# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### 6G Specifications and Gains for AIoT Systems in Aviation  
The advent of 6G technology promises to revolutionize AIoT systems in aviation, offering unprecedented capabilities that will transform aircraft health monitoring, operational efficiency, and safety. 6G is expected to achieve peak data rates of up to 1 Tbps, a 50-fold increase over 5G which will provide the following benefits for aviation:  
* Enable real-time streaming of high-resolution sensor data from multiple aircraft systems simultaneously;
* Support advanced applications such as 8K video streaming for remote visual inspections of aircraft components;
* Facilitate rapid transfer of large datasets, such as engine performance logs or flight recorder data, for immediate analysis. 6G aims to achieve end-to-end latencies of less than 1 ms, a significant improvement over 5G's 1-4 ms which will provide the following benefits for aviation:  
* Enable near-instantaneous communication between aircraft and ground control, crucial for time-critical decisions;
* Support real-time control of unmanned aerial vehicles (UAVs) for inspection and maintenance tasks;
* Facilitate immediate responses to detected anomalies, potentially preventing incidents before they occur. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Properties** & **Traditional Model** & **AIoT-Based Model** \\ \hline Complexity & Simpler models with fewer variables and linear relationships. & Complex, non-linear models with high-dimensional data and multiple interrelated functions. \\ \hline Data Utilization & Limited use of historical data, often relies on current state only. & Extensive use of historical data, real-time sensor data, and contextual information. \\ \hline Predictive Capability & \(F(t)\) is based on simpler models with limited variables. & \(F(t)\) is more accurate due to comprehensive AI analyses \(A(t)\) and consideration of context \(E(t)\). \\ \hline Maintenance Decision-Making & \(g\) is often based on predefined rules or simple thresholds. & \(g^{\prime}\) is a dynamic, AI-driven function that considers multiple factors and can adapt to changing conditions. \\ \hline Cost Optimization & \(h\) is typically a straightforward cost calculation. & \(h\prime\) is more sophisticated, considering performance metrics and potentially long-term impacts. \\ \hline Adaptability & Models are relatively static and require manual updates. & Models can continuously learn and adapt (e.g., \(\psi\) and \(g^{\prime}\) can be updated based on new data). \\ \hline Performance Feedback & Limited or no direct feedback loop for performance improvement. & Includes a performance prediction function \(\omega\) that can inform future decisions. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The main differences of traditional and AIoT-based health monitoring models. 6G is projected to support up to 10 million devices per square kilometer, a ten-fold increase over 5G which will provide the following benefits for aviation:  
* Allow for a dense network of sensors throughout the aircraft, providing granular monitoring of all systems;
* Support the integration of smart materials and nanosensors into aircraft structures for enhanced structural health monitoring;
* Enable comprehensive IoT ecosystems at airports, improving ground operations and maintenance activities. 6G aims for more than 99.999999% reliability [70] and near-100% coverage, including challenging environments like high altitudes and remote areas which will provide the following benefits for aviation:  
* Ensure uninterrupted connectivity for aircraft health monitoring systems, even in remote or oceanic flights;
* Support consistent performance of AloT systems across diverse operational conditions and geographical locations;
* Enhance safety by maintaining constant communication links between aircraft and ground stations.