AI-Driven Traffic Analysis in Digital Twin Networks


Authors: Sarah Al-Shareeda, Khayal Huseynov, Lal Verda Cakir, Craig Thomson, Mehmet Ozdem, Berk Canberk
 
Main Insights:
• This episode explores Digital Twin Networks (DTNs) and how AI-driven traffic analysis is reshaping our understanding and optimization of physical networks. 

DTNs are virtual models of various physical networks, from cellular and wireless to optical and satellite.

By leveraging computational analysis and AI, DTNs address real-world network challenges, including: • Enhancing performance
• Optimizing latency
• Boosting energy efficiency
• Managing resources
• Strengthening communication
• Predicting trends and disruptions
• Detecting anomalies • Ensuring security and privacy

Three-layer architecture of DTNs:
• Physical Layer • Virtual Layer
• Service/Decision Layer

AI tools in DTNs:
• Machine Learning (ML)
• Deep Learning (DL)
• Reinforcement Learning (RL)
• Federated Learning (FL)
• Graph-based approaches

Key challenges in AI-driven DTNs:
• Data quality
• Scalability
• Interpretability
• Security

Strategies for overcoming challenges:
• Prioritizing data quality and representation
• Ensuring robustness, reliability, and security
• Promoting transparency and interpretability
• Continuously refining models for better efficiency 


Conclusion:
The episode emphasizes AI’s transformative role in networked systems, highlighting how AI-powered DTNs promise more efficient, reliable, secure, and well-managed networks, paving the way for future advancements in network optimization.

Link to the paer:
https://arxiv.org/abs/2411.00681