An Architecture and Realisation of a Smart City Digital Twin With eHealth Case Studies

Abstract

Digital twinning is an advanced technology that involves creating virtual replicas of various physical systems. In smart cities, digital twins serve as digital representations that model and simulate various urban elements, such as environment protection (e.g., air quality), critical infrastructure, transportation networks and other urban management processes. It has recently gained considerable attention for its transformative potential, enabling city authorities to visualise and analyse complex city dynamics for better-informed decision-making. Therefore, this paper proposes a simplified layered architecture for smart city digital twins. The layers of the proposed architecture cover the range of operations required by the functionality of the digital twin and the interaction between them, from data transfer or synthesis to big data streaming and intelligent analytics. The paper also introduces an open-source software tool that realises the proposed architecture, with each layer designed as an independent Python module for easy integration and maintenance. Three case studies are used to demonstrate the capabilities of the tool. One use case addresses short-term forecasting of the air quality index, whereas the other use case targets the detection of an individual's respiratory condition based on data received from wearable devices. The third case combines the other two cases to offer a warning system for residents with medical conditions based on air quality. The results of the case studies show the tool's ability to effectively handle environment and eHealth-related use cases and combine them for the welfare of smart city residents, leading to a more resilient health-focused urban landscape.