# Architectural Design for Digital Twin Networks
## 6 Tackling Challenges in DTN Implementation
### Real-Time  
Real-time functionality in DTN operates on two key frontiers. The first is how quickly a DTN can update its internal structure. The second is how efficiently it can generate new configurations when a need for adaptation is detected.  
The process of internal updates largely depends on the monitoring approach used, which is inherently constrained by the type of network being employed. These constraints necessitate trade-offs and careful decision-making. In our case, we utilized a Bluetooth Mesh network, which is heavily constrained by limited data throughput. This raised a critical question: How frequently should the DTN be updated with monitoring data to ensure it remains reliable and trustworthy? To answer this, we researched, in [16], the optimal update frequency to strike a balance between maintaining accurate network representation and minimizing strain on the network. This approach allowed us to define an acceptable frequency that met our needs without overloading the system. However, DTNs designed for different networks, with distinct goals and limitations, would need to undergo a similar evaluation process tailored to their specific context. In networks without size limitations, the primary challenge shifts to scalability, specifically, the ability to process data within a responsive timeframe, even before applying models to it.  
Ethical and privacy considerations are also critical when implementing real-time monitoring, particularly in end-user environments. DTN deployments must ensure compliance with data protection regulations such as the General Data Protection Regulation (GDPR), and adopt appropriate safeguards including anonymization, access control, and user consent mechanisms.  
The second aspect of real-time functionality pertains to the DTN's internal operations--specifically, how quickly it can process data and execute tasks. As previously discussed, a simulator can be slow when attempting highly granular simulations. Such limitations were already observed in a DTN developed for Bluetooth Mesh [16]. This performance is directly influenced by the modeling technique employed. In our case, we initially relied on a simulator due to our team's prior expertise. However, as the DTN evolved through updates and additional research in the context of Bluetooth Mesh networks, the computational speed became a bottleneck for achieving proper real-time support.  
To address this, we explored and tested variations where GNNs took over specific aspects of the simulation. Unfortunately, these trials did not yield the improvements we sought for more advanced solutions. Nevertheless, the current DTN is perfectly suited for its intended purpose, managing a typical Bluetooth Mesh network. This highlights an essential point: When designing for real-time performance, it is crucial to clearly define the operational boundaries upfront. These boundaries should guide the choice of modeling techniques and ensure the implementation aligns with the system's actual requirements.  
Once again, hybrid modeling or multi-modeling is the most compelling solution, allowing the DTN to provide different levels of responsiveness. For instance, a DTN could employ two models: one designed for quick, immediate responses and another for subsequent upgrades or refinements where necessary. Ultimately, focusing on real-time capabilities requires a tailored approach that considers the specific constraints and goals of the network. This ensures the DTN operates effectively within its defined parameters while avoiding unnecessary complexities.