AI-based traffic analysis in digital twin networks

• URL: http://arxiv.org/abs/2411.00681v1
• Date: Fri, 01 Nov 2024 15:41:23 GMT
• Title: AI-based traffic analysis in digital twin networks
• Authors: Sarah Al-Shareeda, Khayal Huseynov, Lal Verda Cakir, Craig Thomson, Mehmet Ozdem, Berk Canberk,
• Abstract summary: Digital Twin Networks (DTNs) are revolutionizing how we understand and optimize physical networks. They leverage computational power and AI capabilities to provide virtual representations, leading to highly refined recommendations for real-world network challenges. This chapter delves into the world of AI-driven traffic analysis within DTNs.
• Score: 3.4742424312781752
• License:
• Abstract: In today's networked world, Digital Twin Networks (DTNs) are revolutionizing how we understand and optimize physical networks. These networks, also known as 'Digital Twin Networks (DTNs)' or 'Networks Digital Twins (NDTs),' encompass many physical networks, from cellular and wireless to optical and satellite. They leverage computational power and AI capabilities to provide virtual representations, leading to highly refined recommendations for real-world network challenges. Within DTNs, tasks include network performance enhancement, latency optimization, energy efficiency, and more. To achieve these goals, DTNs utilize AI tools such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and graph-based approaches. However, data quality, scalability, interpretability, and security challenges necessitate strategies prioritizing transparency, fairness, privacy, and accountability. This chapter delves into the world of AI-driven traffic analysis within DTNs. It explores DTNs' development efforts, tasks, AI models, and challenges while offering insights into how AI can enhance these dynamic networks. Through this journey, readers will gain a deeper understanding of the pivotal role AI plays in the ever-evolving landscape of networked systems.

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