# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 2 Current Trends for 5G in Smart Cities
### Existing Applications of 5G in Smart Cities
In this visualization, multiple IoT devices, each representing various emergency responders and smart sensors, are randomly  
Figure 5: Self-driving vehicle communication latency improvements [42; 47; 48; 49]. distributed within a defined urban space. These devices are algorithmically connected to their nearest 5G base stations, which serve as intermediate nodes facilitating high-speed, low-latency communication. The base stations, in turn, maintain direct links to a centralized command hub responsible for aggregating and analyzing the incoming data. This spatial and topological arrangement reflects how such networks would function in real-world smart city deployments. The plotted connections, color-coded nodes, and strategic placement of network components aim to capture the architecture's efficiency and responsiveness in emergency situations. By portraying the dynamic interplay between sensing, transmission, and centralized decision-making, Figure 6 underscores the infrastructure's role in enabling robust and scalable emergency response systems. This architecture aligns with smart cities' objectives, where digital twins leverage such infrastructure for predictive analysis and efficient emergency management. The robust integration of IoT and 5G enhances situational awareness and rapid responsiveness, accentuating its potential in advancing urban resilience and safety [20; 51]. The relationship between latency and key AI surveillance performance metrics such as accuracy and bandwidth utilization are shown in Figure 7. There are critical trade-offs in 5G network environments [51; 52; 53]. As the latency decreases, both the accuracy and bandwidth utilization improve, demonstrating the potential of 5G to enhance real-time responsiveness and data efficiency [20; 51]. The graph presented in Figure 7 was generated using MATLAB as part of a study on AI surveillance performance metrics under 5G network conditions. The data used for its creation were manually defined rather than sourced from an empirical dataset, with the primary objective of illustrating expected trends in AI-driven systems operating under varying latency conditions. Specifically, the plotted metrics include accuracy (%) and bandwidth utilization (%), both measured as functions of latency (ms). The selected values reflect a trend-based approach, where accuracy increases with latency, potentially due to improved data processing capabilities, while bandwidth utilization also rises, likely because higher latency conditions allow for greater data transmission. These trends align with established principles in networked AI systems, where latency often influences the trade-off between computational performance and resource consumption. Figure 6: 5G-enabled emergency response communication architecture [20; 51; 52; 53]. While the dataset is not derived from real-world measurements, it was intentionally designed to simulate plausible system behavior, making it useful for conceptual analyses. This approach enables a clear visualization of performance trends, facilitating a deeper understanding of how AI surveillance systems may interact with 5G network parameters. Real-time AI-driven surveillance systems require high precision and seamless data exchange to model, monitor, and predict urban dynamics effectively [51; 52]. The adoption of low-latency 5G networks can significantly optimize AI applications, enabling smarter and more responsive urban infrastructure. By incorporating 5G into these applications, smart cities can reach an enhanced level of efficiency, sustainability, and safety.