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RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning

Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set...

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Autores principales: Eltresy, Nermeen A., Dardeer, Osama M., Al-Habal, Awab, Elhariri, Esraa, Hassan, Ali H., Khattab, Ahmed, Elsheakh, Dalia N., Taie, Shereen A., Mostafa, Hassan, Elsadek, Hala A., Abdallah, Esmat A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832353/
https://www.ncbi.nlm.nih.gov/pubmed/31618881
http://dx.doi.org/10.3390/s19204465
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author Eltresy, Nermeen A.
Dardeer, Osama M.
Al-Habal, Awab
Elhariri, Esraa
Hassan, Ali H.
Khattab, Ahmed
Elsheakh, Dalia N.
Taie, Shereen A.
Mostafa, Hassan
Elsadek, Hala A.
Abdallah, Esmat A.
author_facet Eltresy, Nermeen A.
Dardeer, Osama M.
Al-Habal, Awab
Elhariri, Esraa
Hassan, Ali H.
Khattab, Ahmed
Elsheakh, Dalia N.
Taie, Shereen A.
Mostafa, Hassan
Elsadek, Hala A.
Abdallah, Esmat A.
author_sort Eltresy, Nermeen A.
collection PubMed
description Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.
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spelling pubmed-68323532019-11-21 RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning Eltresy, Nermeen A. Dardeer, Osama M. Al-Habal, Awab Elhariri, Esraa Hassan, Ali H. Khattab, Ahmed Elsheakh, Dalia N. Taie, Shereen A. Mostafa, Hassan Elsadek, Hala A. Abdallah, Esmat A. Sensors (Basel) Article Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data. MDPI 2019-10-15 /pmc/articles/PMC6832353/ /pubmed/31618881 http://dx.doi.org/10.3390/s19204465 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Eltresy, Nermeen A.
Dardeer, Osama M.
Al-Habal, Awab
Elhariri, Esraa
Hassan, Ali H.
Khattab, Ahmed
Elsheakh, Dalia N.
Taie, Shereen A.
Mostafa, Hassan
Elsadek, Hala A.
Abdallah, Esmat A.
RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title_full RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title_fullStr RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title_full_unstemmed RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title_short RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning
title_sort rf energy harvesting iot system for museum ambience control with deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832353/
https://www.ncbi.nlm.nih.gov/pubmed/31618881
http://dx.doi.org/10.3390/s19204465
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