Home / Current Issue / Paper 1707333
Enhancing 5G Infrastructure Reliability with AI-Driven Predictive Maintenance and Digital Twins
Subject area: Science,Engineering and Technology · Area of research: Artifical Intelligence
Abstract
This paper unveils a groundbreaking scheme for detecting faults within an artificial intelligence-driven digital twin architecture, seamlessly woven into the fabric of Long Range technology and the powerful embrace of wireless 5G networks. In a world where technology reigns, a powerful scheme emerges, uniting the strength of AI and DT to unveil the hidden anomalies lurking within the network's depths. With the power of LoRa-based sensors, we can gather the pulse of the spectrum in real-time, capturing the strength of signals, the shadows of interference, and the dance of occupancy like a symphony of data. In the realm of innovation, the DT harnesses the power of AI, weaving together the threads of machine learning and data analytics. It delves deep into the spectrum data, unearthing precious insights that shine like stars in the night sky. With these insights, we rise to unveil the shadows of interference, to foresee the dance of spectrum usage, and to conquer the faults that seek to bring us down. In the arena of assessment, we gather to measure the truth and the strength of our vision, striving to elevate the guardians of detection and the pulse of our networks, igniting a journey towards unparalleled performance. The findings reveal the promise of the AI-driven DT method, igniting a new era of efficiency and unwavering reliability in the realm of wireless 5G networks intertwined with LoRa.
Keywords
Digital Twin, Artificial Intelligence, Wireless 5G Network, and Low Power Networks.
References
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[59] Eldosoky, Mahmoud A., Jian Ping Li, Amin Ul Haq, Fanyu Zeng, Mao Xu, Shakir Khan, and Inayat Khan. "WallNet: Hierarchical Visual Attention-Based Model for Putty Bulge Terminal Points Detection." The Visual Computer (2024): 1-16.
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[86] S. Khan and M. Alshara, "Fuzzy Data Mining Utilization to Classify Kids with Autism," International Journal of Computer Science Network Security, vol. 19, no. 2, pp. 147-154, 2019.
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How to cite this paper
@article{1707333,
author = {Asrar Ahmad Ansari, Radha Raman Chandan},
title = {Enhancing 5G Infrastructure Reliability with AI-Driven Predictive Maintenance and Digital Twins},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {1544-1566},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707333.pdf},
abstract = {This paper unveils a groundbreaking scheme for detecting faults within an artificial intelligence-driven digital twin architecture, seamlessly woven into the fabric of Long Range technology and the powerful embrace of wireless 5G networks. In a world where technology reigns, a powerful scheme emerges, uniting the strength of AI and DT to unveil the hidden anomalies lurking within the network's depths. With the power of LoRa-based sensors, we can gather the pulse of the spectrum in real-time, capturing the strength of signals, the shadows of interference, and the dance of occupancy like a symphony of data. In the realm of innovation, the DT harnesses the power of AI, weaving together the threads of machine learning and data analytics. It delves deep into the spectrum data, unearthing precious insights that shine like stars in the night sky. With these insights, we rise to unveil the shadows of interference, to foresee the dance of spectrum usage, and to conquer the faults that seek to bring us down. In the arena of assessment, we gather to measure the truth and the strength of our vision, striving to elevate the guardians of detection and the pulse of our networks, igniting a journey towards unparalleled performance. The findings reveal the promise of the AI-driven DT method, igniting a new era of efficiency and unwavering reliability in the realm of wireless 5G networks intertwined with LoRa.},
keywords = {Digital Twin, Artificial Intelligence, Wireless 5G Network, and Low Power Networks.},
month = {March},
}