AI-based traffic analysis in digital twin networks

TL;DR

The paper surveys AI-driven traffic analysis in Digital Twin Networks (DTNs), outlining how a three-layer DTN architecture enables virtual representations of diverse physical networks to simulate, predict, and optimize real-world performance. It organizes the discussion around the DTN ecosystem, cross-domain development efforts, and key analytic tasks including performance enhancement, management, communication, prediction, anomaly detection, and security. It catalogs the main AI models and tools—ML, DL, RL, FL, and graph-based methods—used to address these tasks, and it details major challenges such as data quality, scalability, interpretability, and privacy, proposing responsible AI considerations. The work highlights domain-specific applications across 5G/6G, wireless, optical, satellite, vehicular, and IIoT networks, illustrating how AI-enabled DTNs can reduce latency, improve energy efficiency, optimize content delivery, and strengthen security. Overall, the paper provides a comprehensive framework to guide future research and deployment of AI-driven traffic analysis in DTNs.

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.

Figures (2)

• Figure 1: DTNs Descriptive Ecosystem
• Figure 2: Key AI-based Tasks in DTNs