# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Cloud-Edge Deployment Architecture  
The architecture of the DTT system employs a modular cloud-edge hybrid design that supports scalable, latency-sensitive, and secure training delivery. This hybrid deployment model allows the orchestration engine to operate seamlessly across decentralized institutional networks, remote training centers, and mobile learning environments while maintaining transparent auditability and regulatory readiness.  
The overall deployment model of the digital training twin system is summarized in Figure 3, which illustrates the hierarchical relationship between cloud infrastructure, message broker layer, and edge-based delivery nodes.  
In the cloud layer, the orchestration engine operates as the central control logic, continuously evaluating learner profiles, selecting training resources, and generating assignment decisions based on real-time competence state updates.  
At the local training sites, edge nodes manage direct learner interaction and training delivery. Each edge node hosts both CBT modules, which include foundational knowledge instruction, procedural videos, and theoretical assessments, and Simulation Clients, which deliver mid-fidelity diagnostic scenarios and task-specific simulation exercises that operate with lower computational overhead than full XR environments. This design allows stable training continuity even when cloud connectivity is limited, while preserving centralized orchestration control for competence gap evaluation and resource allocation decisions.  
The hybrid cloud-edge deployment model employs Apache Kafka for high-throughput event streaming between orchestration modules, simulation engines, and analytics services, ensuring consistent and scalable data flow. For real-time, low-latency communication between edge devices and the cloud, the system uses the MQTT protocol, secured via TLS encryption to protect learner data. A bi-directional synchronization controller manages updates of competence vectors and xAPI logs between edge nodes and the central database, supporting offline operation and ensuring data consistency across distributed environments.  
The hybrid cloud-edge deployment model was specifically selected to optimize training delivery under real-world operational constraints, such as variable network quality, hardware diversity, and learner mobility. The cloud layer hosts centralized, resource-intensive modules--including reinforcement learning agents, federated aggregation servers, and immersive XR simulations--to ensure high computational efficiency and centralized policy control. In contrast, edge nodes act as lightweight execution clients, deployed on local workstations, tablets, or XR headsets, enabling real-time training continuity even in disconnected or low-bandwidth environments. This distributed setup enables seamless load balancing, latency minimization for learner interactions, and localized data processing for GDPR-compliant privacy. Furthermore, orchestration decisions made in the cloud are propagated to edge nodes through encrypted Kafka and MQTT protocols, ensuring low-latency synchronization while maintaining full traceability through xAPI event logs and competence vector updates. This layered infrastructure ensures robustness, scalability, and regulatory readiness in diverse institutional and field-based aviation training contexts.