# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 4 Integrating 5G with Real-Time Digital Twins
Large IoT segments can aid sensor networks within the public infrastructure by continuously tracking building integrity, air quality, and water supply systems to improve urban governance [67; 69; 70]. High-security slices or segments may be designated for governmental functions and emergency response systems, ensuring uninterrupted communication during critical events [62; 67; 62]. A highly promising use of this architecture is in smart grids, where digital twins of power distribution networks assess voltage variations, energy requirements, and equipment efficiency in real time [40; 47; 62; 71]. By integrating 5G with edge computing, utility providers can detect faults in power lines, predict maintenance needs, and optimize energy distribution, leading to higher efficiency and reduced downtime. For example, when a transformer shows initial signs of malfunction, the digital twin can initiate preventive maintenance, avoiding expensive outages and maintaining a consistent and dependable electricity supply for consumers. The 5G network architecture for real-time digital twin infrastructure serves as the foundation of advanced smart city environments, facilitating improved efficiency, flexibility, and durability in urban planning and management [9; 10; 37; 62; 67]. The integration of high-speed wireless communication, edge computing, and sophisticated data processing allows digital twins to actively engage with their physical equivalents, facilitating data-driven decision-making in diverse industries, including transportation, healthcare, energy, and security. A major advantage of integrating 5G with digital twins is its capacity to support expansive IoT ecosystems with high-density connectivity and real-time data processing [37; 67]. Modern smart cities rely on a vast network of interconnected sensors, cameras, autonomous vehicles, and smart grids that continuously generate and exchange data. The ultra-reliable, low-latency capabilities of 5G ensure that these digital twins receive accurate, real-time updates, enhancing their ability to simulate, predict, and optimize urban systems efficiently [10; 55; 71]. For instance, 5G-powered digital twins of traffic systems can dynamically analyze sensor data from intersections, vehicles, and pedestrian crossings to modify signal timings, reroute traffic, and prevent congestion in real time [40; 47; 62; 71]. Beyond traffic management, 5G enables edge computing to offload processing tasks from central servers, reducing delays and improving response times for critical urban services. This distributed approach is especially beneficial for applications such as disaster management, where edge-based digital twins of emergency infrastructure can rapidly identify threats, optimize resource allocation, and trigger automated emergency responses within seconds [10; 71]. One particularly transformative use case is in urban water management. A real-time digital twin of a city's water distribution system can monitor pressure fluctuations, contamination levels, and leaks, identifying anomalies that may indicate pipe bursts or contamination risks. When integrated with automated control systems, these digital twins can isolate affected areas, reroute the water supply, and dispatch maintenance crews proactively, reducing disruptions and preventing resource wastage [62; 67]. The significant reduction in latency achieved by edge computing compared with traditional cloud-based processing within the context of urban digital twins for smart cities is illustrated in Figure 11. As shown, edge computing (blue line) results in a lower latency across various time steps, enhancing the efficiency of real-time data processing. This reduction in latency is critical for improving the responsiveness and overall performance of smart city applications, enabling faster decision-making and better urban management [44; 69; 70]. Another potential application lies in infrastructure upkeep and predictive analysis.