Digital Twin-Based Framework for Predictive Modeling of Infrastructure in IoT-Enabled Smart Cities
Abstract
Innovative, customizable data collection and intelligent decision-making have driven rapid advances in IoT technology, and, in turn, robust, complex smart urban infrastructure is envisioned to achieve the goals of smart cities. Nevertheless, the ability to assess and maintain 'real' urban infrastructure systems remains a challenge. This paper introduces a Digital Twin (DT) model as a framework for targeted, 'predictive' infrastructure systems in IoT smart cities. This approach combines the IoT smart city infrastructure predictive urban systems Digital Twin framework for predictive urban systems managed for analytics, and data-driven machine learning to bridge the technology gap in 'virtual' smart cities infrastructure systems managed to provide a 'digital' urban infrastructure smart systems to provide predictive analytics for ongoing real-time maintenance and optimization of fault prediction and maintenance scheduling of infrastructure smart systems. Operational decision systems, predictive decision systems, and targeted and informed decision systems in urban systems. A constructed urban transportation network for managed, predictive urban infrastructure systems for smart cities, as a developed, targeted system to assess and determine system fault, predictive maintenance, and optimization opportunities, with closed-loop technology for maintenance. This promotes the sustainability of smart cities.