2.4GHz WLAN RF energy harvester for passive indoor sensor nodes

AlNeyadi, Fatima; Alkaabi, Maitha; Alketbi, Salama; Hajraf, Shamsa; Ramzan, Rashad

doi:10.1109/smelec.2014.6920900

Cited by 19 publications

(12 citation statements)

References 5 publications

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“…Instead of relying on dedicated wireless charger, wireless charging systems based on ambient energy harvesting have also been developed. Literature has demonstrated the development of self-recharging sensors platform harvesting environmental RF signals from TV broadcast [149]- [152], amplitude modulated (AM) radio broadcast [153], Global System for Mobile Communications (GSM) bands (900/1800) [154], [155], WiFi routers [156], [157], cellular base stations [158] and satellite [159]- [161].…”

Section: B Applications Of Wireless Chargingmentioning

confidence: 99%

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wang

Niyato

et al. 2016

IEEE Commun. Surv. Tutorials

844

395

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Abstract-Wireless charging is a technology of transmitting power through an air gap to electrical devices for the purpose of energy replenishment. The recent progress in wireless charging techniques and development of commercial products have provided a promising alternative way to address the energy bottleneck of conventionally portable battery-powered devices. However, the incorporation of wireless charging into the existing wireless communication systems also brings along a series of challenging issues with regard to implementation, scheduling, and power management. In this article, we present a comprehensive overview of wireless charging techniques, the developments in technical standards, and their recent advances in network applications. In particular, with regard to network applications, we review the static charger scheduling strategies, mobile charger dispatch strategies and wireless charger deployment strategies. Additionally, we discuss open issues and challenges in implementing wireless charging technologies. Finally, we envision some practical future network applications of wireless charging.

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Section: B Applications Of Wireless Chargingmentioning

confidence: 99%

Wireless Charging Technologies: Fundamentals, Standards, and Network Applications

Wang

Niyato

et al. 2016

IEEE Commun. Surv. Tutorials

844

395

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“…RF energy harvesting is a relatively new technique, which harvests power from RF sources and can power devices in the environments where light, vibration and temperature difference are not available. The RF energy harvesting systems can scavenge ambient RF energy, such as Wi-Fi, or TV, or radio broadcast signal [ 21 ], or obtain energy radiated by dedicated RF transmitters. In [ 22 ], a battery-less RF sensor has been designed to monitor the quality of packed vegetables by observing their temperature and humidity.…”

Section: Related Workmentioning

confidence: 99%

On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance

Munir

Dyo

2018

Sensors

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The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.

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“…2, GSM 900/1800 bands have high RF power densities. While other RF sources like AM radio stations [12,14] and UHF RFID [48,49] are promising sources they are still very limited. …”

Section: Rf Energy Sourcesmentioning

confidence: 99%