Smart IoT system empowered by customized energy-aware wireless sensors integrated in graphene-based tissues to improve workers thermal comfort

Catarinucci, Luca; Colella, Riccardo; Corcione, Carola Esposito; Ingrosso, Chiara; Greco, Antonio; Ferrari, Francesca; Curri, Maria Lucia; Leo, Carlo Giacomo; Mandriota, Giacomo; Molinaro, Vincenzo; Montanaro, Teodoro; Patrono, Luigi; Sabina, Saverio; Sergi, Ilaria; Shumba, Angela Tafadzwa; Spedicato, Luigi; Striani, Raffaella

doi:10.1016/j.jclepro.2022.132132

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…The advent of graphene, a two-dimensional material with extraordinary properties, has catalyzed a transformative shift within the Internet of Things (IoT), offering promising solutions to challenges such as energy consumption, data processing, and device miniaturization [ 1 ]. Graphene’s unique composition-a single layer of carbon atoms arranged in a hexagonal lattice-endows it with unparalleled electrical, mechanical, and optical properties, rendering it an optimal choice for diverse IoT components [ 2 ]. Its remarkable electrical conductivity, transparency, and flexibility empower it with the ability to facilitate ultrasensitive chemisensing capabilities crucial for environmental monitoring, healthcare diagnostics, and security protocols.…”

Section: Introductionmentioning

confidence: 99%

Graphene-based nanotechnology in the Internet of Things: a mini review

Ganguly,

Sengupta

2024

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Graphene, a 2D nanomaterial, has garnered significant attention in recent years due to its exceptional properties, offering immense potential for revolutionizing various technological applications. In the context of the Internet of Things (IoT), which demands seamless connectivity and efficient data processing, graphene's unique attributes have positioned it as a promising candidate to prevail over challenges and optimize IoT systems. This review paper aims to provide a brief sketch of the diverse applications of graphene in IoT, highlighting its contributions to sensors, communication systems, and energy storage devices. Additionally, it discusses potential challenges and prospects for the integration of graphene in the rapidly evolving IoT landscape.

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Section: Introductionmentioning

confidence: 99%

Graphene-based nanotechnology in the Internet of Things: a mini review

Ganguly,

Sengupta

2024

Discover Nano

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“…Research [10] designed a deep neural network for drowsiness detection based on electroencephalography (EEG) in various states of consciousness, namely "awake," "sleep," and "drowsy." The shortcomings of this research include non-optimal window lengths and the need for additional classification methods to verify the performance of the proposed neural network.…”

Section: Introductionmentioning

confidence: 99%

IoT-based Heart Signal Processing System for Driver Drowsiness Detection

Yunidar,

Melinda,

Khairani

et al. 2023

Green Intell. Syst. Appl.

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Traffic accidents often result in loss of life and significant economic losses. Indonesia's high number of traffic accidents indicates the need for effective solutions to overcome this problem. Developing a drowsiness detection device is one effort that can be made to reduce accidents caused by drowsy drivers. The data obtained in this study used driver heart rate data. The drowsiness detection tool was developed using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor. Testing was carried out on 25 subjects under two conditions: 'Drowsy' and 'Normal.' The driver's level of drowsiness is determined based on the heart rate measured by the detection device. The Blynk application is used as a visual interface to provide notifications via smartphone if the driver is drowsy. The accuracy of the drowsiness detection tool was compared with the results obtained from the Pulse Oximeter. This research shows that the drowsiness detection tool using the Wemos D1 Pro Esp8266 microcontroller and MAX30102 sensor has an accuracy of around 98% when compared with the pulse oximeter. The Blynk application successfully sends notifications precisely when the driver is drowsy. This study highlights the potential of drowsiness detection devices to improve traffic safety and reduce accidents caused by drowsy drivers.

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