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Volume 19
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10.3390/s19204465
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Article

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

by
Nermeen A. Eltresy
1,
Osama M. Dardeer
1,
Awab Al-Habal
2,
Esraa Elhariri
3,
Ali H. Hassan
2,
Ahmed Khattab
2,*,
Dalia N. Elsheakh
1,
Shereen A. Taie
3,
Hassan Mostafa
2,4,
Hala A. Elsadek
1 and
Esmat A. Abdallah
1
1
Electronics Research Institute (ERI), Giza 12622, Egypt
2
Electronics and Electrical Communication Engineering Department, Cairo University, Giza 12613, Egypt
3
Computer Science Department, Faculty of Computers and Information, Fayoum University, Fayoum 63514, Egypt
4
University of Science and Technology, Nanotechnology and Nanoelectronics Program, Zewail City of Science and Technology, 6th of October City 12578, Egypt
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4465; https://doi.org/10.3390/s19204465
Submission received: 22 August 2019 / Revised: 10 October 2019 / Accepted: 11 October 2019 / Published: 15 October 2019
(This article belongs to the Section Internet of Things)
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Abstract

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.
Keywords: ambience monitoring; antenna array; deep learning; Internet of Things (IoT); rectenna; RF energy harvesting; time series prediction ambience monitoring; antenna array; deep learning; Internet of Things (IoT); rectenna; RF energy harvesting; time series prediction

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MDPI and ACS Style

Eltresy, N.A.; Dardeer, O.M.; Al-Habal, A.; Elhariri, E.; Hassan, A.H.; Khattab, A.; Elsheakh, D.N.; Taie, S.A.; Mostafa, H.; Elsadek, H.A.; et al. RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning. Sensors 2019, 19, 4465. https://doi.org/10.3390/s19204465

AMA Style

Eltresy NA, Dardeer OM, Al-Habal A, Elhariri E, Hassan AH, Khattab A, Elsheakh DN, Taie SA, Mostafa H, Elsadek HA, et al. RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning. Sensors. 2019; 19(20):4465. https://doi.org/10.3390/s19204465

Chicago/Turabian Style

Eltresy, Nermeen A., Osama M. Dardeer, Awab Al-Habal, Esraa Elhariri, Ali H. Hassan, Ahmed Khattab, Dalia N. Elsheakh, Shereen A. Taie, Hassan Mostafa, Hala A. Elsadek, and et al. 2019. "RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning" Sensors 19, no. 20: 4465. https://doi.org/10.3390/s19204465

APA Style

Eltresy, N. A., Dardeer, O. M., Al-Habal, A., Elhariri, E., Hassan, A. H., Khattab, A., Elsheakh, D. N., Taie, S. A., Mostafa, H., Elsadek, H. A., & Abdallah, E. A. (2019). RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning. Sensors, 19(20), 4465. https://doi.org/10.3390/s19204465

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