## 5 Research Limitations and Future Research  
The integration of AI-powered Digital Twins and IoT in smart cities and sustainable building environments faces several limitations that hinder broader implementation and impact [7; 21; 46]. A significant challenge is the lack of standardization across IoT devices, data formats, and protocols, which makes it difficult to create cohesive systems that communicate effectively. On the other hand, this lack of uniformity limits interoperability between devices and platforms, creating data silos that reduce the efficiency and effectiveness of Digital Twins. Additionally, data integration challenges are substantial; the vast amounts of data collected by IoT devices from various sources need to be harmonized, cleaned, and processed to generate actionable insights [14; 22]. The heterogeneous nature of data collected from diverse devices in real-time complicates the process, often resulting in delays or inaccuracies in decision-making processes. Figure 14: DT- AI applications in Building Environment. The key limitations and gaps in the implementation of AI-powered Digital Twins and IoT in smart cities and sustainable building environments are outlined in Figure 15. One primary limitation is the lack of standardized data protocols and integration frameworks, leading to fragmented systems and limited interoperability among various devices and platforms [50; 54; 98]. Additionally, challenges around data quality, privacy, and security are prevalent, as the vast amounts of information collected from IoT devices must be effectively managed to ensure reliability and user trust. Another gap is in the scalability of AI models to process real-time data continuously, which is critical for accurate predictions and effective decision-making in dynamic urban environments. Addressing these issues would require advancements in data standardization, security measures, and model scalability to fully realize the potential of Digital Twins and IoT in enhancing urban sustainability [14; 30; 33]. Efficient integration of heterogeneous data from different types of sensors is crucial for maintaining accurate and reliable digital twin models. Techniques such as data fusion, including Kalman filtering and Bayesian inference, can combine data from multiple sources to improve accuracy and consistency. Middleware platforms, such as Apache Kafka and MQTT, enable seamless integration and real-time data streaming from diverse IoT devices, ensuring low-latency communication [39; 50; 99]. To maintain data quality, preprocessing techniques such as normalization, outlier detection, and imputation address issues of noise and missing data. Furthermore, edge computing can preprocess and validate sensor data locally, reducing network load and ensuring real-time performance. Standardized communication protocols, such as OPC UA and IoT-specific APIs, enhance interoperability between disparate systems, ensuring that heterogeneous data sources contribute cohesively to the digital twin's dynamic model, ultimately improving predictive capabilities and operational efficiency [9; 28; 48]. Future research in this field must address the standardization and interoperability challenges to enable seamless integration of AI-driven Digital Twins across urban and building systems. Developing universal standards for IoT devices and data protocols could pave the way for unified systems capable of supporting sustainable urban management  
Figure 15: Limitations of DT-AI Integration in Building Environments. at scale [50; 64; 91; 100]. Furthermore, improving the scalability and flexibility of AI models to accommodate high-frequency data in real-time applications remains a significant challenge. As smart city initiatives grow in complexity, future research must also explore advanced data fusion techniques, such as federated learning and blockchain, for enhanced data security and privacy [8; 33; 35; 101].