Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing
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. Thi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/1/29 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549127772536832 |
|---|---|
| author | Nerea Gómez Larrakoetxea Borja Sánz Uquijo Iker Pastor López Jon García Barruetabeña Pablo García Bringas |
| author_facet | Nerea Gómez Larrakoetxea Borja Sánz Uquijo Iker Pastor López Jon García Barruetabeña Pablo García Bringas |
| author_sort | Nerea Gómez Larrakoetxea |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f5a029048c14e6e863ac3c66f633ef9 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-2f5a029048c14e6e863ac3c66f633ef92025-01-10T13:18:00ZengMDPI AGMathematics2227-73902024-12-011312910.3390/math13010029Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge ComputingNerea Gómez Larrakoetxea0Borja Sánz Uquijo1Iker Pastor López2Jon García Barruetabeña3Pablo García Bringas4Faculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, SpainFaculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, SpainFaculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, SpainFaculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, SpainFaculty of Engineering, University of Deusto, Unibertsitate Etorb., 24, Deusto, 48007 Bilbo, SpainThe 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.https://www.mdpi.com/2227-7390/13/1/29edge computingreal-time data processingdata modelingindustrial applications |
| spellingShingle | Nerea Gómez Larrakoetxea Borja Sánz Uquijo Iker Pastor López Jon García Barruetabeña Pablo García Bringas Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing Mathematics edge computing real-time data processing data modeling industrial applications |
| title | Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing |
| title_full | Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing |
| title_fullStr | Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing |
| title_full_unstemmed | Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing |
| title_short | Enhancing Real-Time Processing in Industry 4.0 Through the Paradigm of Edge Computing |
| title_sort | enhancing real time processing in industry 4 0 through the paradigm of edge computing |
| topic | edge computing real-time data processing data modeling industrial applications |
| url | https://www.mdpi.com/2227-7390/13/1/29 |
| work_keys_str_mv | AT nereagomezlarrakoetxea enhancingrealtimeprocessinginindustry40throughtheparadigmofedgecomputing AT borjasanzuquijo enhancingrealtimeprocessinginindustry40throughtheparadigmofedgecomputing AT ikerpastorlopez enhancingrealtimeprocessinginindustry40throughtheparadigmofedgecomputing AT jongarciabarruetabena enhancingrealtimeprocessinginindustry40throughtheparadigmofedgecomputing AT pablogarciabringas enhancingrealtimeprocessinginindustry40throughtheparadigmofedgecomputing |