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 M.; Taie, Shereen A.; Mostafa, Hassan; Elsadek, Hala; Abdallah, Esmat A.

doi:10.3390/s19204465

Cited by 26 publications

(17 citation statements)

References 45 publications

(93 reference statements)

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“…The authors in [200] and [201] use electric power data and heat flow information in a building to predict the energy requirements in the future so as to better manage energy consumption. Ambience control of a museum has been performed in [202] where the authors use deep learning algorithms to predict the CO 2 and humidity levels for the care of exhibits. Comfort aware energy management has been performed in [203] where the authors use a CNN to regulate thermal comfort in a building using various physical quantities.…”

Section: Smart Industrymentioning

confidence: 99%

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IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Industrymentioning

confidence: 99%

“…SAE [201] Regression-Energy predictions for buildings Homogeneous (Heat flow data in buildings) RNN (GRU, LSTM) [202] Regression-Prediction of environmental variables (CO 2 , Humidity etc. )…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…Based on a review of the past literature in this area [61 -63], we found that RF waves energy harvesting is the best solution in many scenarios when it comes to low-energy IoT devices. The works in [64,65] and [66] highlight the potential of using RF Energy Harvesters (RFEHs) to power IoT devices for environmental and healthcare monitoring. Other approaches for harvesting energy from RF signals include opportunistic charging from nearby smartphones [67], or the sharing of energy between different wireless systems close to each other [68].…”

Section: Rf Energy Harvestingmentioning

confidence: 99%

Energy Harvesting Techniques for Internet of Things (IoT)

et al. 2021

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The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

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“…In general, small electronic devices with minimum power consumption requirements (e.g., WSN's node), can be powered from ambient RF Energy. The applicability of the proposed improved version of storage-based RF energy harvesting system could be the power supply of a WSN's node for Internet of Things (IoT) applications [27]. A WSN' node can be self-powered from ambient RF energy by the storage-based harvester when the "wake-up" mode is active (e.g., for wireless data transmission).…”

Section: Evaluation Of Measurements and Applicabilitymentioning

confidence: 99%

Use Ultra-Wideband Discone Rectenna for Broadband RF Energy Harvesting Applications

et al. 2020

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In this study, a broadband Radio Frequency (RF) energy harvester implementation is presented. The system uses a broadband discone antenna, which can operate efficiently in a broad frequency spectrum, including LTE, DCS 1800 and UMTS 2100 cellular frequency bands. The system is able to harvest energy from various electromagnetic field sources, thus has the potential to efficiently charge a storage energy element in a short time. The prototype broadband RF energy harvester was tested in the laboratory and also in a typical urban environment.Technologies 2020, 8, 21 2 of 13 energy from more ambient wireless sources that are distributed in a wide frequency range. Multiband rectennas normally have high conversion efficiency contrary to a narrow operating bandwidth, and can be ideal for energy harvesting from wireless communications systems, such as DCS-1800, LTE, UMTS [5].The authors in [6] proposed a complimentary split ring resonator (CSRR) metamaterial multiband antenna with a hybrid junction ring rectifier as a multiband rectenna that was able to utilize wireless transmissions from four communication bands such as GSM, UMTS, LTE, WiFi, using separately rectifier branches. Subrectifiers have been matched by the hybrid ring junction at each operating band. With this setup, the rectenna has a peak conversion efficiency up to 67% at 1.8 GHz. Additionally, the proposed multiband rectenna was tested in an urban environment.Broadband rectennas usually have lower conversion efficiency in contrary to a wide operating bandwidth. The design of this type of rectenna is a difficult process due to the complex matching network and the nonlinearity of the rectifiers' diodes [5]. Tapered line matching networks could be a solution for this problem as proposed by the authors of [7] with impedance transformation from the source (50 Ω) to the load (140 Ω). The proposed broadband rectenna exhibits an improved conversion efficiency above 30% across the entire frequency range from 1.2 to 5 GHz, using an Archimedean Spiral Antenna and a wideband-tapered microstrip balance-unbalanced transmission line (BALUN).The authors in [8] proposed a broadband rectenna using a double-sided printed monopole antenna consists of circular patch with a truncated ground plane. This type of rectenna, has an operating bandwidth range from 0.9 to 5.5 GHz and offers maximum efficiency (62.5%) with a resistive terminal load of 5 kΩ at 1.8 GHz, as shown from measurements results.The work in [9] introduced a compact broadband rectenna using a coplanar waveguide (CPW) rectangular monopole antenna with an inverted-L open circuit stub as the matching network and a rectifying circuit based on a voltage doubler with a minimum footprint size (58 × 55 mm 2 ). The proposed rectenna operates from 1.8 to 3.5 GHz and 5.4 to 6 GHz, and is able to utilize transmissions from wireless applications such as GSM 1800, UMTS 2100, WLAN 802.11 a, b and g systems and also WiMAX signals efficiently up to 28% for power conversion.Although the above systems can effectively operate in seve...

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

Cited by 26 publications

References 45 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Energy Harvesting Techniques for Internet of Things (IoT)

Use Ultra-Wideband Discone Rectenna for Broadband RF Energy Harvesting Applications

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