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- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2022 Association for Computing Machinery.
- ISSN
- 1550-4859
- eISSN
- 1550-4867
- DOI
- 10.1145/3526195
- Publisher site
- See Article on Publisher Site
Abstract
Artificial Intelligence (AI) technology has been empowered to be a significant driven force within the edge context for powering up contemporary complex systems, such as smart critical infrastructure. Interconnectivity between physical and cyber spaces further introduces the needs of digital twin, which allows AI-based solutions to optimize various tasks in physical operations. However, due to the complexity of the setting in digital twin, task allocation is encountering multiple challenges, such as concurrent meeting the requirements of energy saving, efficiency, and accuracy. In this work, we propose a Digital Twin-Enabled Edge AI (DTE2AI), supported by our Energy-aware High Accuracy Strategy (EAHAS), which focuses on optimizing the training accuracy of AI tasks under the limits of training time and energy consumption. The average of the training accuracy was enhanced 12% based on our experiment evaluations.
Journal
ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery
Published: Sep 15, 2022
Keywords: Digital twin