## 3 Architecture and Devices of Simple DTs vs. Complex AI-Based DTs  
In simple DTs, the architecture is lightweight, typically consisting of a basic sensor-to-cloud or sensor-to-edge configuration. It uses IoT devices such as temperature, pressure, or motion sensors to collect data. Data is transmitted via wired (Ethernet) or wireless (Wi-Fi, Bluetooth, Long Range Wide Area Network (LoRaWAN)) communication. A cloud platform (AWS Internet of Things (IoT), Amazon, Seattle, WA, USA, Azure IoT, Microsoft, Redmond, WA, USA, or an on-premises server) stores and processes the data in real time. Dashboards and analytics tools (Grafana 12.0.2, Grafana Labs, Penthouse, NY, USA, Power BI, Microsoft, Redmond, WA, USA) provide simple visualizations. The system uses a rule-based model, meaning that responses are predefined and deterministic. It often uses microcontrollers (ESP32, TSMC, Hsinchu, Taiwan, Arduino, Arduino S.R.L., Monza, Italy) or single-board computers (Raspberry Pi, Raspberry Pi Foundation, Cambridge, UK) for edge computing. Updates are manual or scheduled with little real-time adaptation [15; 16; 17].  
In complex AI-based DTs, the architecture is multi-layered, including IoT, edge computing, AI, and deep learning models in the cloud. It uses advanced edge devices (NVIDIA Jetson, NVIDIA, Santa Clara, CA, USA, Intel Movidius, Intel, Santa Clara, CA, USA) with AI inference capabilities; includes high-precision sensors (light detection and ranging (LiDAR), 3D cameras, industrial-grade IoT sensors) to collect rich data; and supports real-time data processing using AI and ML models, enabling predictive analytics and autonomous decision making. AI algorithms are deployed on AI edge devices or AI cloud services (Google AI models, Google, Mountain View, CA, USA, Azure ML, Microdot, Redmond, WA, USA, TensorFlow, Google, Mountain View, CA, USA). It integrates digital simulation tools (Ansys Twin Builder, Ansys Inc., Canonsburg, PA, USA, Siemens MindSphere, Siemens, Munich, Germany) for continuous optimization. Self-learning engines enable adaptive updates, making the twin dynamic and evolving over time [18; 19; 20].  
Figure 9: Publications by funding.  
Ways of Personalizing DTs to Optimize Adaptation to Signal Selection, Gathering, and Preprocessing Level  
The personalization of DTs involves dynamically selecting the most appropriate sensors based on changing environmental conditions or system needs, optimizing data accuracy and relevance. AI-based algorithms can assess operating conditions and prioritize critical signals, reducing unnecessary data transmission and focusing on key parameters. Machine learning models can be trained to filter out noise and redundant data, ensuring only relevant information is processed and stored. Implementing AI-enabled preprocessing at the edge (on IoT devices or AI processors at the edge) reduces latency and optimizes real-time decision making before sending data to the cloud. Personalization allows the DT to dynamically adjust the sensor sampling rate based on system requirements, increasing efficiency and minimizing bandwidth usage. Instead of continuously collecting data, DTs can be programmed to collect and process signals only when predefined conditions or anomalies occur. Domain experts can define weighting values for different signals, ensuring that the most important parameters drive decision making and predictions. Machine learning models can be trained to extract the most relevant features from raw sensor data, improving signal quality and optimizing compute resources. Configurable anomaly detection settings allow the twin to adjust sensitivity levels based on specific industry needs, preventing false alarms while ensuring system reliability. The system can learn from user feedback and historical data patterns to improve signal selection, preprocessing methods, and overall performance of the DT over time [21; 22; 23].