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

TL;DR

This paper tackles latency and data-distribution bottlenecks in cloud-centric Digital Twin (DT) deployments over IP networks. It proposes an NDN-based, four-layer Intellifiber framework that enables edge-distributed DTs, leveraging content-name routing and in-network caching to reduce data fetch latency. Through NS-3/ndnSIM simulations on an Intellifiber topology with five DT types, the study shows that NDN reduces DT latency by about 10x compared to IP, while deploying DTs at the edge offers an additional ~46% latency reduction relative to cloud-only DTs. The findings support data-centric DT federation and caching as a path toward scalable, real-time DT services, with privacy-preserving potential via localized data processing; the work is an early-stage but compelling demonstration of NDN for distributed DTs.Future work could explore privacy-preserving federated approaches and integration with future networks (e.g., 6G) to further enhance DT orchestration across distributed landscapes.

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.

Figures (8)

• Figure 1: Topology of Intellifiber
• Figure 2: NDN-assisted Digital Twins
• Figure 3: CDF of response latency
• Figure 4: Average response latency
• Figure 5: Count of cache hits