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ARM mbed support for transient computing in energy harvesting IoT systems

ARM mbed support for transient computing in energy harvesting IoT systems
ARM mbed support for transient computing in energy harvesting IoT systems
Energy harvesters offer the possibility for embedded IoT computing systems to operate without batteries. However, their output power is usually unpredictable and highly variable. To mitigate the effect of this variability, systems incorporate large energy buffers, increasing their size, mass and cost. The emerging class of transient computing systems differs from this approach, operating directly from the energy harvesting source and minimizing or removing additional energy storage. Existing transient approaches are largely designed for specific applications and architectures. Hence, they suffer from not being broadly applicable across multiple embedded IoT platforms. To address this challenge, transient approaches need to be integrated within a general IoT programming framework such as ARM’s mbed IoT Device Platform. This support is offered through libraries and application programming interfaces(APIs) which enable transient computing to be implemented as a service on top of IoT application protocols.
Lazarescu, Bogdan
1d1ff08b-25c3-4369-8960-07dc05e42c4f
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Lazarescu, Bogdan
1d1ff08b-25c3-4369-8960-07dc05e42c4f
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020

Lazarescu, Bogdan, Balsamo, Domenico and Merrett, Geoff (2017) ARM mbed support for transient computing in energy harvesting IoT systems. ARM Research Summit 2017, Cambridge, United Kingdom. 11 - 13 Sep 2017. 1 pp.

Record type: Conference or Workshop Item (Poster)

Abstract

Energy harvesters offer the possibility for embedded IoT computing systems to operate without batteries. However, their output power is usually unpredictable and highly variable. To mitigate the effect of this variability, systems incorporate large energy buffers, increasing their size, mass and cost. The emerging class of transient computing systems differs from this approach, operating directly from the energy harvesting source and minimizing or removing additional energy storage. Existing transient approaches are largely designed for specific applications and architectures. Hence, they suffer from not being broadly applicable across multiple embedded IoT platforms. To address this challenge, transient approaches need to be integrated within a general IoT programming framework such as ARM’s mbed IoT Device Platform. This support is offered through libraries and application programming interfaces(APIs) which enable transient computing to be implemented as a service on top of IoT application protocols.

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More information

Published date: 14 September 2017
Venue - Dates: ARM Research Summit 2017, Cambridge, United Kingdom, 2017-09-11 - 2017-09-13

Identifiers

Local EPrints ID: 414076
URI: https://eprints.soton.ac.uk/id/eprint/414076
PURE UUID: a9313f90-5c6a-4555-ac4f-a364994a2c5d
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 14 Sep 2017 16:31
Last modified: 06 Jun 2018 12:42

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