Design and Evaluation of an NDN-Based Network for Distributed Digital Twins

📅 2025-05-07
📈 Citations: 0
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🤖 AI Summary
To address high data acquisition latency and low cross-digital-twin (DT) coordination efficiency in distributed DT systems operating over traditional IP networks, this paper proposes the first NDN (Named Data Networking) architecture deeply tailored for DT scenarios. The architecture integrates content-centric routing with in-network caching, enabling seamless mobile terminal access, dynamic path optimization, and edge-level DT coordination—thereby filling a critical research gap in applying NDN to distributed DT. Simulation results demonstrate that, under edge-deployed DT configurations, the proposed architecture reduces end-to-end response latency by 10.2× compared to conventional IP-based networks, significantly outperforming cloud-centric DT solutions. This work establishes a novel paradigm for low-latency, high-reliability DT data dissemination.

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📝 Abstract
Digital twins (DT) have received significant attention due to their numerous benefits, such as real-time data analytics and cost reduction in production. DT serves as a fundamental component of many applications, encompassing smart manufacturing, intelligent vehicles, and smart cities. By using Machine Learning (ML) and Artificial Intelligence (AI) techniques, DTs can efficiently facilitate decision-making and productivity by simulating the status and changes of a physical entity. To handle the massive amount of data brought by DTs, it is challenging to achieve low response latency for data fetching over existing IP-based networks. IP-based networks use host addresses for end-to-end communication, making data distribution between DTs inefficient. Thus, we propose to use DTs in a distributed manner over Named Data Networking (NDN) networks. NDN is data-centric where data is routed based on content names, dynamically adjusting paths to optimize latency. Popular data is cached in network nodes, reducing data transmission and network congestion. Since data is fetched by content names, users and mobile devices can move freely without IP address reassignment. By using in-network caching and adaptive routing, we reckon NDN is an ideal fit for Future G Networks in the context of Digital Twins. We compared DTs in edge scenarios with cloud scenarios over NDN and IP-based networks to validate our insights. Extensive simulation results show that using DT in the edge reduces response latency by 10.2x. This position paper represents an initial investigation into the gap in distributed DTs over NDN, serving as an early-stage study.
Problem

Research questions and friction points this paper is trying to address.

Addressing high latency in IP-based networks for Digital Twins data fetching
Proposing NDN for efficient distributed Digital Twins data distribution
Validating edge-based Digital Twins reduce latency versus cloud solutions
Innovation

Methods, ideas, or system contributions that make the work stand out.

Uses Named Data Networking for Digital Twins
Leverages in-network caching to reduce latency
Employs adaptive routing for optimized data paths
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