IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning
Open accessAbstract
The assets in Industry 4.0 are categorized into physical, virtual, and human. The innovation and popularization of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc., for asset identification and tracking. The generated data from the Industrial Internet of Things (IIoT) ease information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of the manufacturing equipment. Predictive maintenance enables the business owner to decide, such as repairing or replacing the component before an actual failure that affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimize the task distributions and predictive maintenance model. This article presents the genetic algorithm (GA)-based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost, and energy performance of GA along with MinMin, MaxMin, FCFS, and RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time data sets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost, and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. Th…
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With one or two cloudlet wireless hops, the cloudlet outperformed the cloud-based approach for file editing and video streaming.
“With one or two cloudlet wireless “hops” used to transfer data, the cloudlet outperformed the cloud-based approach for application scenarios that included file editing and video streaming; with more intermediate steps, the cloud option performed better because of smaller request transfer delays [18].”
From Edge AI: A survey (2023) · cites this paper as [18]
With more intermediate steps, the cloud option performed better because of smaller request transfer delays.
“With one or two cloudlet wireless “hops” used to transfer data, the cloudlet outperformed the cloud-based approach for application scenarios that included file editing and video streaming; with more intermediate steps, the cloud option performed better because of smaller request transfer delays [18].”
From Edge AI: A survey (2023) · cites this paper as [18]
Cites
Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.10.043 · Cited by 669 · cited 1× · see citing works
“However, simulation research in [4] concludes that the cloud data center potentially experiences higher latency and network usage due to the vast geographical distance between IIoT devices and cloud data center.”
BackgroundSelf-citationRouter: fog enabled cloud based intelligent resource management approach for smart home iot devices
Gill, S. S., Garraghan, P., & Buyya, R. (2019) · Journal of Systems and Software · doi:10.1016/j.jss.2019.04.058 · Cited by 165 · see citing works
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Singh, R., & Gill, S. S. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.02.004 · Cited by 572 · see citing works
Self-citationFog computing: a taxonomy, systematic review, current trends and research challenges
Singh, J., Singh, P., & Gill, S. S. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.06.005 · Cited by 154 · see citing works
Self-citationSingh, H., Tyagi, S., Kumar, P., Gill, S. S., & Buyya, R. (2021) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2021.102353 · Cited by 104 · see citing works
Self-citationVarriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02244-8 · Cited by 79 · see citing works
Alsadie, D. (2024) · IEEE Access · doi:10.1109/access.2024.3447097 · Cited by 81 · see citing works
Edge Computing and Microservices Middleware for Home Energy Management Systems
Ferreira, L. C. B. C., Borchardt, A. D. R., Cardoso, G. D. S., Mendes Lemes, D. A., Sousa, G. R. D. R. D., Neto, F. B., de Lima, E. R., Fraidenraich, G., Cardieri, P., & Meloni, L. G. P. (2022) · IEEE Access · doi:10.1109/access.2022.3214229 · Cited by 28 · see citing works
“However, the middleware can also work under the fog computing paradigm, acting as a gateway, gathering data from multiple users and pre-processing them before sending them to the cloud [6].”
Background
- Authors
- Teoh, Y. K., Gill, S. S., & Parlikad, A. K.
- Affiliations
- Queen Mary University of London (GB)
- University of Cambridge (GB)
- Venue
- IEEE Internet of Things Journal
- Published
- 2023-02-01
- Volume
- 10
- Issue
- 3
- Pages
- 2087-2094
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3120799912
- Cited by
- 231
- References
- 2
- Citations by year
- 2026: 37 · 2025: 62 · 2024: 67 · 2023: 33 · 2022: 22 · 2021: 10
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- predictive maintenance, fog computing, Industrial Internet of Things, asset management, Industry 4.0, genetic algorithm, resource management, task scheduling, logistic regression, energy efficiency
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