Harvesting energy from ambient radio signals: A load of hot air?

Allen, Ben; Ajmal, Tahmina; Dyo, Vladimir; Jazani, David

doi:10.1109/lapc.2012.6402944

Cited by 13 publications

(14 citation statements)

References 8 publications

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“…A thorough assessment of the power density in a variety of scenarios and distances was performed in [6], but was limited to (global system for mobile communications) GSM900/1800 and WiFi bands. Other work has focused on the medium wave range (909 kHz) to benefit from the large transmit powers and low path loss at those frequencies [7,8] or extremely large transmit powers up to 1 MW over a short distance (a few kilometres) to scavenge tens of microwatts (μW) [9].…”

Section: Introductionmentioning

confidence: 99%

Ambient RF energy harvesting trial in domestic settings

Mimis

Gibbins

Dumanlı

et al. 2015

IET Microwaves, Antennas & Propagation

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This study investigates the feasibility of ambient radio frequency (RF) energy harvesting for powering low-power electronic devices, in domestic environments. An RF spectrum survey was carried out in a variety of locations around the city of Bristol, UK between 500 MHz and 6 GHz. Locations are limited to indoor residential environments, and as a comparison an office. The measurement setup consists of an omnidirectional, broadband, discone antenna and a handheld spectrum analyser. On the basis of the measured power consumption of a number of low-power electronic devices and the analysed results, the required harvesting time to power each device is calculated. The results show that in order to harvest enough energy to power-up a small calculator (2 μW) for 1 s, an antenna array covering 1.7-2.5 GHz with effective area of 1 m 2 requires on average about 10 min of harvesting.

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Section: Introductionmentioning

confidence: 99%

Ambient RF energy harvesting trial in domestic settings

Mimis

Gibbins

Dumanlı

et al. 2015

IET Microwaves, Antennas & Propagation

View full text Add to dashboard Cite

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“…This technique is commonly used in passive RFID systems. The source of the power can either be dedicated RF waves generated for wireless charging (e.g., the Qi wireless charging standard) [31], or ambient RF signals that are transmitted for wireless data transfer (e.g., WiFi or TV signals) [14]. Energy harvesting from ambient WiFi signals has been demonstrated [30], although the amount of harvested power that can be harvested is often minuscule.…”

Section: Harvesting Ambient Energymentioning

confidence: 99%

Powering the internet of things

Jayakumar

Lee

et al. 2014

Proceedings of the 2014 International Symposium on Low Power Electronics and Design

185

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Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societalscale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. Further, many of them will have stringent size constraints. With no cord for power and limited space for a battery, powering these devices (to achieve several months to possibly years of unattended operation) becomes a daunting challenge. This paper highlights some promising directions for addressing this challenge, focusing on three main building blocks: (a) the design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, (b) the development of intelligent system-level power management techniques, and (c) the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing -in some cases, even eliminating -their dependence on batteries. We discuss these building blocks in detail and illustrate case-studies of systems that use them judiciously, including the QUBE wireless embedded platform, which exploits the characteristics of emerging non-volatile memory technologies to seamlessly and efficiently enable long-running computations in systems that experience frequent power loss (i.e., intermittently powered systems).

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“…A complete analysis of the power density in different scenarios and distances are measured for wireless local area network and Global System for Mobile Communications (GSM) network. The harvested energy is used for powering minute sensors on medium distance in Visser et al In Allen et al, compact antenna is used to harvest energy from ambient environment. Hence, these energy are good enough to power wireless application at a distance of 20 km from the transmitter.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis on ambient RF energy harvesting for low‐power wireless applications

Inbaraj¹,

Kailasam²,

Sankararajan³

2018

Int J Communication

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Renewable energy sources from the earth constitute another option apart from the available ones for wellspring of energy for economizing on cost of power supply. The energy obtained from ambient sources is called energy harvesting. Energy-harvesting low-power systems have acquired a remarkable consideration as a viable hotspot for expanding both energy efficiency and spectral efficiency. Radio frequency (RF) energy harvesting from ambient source is a promising technique for fulfilling the irreplaceable power prerequisites for powering the low-power devices. Hence, it requires the development of an antenna for harvesting RF energy. In this paper, a coplanar waveguide (CPW) antenna has been designed and fabricated using FR4 lossy substrate. This CPW antenna covers frequency bands from the most important RF patrons (GSM 900, GSM 1800, 3G, and Wi-Fi) within the frequency range from (0.58 to 3 GHz) with a percentage fractional bandwidth of 116% with the center frequency of 1.65 GHz. The fabricated antenna then has been experimentally validated at SSN College of Engineering campus. The effects on the RF power density level for CPW antenna are examined by statistical approach known as Taguchi method. The L9 and L8 orthogonal arrays and analysis of variance are implemented to analyze the execution qualities. The CPW antenna control factors are distance, time, and number of receivers. Then, statistical test (P) are used to determine the significant factors on RF power density. KEYWORDSANOVA, CPW antenna, RF energy harvesting, spectral survey, Taguchi orthogonal array | INTRODUCTIONThe enormous growth in communication networks has triggered persistent expansion of the need for energy supply, leading to a battery exhaustion issue for wireless communication devices. 1 Research work has been conducted recently to increase energy from ambient sources, which is an alternate option for maintaining a sustainable energy supply instead of rechargeable or replaceable batteries. 2 Energy harvesting has been proposed for accomplishing this and ensuring unlimited energy supply to electronic systems and saving energy consumption. 3,4 Hence, energy harvesting grasps energy from the ambient environment and stores them for future use. There are different types of energy harvesting such as thermal, solar, and radio frequency (RF). Energy harvesting from electromagnetic (EM) radiation has huge consideration because of wireless communications networks. 5 Electromagnetic radiations from the far-field applications have emerged in the form of radio frequency and can be collected by aerial, which converts the radio waves into electric power using rectifier circuits. 6 This paper envisages the usage of harvesting arising from the availability of frequency bands such as Wi-Fi routers, cell phones, and laptops. Cognitive radio can be powered from RF ambient sources, which improve the spectrum efficiency and energy efficiency. 6 Use of the RF signal and the distance between an RF energy source can help harvesting RF energy. First, the purpose of the ...

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Harvesting energy from ambient radio signals: A load of hot air?

Cited by 13 publications

References 8 publications

Ambient RF energy harvesting trial in domestic settings

Ambient RF energy harvesting trial in domestic settings

Powering the internet of things

Statistical analysis on ambient RF energy harvesting for low‐power wireless applications

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