On the impact of mobility on battery-less RF energy harvesting system performance

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Hdl Handle:
http://hdl.handle.net/10547/622933
Title:
On the impact of mobility on battery-less RF energy harvesting system performance
Authors:
Munir, Bilal; Dyo, Vladimir ( 0000-0003-4747-0870 )
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.
Citation:
Munir B, Dyo V (2018) 'On the impact of mobility on battery-less RF energy harvesting system performance', Sensors, 18 (11), pp.3597-.
Publisher:
MDPI
Journal:
Sensors
Issue Date:
23-Oct-2018
URI:
http://hdl.handle.net/10547/622933
DOI:
10.3390/s18113597
Additional Links:
https://www.mdpi.com/1424-8220/18/11/3597
Type:
Article
Language:
en
ISSN:
1424-8220
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorMunir, Bilalen
dc.contributor.authorDyo, Vladimiren
dc.date.accessioned2018-10-25T10:40:25Z-
dc.date.available2018-10-25T10:40:25Z-
dc.date.issued2018-10-23-
dc.identifier.citationMunir B, Dyo V (2018) 'On the impact of mobility on battery-less RF energy harvesting system performance', Sensors, 18 (11), pp.3597-.en
dc.identifier.issn1424-8220-
dc.identifier.doi10.3390/s18113597-
dc.identifier.urihttp://hdl.handle.net/10547/622933-
dc.description.abstractThe 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.en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.urlhttps://www.mdpi.com/1424-8220/18/11/3597en
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectwireless sensor networksen
dc.subjectmobile sensorsen
dc.subjectsupercapacitorsen
dc.subjectenergy harvestingen
dc.subjectJ910 Energy Technologiesen
dc.titleOn the impact of mobility on battery-less RF energy harvesting system performanceen
dc.typeArticleen
dc.identifier.journalSensorsen
dc.date.updated2018-10-25T10:31:35Z-
dc.description.noteopen access article-
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