AI and Digital Twin for Consumer Electronics in Smart Cities

The development of smart cities is being driven by consumer electronics-grade digital twin led by a robust combination of advanced Artificial Intelligence (AI) technologies. AI's capabilities for complex data analysis and pattern recognition provide a solid foundation for real-time data transfer and feedback in digital twin, enhancing simulation and analytics capabilities. The security and privacy of consumer electronics-grade digital twin are critical for smart cities, especially in the context of Internet of Things (IoT) security, as they simulate and control real-world critical infrastructure. The combination of sophisticated AI algorithms for network security and threat detection can effectively prevent potential network attacks and data breaches, ensuring the safety and reliability of digital twin systems. Smart transportation digital twin systems stand out as exemplary applications of AI technologies within the realm of digital twin. These systems simulate city traffic flows, autonomous vehicles, and urban planning, leading to improved traffic efficiency, reduced congestion and accidents, and promoting sustainable urban development. Considering the significance vehicle trajectory prediction holds in smart transportation digital twin systems, we propose a vehicle-to-vehicle interaction-based Dynamic Graph Neural Network (DGNN) model, which we experimentally analyzed for its performance in vehicle trajectory prediction.