kostenfrei https://doaj.org/article/8345c842c5f24ca08bedd275625c111a kostenfrei https://online-journals.org/index.php/i-jim/article/view/22623 kostenfrei https://doaj.org/toc/1865-7923 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2086 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2021 22 111-126
language	English
source	In International Journal of Interactive Mobile Technologies 15(2021), 22, Seite 111-126 volume:15 year:2021 number:22 pages:111-126
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callnumber-first	T - Technology
author	Ayoub Alsarhan
spellingShingle	Ayoub Alsarhan misc TK5101-6720 misc contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning misc Telecommunication A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
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topic_title	TK5101-6720 A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning
topic	misc TK5101-6720 misc contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning misc Telecommunication
topic_unstemmed	misc TK5101-6720 misc contact tracing, uavs, covid-19, wireless monitoring system, wireless mesh networks, reinforcement learning misc Telecommunication
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title	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
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title_auth	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
abstract	<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<
abstractGer	<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<
abstract_unstemmed	<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<
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title_short	A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
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