Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing

Nerea Gómez Larrakoetxea (), Borja Sánz Uquijo, Iker Pastor López, Jon García Barruetabeña and Pablo García Bringas
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Nerea Gómez Larrakoetxea: Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, Spain
Borja Sánz Uquijo: Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, Spain
Iker Pastor López: Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, Spain
Jon García Barruetabeña: Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, Spain
Pablo García Bringas: Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, Spain

Mathematics, 2024, vol. 13, issue 1, 1-16

Abstract: The 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.

Keywords: edge computing; real-time data processing; data modeling; industrial applications (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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