Enhancing real-time processing in Industry 4.0 through the paradigm of edge computing

Vista sencilla de ítem
dc.contributor.authorGomez Larrakoetxea, Nerea
dc.contributor.authorSanz Urquijo, Borja
dc.contributor.authorPastor López, Iker
dc.contributor.authorGarcía Barruetabeña, Jon
dc.contributor.authorGarcía Bringas, Pablo
dc.date.accessioned2025-02-13T15:10:41Z
dc.date.available2025-02-13T15:10:41Z
dc.date.issued2025-01
dc.date.updated2025-02-13T15:10:41Z
dc.description.abstractThe industrial sector has undergone significant digital transformation, driven by advancements in technology and the Internet of Things (IoT). These developments have facilitated the collection of vast quantities of data, which, in turn, pose significant challenges for real-time data processing. This study seeks to validate the efficacy and accuracy of edge computing models designed to represent subprocesses within industrial environments and to compare their performance with that of traditional cloud computing models. By processing data locally at the point of collection, edge computing models provide substantial benefits in minimizing latency and enhancing processing efficiency, which are crucial for real-time decision-making in industrial operations. This research demonstrates that models derived from distinct subprocesses yield superior accuracy compared to comprehensive models encompassing multiple subprocesses. The findings indicate that an increase in data volume does not necessarily translate to improved model performance, particularly in datasets that capture data from production processes, as combining independent process data can introduce extraneous ‘noise’. By subdividing datasets into smaller, specialized edge models, this study offers a viable approach to mitigating the latency challenges inherent in cloud computing, thereby enhancing real-time data processing capabilities, scalability, and adaptability for modern industrial applications.en
dc.identifier.citationGómez Larrakoetxea, N., Sánz Uquijo, B., López, I. P., Barruetabeña, J. G., & Bringas, P. G. (2025). Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing. Mathematics, 13(1). https://doi.org/10.3390/MATH13010029
dc.identifier.doi10.3390/MATH13010029
dc.identifier.eissn2227-7390
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2294
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2024 by the authors
dc.subject.otherData modeling
dc.subject.otherEdge computing
dc.subject.otherIndustrial applications
dc.subject.otherReal-time data processing
dc.titleEnhancing real-time processing in Industry 4.0 through the paradigm of edge computingen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleMathematics
oaire.citation.volume13
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
gomez_enhancing_2025.pdf
Tamaño:
241.84 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos