Industrial Metaverse Urban Digital Twins: A Reference Architecture for Edge-Synchronized Smart-City Infrastructure

Abstract

Smart-city infrastructure is increasingly operated through cyber–physical systems that combine embedded sensing, edge computing, communication networks, automation platforms, and digital-twin environments. The emerging industrial metaverse extends this paradigm by enabling immersive, real-time interaction with infrastructure assets, mobile agents, and operational data. Existing metaverse-city discussions, however, often remain visualization-oriented, and the literature offers no unifying architectural framework for integrating geospatial modeling, industrial sensing, distributed synchronization, multi-agent simulation, and human-in-the-loop control into a deployable smart-city platform. This paper presents a design framework and reference architecture for industrial metaverse urban digital twins. The framework has not yet been implemented as a software system; its value lies in a principle-driven design blueprint—layers, responsibilities, inter-layer data flows, and design rules—that is intended to guide future implementations. The architecture organizes the system into five layers: physical infrastructure and sensing, geospatial–semantic modeling, edge/cloud digital-twin services, multi-agent simulation and analytics, and immersive human–machine interaction. It links WGS-84 georeferencing, semantic city models, event-driven synchronization, and latency-aware networking into a unified computational pipeline. Boundary conditions, deployment assumptions, and the integration of legacy devices that do not conform to prevailing standards are stated for each layer, and security and data-governance obligations are assigned per layer. An illustrative smart-city scenario and a proposed evaluation protocol show how future implementations can be assessed under different latency and crowd-density conditions; no empirical results are reported in this Perspective. The protocol defines each metric formally, ranks the metrics by priority, documents the conflicts among them, and states test scenarios with acceptance thresholds. Analytical resource budgets, derived from stated assumptions rather than measured, identify interest management as the decisive scaling mechanism. Deployment challenges related to scalability, synchronization accuracy, cybersecurity, accessibility, cross-domain operation under multiple administrative authorities, and standards-based interoperability are also discussed.