Fault-Tolerant Digital Twin Framework for Urban Data Integration Systems

Abstract

The rapid urbanization of modern cities presents significant challenges in resource management and public infrastructure optimization. This research addresses these issues by integrating digital twin technology with the Industrial Internet of Things (IIoT) to enhance the efficiency, scalability, and resilience of smart city infrastructures. The primary objective is to overcome existing limitations in data integration, real-time adaptability, and predictive accuracy through a novel framework that employs advanced data fusion methodologies, machine learning algorithms, and high-performance computing resources. The study utilizes supervised learning models such as random forests and gradient boosting, alongside unsupervised methods like k-means clustering for anomaly detection and predictive maintenance. The experimental setup involved high-fidelity simulations using IIoT-generated real-time data streams, with performance evaluated through metrics like Mean Absolute Error, Root Mean Square Error, and F1-scores. Results demonstrated a significant improvement in predictive accuracy and operational efficiency, alongside reduced energy consumption, validating the framework's applicability in real-world smart city scenarios. This research contributes to the body of knowledge by providing a scalable and robust solution for urban management and offers a foundation for future studies focused on refining computational resource management and extending digital twin applications across diverse urban domains.