Sensors (Basel)
Faculty of Computing, Engineering and Sciences, University of South Wales, Pontypridd CF37 1DL, UK.
Published: February 2025
This paper investigates the feasibility of harvesting Radio Frequency (RF) energy from the Wi-Fi frequency band to power low-power Internet-of-Things (IoT) devices. With the increasing prevalence of IoT applications and wireless sensor networks (WSNs), there is a critical need for sustainable energy sources that can extend the operational lifespan of these devices, particularly in remote locations, where access to reliable power supplies is limited. The paper describes the design, simulation, and fabrication of a dual-band antenna capable of operating at 2.4 GHz and 5 GHz, the frequencies used by Wi-Fi. The simulation and experimental results show that the proposed design is efficient based on the reflection coefficient. Using a high-frequency simulator, we developed two C-shaped and an F-shaped microstrip antenna design, optimized for impedance matching and efficient RF-DC conversion.The captured RF energy is converted into usable electrical power that can be directly utilized by low-power IoT devices or stored in batteries for later use. The paper introduces an efficient design for dual-band antennas to maximize the reception of Wi-Fi signals. It also explains the construction of an impedance-matching network to reduce signal reflection and improve power transfer efficiency. The results indicate that the proposed antennas can effectively harvest Wi-Fi energy, providing a sustainable power source for IoT devices. The practical implementation of this system offers a promising solution to the energy supply challenges faced by remote and low-power IoT applications, paving the way for more efficient and longer-lasting wireless sensor networks.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11859205 | PMC |
http://dx.doi.org/10.3390/s25041055 | DOI Listing |
J Phys Condens Matter
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Zhengzhou University, kexue dadao No.100,zhengzhou, Zhengzhou, 450001, CHINA.
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Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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College of Physical Education, Luoyang Normal University, Henan, China.
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Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China.
The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes.
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March 2025
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection System (IDS) is specifically designed for IoT environments.
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