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

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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ORCID for Geoffrey Merrett: orcid.org/0000-0003-4980-3894

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