Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services

Demetris Trihinas, Lauritz Thamsen, Jossekin Beilharz, Moysis Symeonides

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Cloud Engineering, IC2E 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-35
Number of pages7
ISBN (Electronic)9781665491150
DOIs
Publication statusPublished - 2022
Event10th IEEE International Conference on Cloud Engineering, IC2E 2022 - Pacific Grove, United States
Duration: 26 Sept 202230 Sept 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Cloud Engineering, IC2E 2022

Conference

Conference10th IEEE International Conference on Cloud Engineering, IC2E 2022
Country/TerritoryUnited States
CityPacific Grove
Period26/09/2230/09/22

Keywords

  • Edge Computing
  • Energy Modeling
  • Internet of Things
  • Machine Learning
  • Software Testing

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