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Exploring ARM mbed support for transient computing in energy harvesting IoT systems

Exploring ARM mbed support for transient computing in energy harvesting IoT systems
Exploring 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. Different transient computing approaches have been proposed which enable computation to be sustained despite power outages. However, existing approaches are largely designed for specific applications and architectures, and hence 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. In this paper, we explore how stateof-art transient computing approaches can be integrated into mbed, increasing ease-to-use and deployment across different platforms. This support is offered through libraries and application programming interfaces (APIs) provided by the ARM mbed OS, which enable transient computing to be implemented as a service on top of IoT application protocols. We demonstrate the ability for a transient approach to operate effectively on mbed, by practically implementing it on a low-power NXP microcontroller (MCU) with Flash memory, operating from only 1 mF additional
capacitance.
transient computing, Embedded Systems, mbed
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Elboreini, Ali
cb8c5ee1-6c0c-4307-8d09-38337dddcfda
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Elboreini, Ali
cb8c5ee1-6c0c-4307-8d09-38337dddcfda
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020

Balsamo, Domenico, Elboreini, Ali, Al-Hashimi, Bashir and Merrett, Geoffrey (2017) Exploring ARM mbed support for transient computing in energy harvesting IoT systems. 7th IEEE International Workshop on Advances in Sensors and Interfaces, 2017. 15 - 16 Jun 2017. (doi:10.1109/IWASI.2017.7974230).

Record type: Conference or Workshop Item (Paper)

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. Different transient computing approaches have been proposed which enable computation to be sustained despite power outages. However, existing approaches are largely designed for specific applications and architectures, and hence 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. In this paper, we explore how stateof-art transient computing approaches can be integrated into mbed, increasing ease-to-use and deployment across different platforms. This support is offered through libraries and application programming interfaces (APIs) provided by the ARM mbed OS, which enable transient computing to be implemented as a service on top of IoT application protocols. We demonstrate the ability for a transient approach to operate effectively on mbed, by practically implementing it on a low-power NXP microcontroller (MCU) with Flash memory, operating from only 1 mF additional
capacitance.

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More information

Accepted/In Press date: 15 June 2017
e-pub ahead of print date: 13 July 2017
Venue - Dates: 7th IEEE International Workshop on Advances in Sensors and Interfaces, 2017, 2017-06-15 - 2017-06-16
Keywords: transient computing, Embedded Systems, mbed
Organisations: Faculty of Physical Sciences and Engineering, Electronic & Software Systems

Identifiers

Local EPrints ID: 410422
URI: https://eprints.soton.ac.uk/id/eprint/410422
PURE UUID: e0ba6196-607f-447d-8f86-f2e85e876745
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 08 Jun 2017 16:31
Last modified: 06 Jun 2018 12:42

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