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Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services. (arXiv:2304.06414v1 [cs.DC])

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Energy consumption and carbon emissions are expected to be crucial factors
for Internet of Things (IoT) applications. Both the scale and the
geo-distribution keep increasing, while Artificial Intelligence (AI) further
penetrates the "edge" in order to satisfy the need for highly-responsive and
intelligent services. To date, several edge/fog emulators are catering for IoT
testing by supporting the deployment and execution of AI-driven IoT services in
consolidated test environments. These tools enable the configuration of
infrastructures so that they closely resemble edge devices and IoT networks.
However, energy consumption and carbon emissions estimations during the testing
of AI services are still missing from the current state of IoT testing suites.
This study highlights important questions that developers of AI-driven IoT
services are in need of answers, along with a set of observations and
challenges, aiming to help researchers designing IoT testing and benchmarking
suites to cater to user needs.

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