Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

This article explores the transformative integration of cloud computing, artificial intelligence, and 5G networks, focusing on predictive maintenance and personalized service delivery. The article examines how 5G infrastructure generates unprecedented volumes of data that can be leveraged for intelligent network management through AI-driven analytics. The article presents a novel framework for integrating federated learning with 5G infrastructure to preserve privacy while maintaining prediction accuracy, evaluates deep learning-based anomaly detection algorithms for fault prediction, and develops a cloud-native architecture for dynamic resource allocation. Key areas explored include theoretical frameworks for AI-driven 5G networks, predictive maintenance methodologies that employ diverse machine learning approaches, privacy-preserving AI techniques that protect sensitive user data, and personalized service delivery systems that adapt to user contexts in real time. The findings demonstrate significant improvements in operational efficiency, network reliability, service personalization, and regulatory compliance while maintaining privacy and security.

Authors

Publication Details

Journal
World Journal of Advanced Research and Reviews
Published
2025-05-04
DOI
https://doi.org/10.30574/wjarr.2025.26.2.1588
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

Vivek Aby Pothen
World Journal of Advanced Research and Reviews
IoT and Edge/Fog Computing
article

Cloud-Powered 5G: Leveraging ai and massive datasets for predictive maintenance and personalized services

Vivek Aby Pothen
article en

Abstract

This article explores the transformative integration of cloud computing, artificial intelligence, and 5G networks, focusing on predictive maintenance and personalized service delivery. The article examines how 5G infrastructure generates unprecedented volumes of data that can be leveraged for intelligent network management through AI-driven analytics. The article presents a novel framework for integrating federated learning with 5G infrastructure to preserve privacy while maintaining prediction accuracy, evaluates deep learning-based anomaly detection algorithms for fault prediction, and develops a cloud-native architecture for dynamic resource allocation. Key areas explored include theoretical frameworks for AI-driven 5G networks, predictive maintenance methodologies that employ diverse machine learning approaches, privacy-preserving AI techniques that protect sensitive user data, and personalized service delivery systems that adapt to user contexts in real time. The findings demonstrate significant improvements in operational efficiency, network reliability, service personalization, and regulatory compliance while maintaining privacy and security.

World Journal of Advanced Research and ReviewsVol. 26(2)
Openalex Percentile: Top 91%
IoT and Edge/Fog Computing
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