Continuous Training vs. Transfer Learning on Edge and Fog Environments: A Steam Detection use Case
Kukkaro, Ari; Moreschini, Sergio; Hästbacka, David (2024-12-27)
IEEE
27.12.2024
A. Kukkaro, S. Moreschini and D. Hästbacka, "Continuous Training vs. Transfer Learning on Edge and Fog Environments: A Steam Detection use Case," 2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Paris, France, 2024, pp. 138-141, doi: 10.1109/SEAA64295.2024.00029
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503252200
https://urn.fi/URN:NBN:fi:oulu-202503252200
Tiivistelmä
Abstract
The implementation of smart manufacturing, which utilises advanced digital technologies to enhance the agility and productivity of the traditional manufacturing sector, has the potential to reduce resource consumption, optimise processes and enhance safety. One challenge in process automation (PA) is its strict real-time requirements. One solution to this challenge is the use of Edge and Fog computing platforms with finite computational power, which brings processing and data storing closer to the data sources. This proximity of computing devices reduces the latency and bandwidth requirements, relaxes the need for a reliable Internet connection, and provides more security in design over the Cloud solutions. This paper compares the performance of Edge and Fog computing for soft real-time machine learning-based visual process monitoring that supports the human operator. The objective is to get a better understanding how this ML task can be relocated within Edge and Fog layers. Moreover, the article provides con-siderations of emerging difficulties of practical implementation of Continuous Training pipeline and soft real-time steam detection.
The implementation of smart manufacturing, which utilises advanced digital technologies to enhance the agility and productivity of the traditional manufacturing sector, has the potential to reduce resource consumption, optimise processes and enhance safety. One challenge in process automation (PA) is its strict real-time requirements. One solution to this challenge is the use of Edge and Fog computing platforms with finite computational power, which brings processing and data storing closer to the data sources. This proximity of computing devices reduces the latency and bandwidth requirements, relaxes the need for a reliable Internet connection, and provides more security in design over the Cloud solutions. This paper compares the performance of Edge and Fog computing for soft real-time machine learning-based visual process monitoring that supports the human operator. The objective is to get a better understanding how this ML task can be relocated within Edge and Fog layers. Moreover, the article provides con-siderations of emerging difficulties of practical implementation of Continuous Training pipeline and soft real-time steam detection.
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