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ML-Based Network Efficiency Prediction in IIoT.

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Bibliographische Detailangaben
Titel: ML-Based Network Efficiency Prediction in IIoT.
Autoren: Mensah, Queen Emmanuella, Musa, Usman Ibrahim, Sinjanka, Yusupha, Kaur, Kawaljeet
Quelle: International Journal of Design, Analysis & Tools for Integrated Circuits & Systems; Dec2023, Vol. 12 Issue 2, p24-29, 6p
Schlagwörter: INDUSTRIAL productivity, NETWORK performance, INDUSTRIAL capacity, INTERNET of things, INDUSTRIAL expansion, MACHINE learning
Abstract: The rapid expansion of Industrial Internet of Things (IIoT) technology has brought significant advancements to industrial operations, enabling real-time data monitoring and control. Nevertheless, optimizing network efficiency within the IIoT ecosystem remains a central concern. This research paper investigates the role of Machine Learning (ML) in predicting network efficiency. We conduct an extensive assessment of the ML model’s performance, revealing its high accuracy and robust generalization capabilities. These qualities position it as an indispensable asset for IIoT applications. Our findings underscore the significance of ML in enhancing IIoT network performance and highlight its potential to transform industrial operations. The model’s accuracy in predicting network efficiency, specifically distinguishing between high and low states, demonstrates its practical utility in facilitating proactive maintenance and resource allocation, ultimately leading to improved industrial productivity. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:The rapid expansion of Industrial Internet of Things (IIoT) technology has brought significant advancements to industrial operations, enabling real-time data monitoring and control. Nevertheless, optimizing network efficiency within the IIoT ecosystem remains a central concern. This research paper investigates the role of Machine Learning (ML) in predicting network efficiency. We conduct an extensive assessment of the ML model’s performance, revealing its high accuracy and robust generalization capabilities. These qualities position it as an indispensable asset for IIoT applications. Our findings underscore the significance of ML in enhancing IIoT network performance and highlight its potential to transform industrial operations. The model’s accuracy in predicting network efficiency, specifically distinguishing between high and low states, demonstrates its practical utility in facilitating proactive maintenance and resource allocation, ultimately leading to improved industrial productivity. [ABSTRACT FROM AUTHOR]
ISSN:2223523X