Walani, C.C. and Doorsamy, W. orcid.org/0000-0001-9043-9882 (2025) Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring. Big Data and Cognitive Computing, 9 (5). 121. ISSN 2504-2289
Abstract
This study evaluates edge and cloud computing paradigms in the context of data-driven condition monitoring of rotating electrical machines. Two well-known platforms, the Raspberry Pi and Amazon Web Services Elastic Compute Cloud, are used to compare and contrast these two computing paradigms in terms of different metrics associated with their application suitability. The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. The findings reveal that while the cloud platform offers superior computational and memory resources, making it more suitable for complex machine learning tasks, it also incurs higher costs and latency. On the other hand, the edge platform excels in real-time processing and reduces network data burden, but its computational and memory resources are found to be a limitation with certain tasks. The study provides both quantitative and qualitative insights into the trade-offs involved in selecting the most suitable computing approach for condition monitoring applications. Although the scope of the empirical study is primarily limited to factors such as computational efficiency, scalability, and resource utilisation, particularly in the context of specific machine learning models, this paper offers broader discussion and future research directions of other key issues, including latency, network variability, and energy consumption.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 by the authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | condition monitoring; cloud computing; edge computing; induction machine; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jul 2025 15:10 |
Last Modified: | 07 Jul 2025 15:10 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/bdcc9050121 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:228671 |