# Unleashing the Potential of 5G for Smart Cities: A Focus on Real-Time Digital Twin Integration
## 4 Integrating 5G with Real-Time Digital Twins  
The basic principle of digital twins revolves around generating a dynamic virtual replica of a physical system, enabling continuous synchronization between the digital and physical worlds [9; 10; 11]. A digital twin is built using data collected from sensors, IoT devices, and other sources, allowing it to mirror real-world conditions in real time [36; 68]. Through the use of advanced analytics, simulations, and artificial intelligence, digital twins provide significant insights into system behavior, predict future states, and optimize performance. This technology has been widely adopted across industries from manufacturing and healthcare to urban planning, where it enhances decision-making, improves efficiency, and reduces operational risks [10; 11]. The concept of integrating real-time digital twins into smart cities focuses on a virtual depiction of a physical entity that has surfaced as a game-changing instrument for overseeing intricate systems [9; 10]. These digital representations offer an active, real-time perspective of city infrastructure, enabling urban planners and stakeholders to assess performance, anticipate problems, and model scenarios for informed decision-making [9; 67]. Figure 9: Energy consumption comparison: 4G vs. 5G network deployment [10; 28; 57; 67]. For instance, a digital twin of an urban water distribution system could provide real-time monitoring of water pressure, consumption, and potential leakages. If a system detects a decrease in water pressure in a particular district, it can alert operators about possible leaks and recommend preemptive maintenance actions, thus minimizing downtime and saving on operational costs. The efficiency of digital twins relies on the capacity to handle and evaluate large volumes of real-time information. This is where 5G technology is needed for the supply of fast, low-latency, and dependable connectivity to facilitate the smooth functioning of real-time digital twins [10; 37; 62]. In practice, this means that 5G networks enable faster data transfer between IoT devices (such as sensors and actuators) and digital twin systems, which is needed for applications like autonomous traffic management, real-time disaster response, and energy-grid balancing. For example, 5G enables smart traffic systems that control traffic signals based on current traffic patterns and sensor inputs possess the capability to significantly reduce road congestion and travel time during peak hours. The 5G network architecture for real-time digital twin infrastructure represented in Figure 10 shows the key components and their interconnections. At the core of this architecture is the Core Network (blue), which serves as the central hub; this manages the data traffic and links with the Radio Access Network (RAN) (green) to facilitate wireless communication [10; 60; 63]. The Core Network controls essential functions such as authentication, security, mobility management, and data routing, thus ensuring that different digital twin applications operate seamlessly across multiple connected devices and services. A fundamental component of this architecture is Edge Computing (red), which is strategically placed close to IoT devices (cyan) to allow for real-time data processing with minimal latency. Unlike traditional cloud computing, where all the data are sent to centralized data centers for processing, edge computing enables computations to occur at network edges, reducing the time needed for data transmission. This radically improves response times, making real-time digital twins highly effective for applications that demand instant decision-making, such as autonomous traffic control, emergency responses, and smart energy management [10; 37; 60; 63]. In a real-time traffic management system, for instance, sensors embedded in roads and traffic lights continuously collect data about vehicle density, pedestrian movement, and environmental conditions.