# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### AloT and 6G Convergence as Transformative Paradigm for AHMS  
The integration of AloT with the burgeoning capabilities of 6G technology is setting a new frontier in the AHMS. This integration not only promises to transform how data are collected and analyzed in aviation but also reshapes the interconnectivity between various stakeholders within the industry. The convergence of these technologies offers unprecedented predictive accuracy, operational efficiency, safety enhancements, and cost reductions. Figure 8 presents a framework that encapsulates the future of aviation health monitoring systems, showcasing the seamless integration of AloT with cutting-edge 6G technology. This advanced ecosystem represents a revolutionary approach to aviation maintenance, operations, and holistic industry management, promising to redefine standards of safety, efficiency, and innovation in the aerospace sector. At the core of this transformative shift is 6G communication technology, characterized by its high data rate, low latency, and enhanced reliability [67]. The 6G channel acts as the backbone for the massive influx of data generated by IoT devices distributed across the aviation ecosystem. This network supports robust, real-time communication between airborne systems and ground operations, facilitating immediate data synchronization and decision-making processes. The evolution of wireless technology from 4G to 5G and the anticipated 6G has significant implications for aviation health monitoring systems. In Table 1 a detailed comparison of data transmission speeds and capabilities across these generations, highlighting the potential improvements in real-time monitoring and rapid responses for aviation systems, is provided. 4G LTE Advanced, while capable of supporting basic health monitoring functions, is limited in its ability to handle the vast amounts of data generated by modern aircraft systems. With peak data rates of 1 Gbps and latencies of 10-30 ms, 4G networks can support periodic data transmissions but may struggle with real-time, high-volume data streams required for comprehensive health monitoring. 5G technology represents a significant leap forward, with peak data rates of up to 20 Gbps and latencies as low as 1-4 ms [68]. This improvement allows for more frequent and detailed data transmissions from aircraft systems. The increased connection density of up to 1 million devices per square kilometer also enables a more extensive network of sensors and IoT devices on and around aircraft. The projected capabilities of 6G technology promise to revolutionize aviation health monitoring. With anticipated peak data rates of 1 Tbps and sub-millisecond latencies, 6G networks will enable true real-time monitoring and near-instantaneous responses to poten \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **4G LTE Advanced** & **5G** & **6G (Projected)** \\ \hline Peak Data Rate & 1 Gbps & 20 Gbps & 1 Tbps \\ Latency & 10–30 ms & 1–4 ms & \textless{1 ms} \\ Connection Density & \(10^{5}\) devices/km\({}^{2}\) & \(10^{6}\) devices/km\({}^{2}\) & \(10^{6}\) devices/km\({}^{2}\) \\ Spectrum Efficiency & 30 bps/Hz & 30–100 bps/Hz & 100–1000 bps/Hz \\ Network Energy Efficiency & \(1\times\) (baseline) & 10–100\(\times\) of 4G & 1000\(\times\) of 4G \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of wireless generations for aviation health monitoring. Figure 8: AIoT and 6G convergence for aviation health monitoring ecosystem integration. tial issues [67]. The massive increase in connection density to 10 million devices per square kilometer will support an unprecedented level of sensor integration and data collection. To illustrate the practical implications of these advancements, consider the following scenario: an aircraft engine monitoring system transmitting data on 1000 parameters. * On a 4G network, this might be limited to updates every few seconds, with potential delays in critical alert transmission.