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

  • ️Mon Jan 01 2018

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

Bilal Munir et al. Sensors (Basel). 2018.

Abstract

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.

Keywords: energy harvesting; mobile computing; supercapacitors; wireless sensor networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1

Block diagram of RF energy harvesting sensor node.

Figure 2
Figure 2

The timing diagram of capacitor charging. The sensor activates when the capacitor voltage reaches Vmax and keeps operating until the voltage drops to Vmin [6].

Figure 3
Figure 3

Smart switching circuit for supercapacitors.

Figure 4
Figure 4

The amount of usable harvested energy in the fixed capacitor configuration decreases with capacitor size due to self-discharge losses between intermittent charges. The hybrid configuration minimizes the losses by using a small capacitor in low energy areas.

Figure 5
Figure 5

The number of unique locations served by a sensor in the fixed capacitor configuration decreases with capacitor size as the node is less likely to boot on a larger capacitor. The hybrid configuration provides nearly full coverage.

Figure 6
Figure 6

The number of sensor activations near charging stations in the fixed capacitor configuration drops rapidly with capacitor size as the node is less likely to boot on a larger capacitor.

Figure 7
Figure 7

Measurement setup.

Figure 8
Figure 8

Oscilloscope screenshot for the energy profile of a simple application and the prototype. The oscilloscope was set at 20 mV/div with a timebase of 10 ms/div.

Figure 9
Figure 9

The charging time duration for 50-mF and 200-mF supercapacitors increases exponentially with charging distance. Practical applications that require longer charging range would thus require a very small capacitor.

Figure 10
Figure 10

Node lifetime.

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