Smart City Digital Twins: A Modular and Adaptive Architecture for Real-Time Data-Driven Urban Management

This paper presents a modular Digital Twin software architecture designed for smart cities, leveraging Edge-Cloud Continuum to enable the development of flexible and scalable DT-based solutions. Digital Twin technology provides a powerful framework for simulating, analyzing, and optimizing urban environments by integrating real-time and historical data from various city sensors through IoT, AI, and cloud computing. The proposed architecture addresses the limitations of existing DT frameworks by focusing on smart city-specific requirements such as dynamic resource management, real-time data processing, and autonomous decision-making. The viability of the proposed framework is demonstrated through a case study on autonomous traffic management in the city of Issy-les-Moulineaux. It shows how the proposed framework predicts traffic patterns and manages network resource allocation by adjusting the data sampling frequency to balance prediction accuracy and communication costs. The architecture's modular design supports seamless integration and adaptability, making it suitable for various smart city applications, thereby advancing the development of more efficient, sustainable, and resilient urban environments.