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Cuadrado, FelixAuthor

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August 28, 2025
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CAPTAIN: A Testbed for Co-Simulation of Scalable Serverless Computing Environments for AIoT Enabled Predictive Maintenance in Industry 4.0

Publicated to: Ieee Internet Of Things Journal. 12 (16): 32283-32295 - 2025-08-15 12(16), DOI: 10.1109/JIOT.2024.3488283

Authors:

Golec, Muhammed; Wu, Huaming; Ozturac, Ridvan; Kumar Parlikad, Ajith; Cuadrado, Felix; Singh Gill, Sukhpal; Uhlig, Steve
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Affiliations

Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England - Author
Tianjin Univ, Ctr Appl Math, Tianjin 300072, Peoples R China - Author
Trendyol Grp, Engn Team, TR-34485 Istanbul, Turkiye - Author
Univ Cambridge, Inst Mfg, Dept Engn, Cambridge CB3 0FS, Cambs, England - Author
Univ Politecn Madrid, Sch Telecommun Engn, Madrid 28032, Spain - Author
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Abstract

The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module's performance using a predictive maintenance-based real-world IIoT application and the AI module's performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO(2). The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0.
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Keywords

Artificial intelligenceArtificial intelligence (ai)Cloud computingComputational modelingContainersFlexible manufacturingFourth industrial revolutionIndustrial internet of thingsIndustrial internet of things (iiot)ManufacturingPredictive maintenancePredictive modelsServerless computinServerless computingTime-frequency analysis

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Internet Of Things Journal due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 11/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Information Systems. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-12-21:

  • Google Scholar: 1
  • WoS: 4
  • Scopus: 4
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 21.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 19 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

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    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: China; Turkey; United Kingdom.

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    Awards linked to the item

    The work of Muhammed Golec was supported by the Ministry of Education of the Turkish Republic for the funding. The work of Huaming Wu was supported in part by the National Natural Science Foundation of China under Grant 62071327, and in part by the Tianjin Science and Technology Planning Project under Grant 22ZYYYJC00020. The work of Felix Cuadrado was supported by HE ACES Project under Grant 101093126.
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