# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### Comprehensive AIoT Integration in Aviation on the Base of WDAR, FMDE, and G-FMDE Synergy  
The aircraft communication architecture depicted in Figure 6 represents a state-of-the-art implementation of the AIoT in aviation health monitoring systems. This advanced architecture incorporates three key components: the WDAR, the FMDE unit, and the ground-based G-FMDE system. Together, these elements create a comprehensive framework for data collection, transmission, and analyses that exemplifies the potential of AIoT in modern aviation. The architecture consists of three primary systems:  
1. The WDAR, an optional system used to transmit flight data for analysis;
2. The FMDE, another optional unit that combines full aircraft interface device capabilities into a single hardware, software, and connectivity solution;
3. The G-FMDE, a ground-based component that interconnects the external analytical IT system with the airline's analytical system [65]. This tri-part system creates a robust AIoT ecosystem that significantly enhances the depth, breadth, and efficiency of aircraft health monitoring capabilities. The integration of WDAR and FMDE enables the collection of over 20,000 parameters from various aircraft systems. This massive increase in data points represents the \"Internet of Things\" aspect of AIoT, with thousands of sensors continuously reporting their status. Besides the characteristics recalled in Section 3.4, this method includes ensures robust and flexible data transmission, crucial for the effectiveness of the AIoT system. The collected data are transmitted to three interconnected analytical systems:  
1. The airline analytical system handles immediate operational needs and airline-specific analyses. 2. The external analytical IT system provides advanced or specialized analyses, potentially leveraging cloud computing or shared industry resources. 3. The G-FMDE acts as a bridge between the external and airline systems, facilitating seamless data integration and analyses. The inclusion of the G-FMDE is particularly significant as it allows for more efficient data sharing between ground-based systems. This interconnectivity is crucial for realizing the full potential of AIoT, as it enables more comprehensive analyses and the application of advanced AI algorithms across a broader dataset. AIoT advantages in aviation:  
* With over 20,000 parameters being monitored, the system can detect subtle precursors to equipment failure or performance issues with unprecedented accuracy. * The vast amount of data collected, combined with the interconnected analytical systems, allows for more sophisticated AI models, leading to more accurate and earlier predictions of maintenance needs or potential failures. * The integration of ground-based systems through the G-FMDE enables near-real-time analyses of aircraft data, allowing for immediate responses to emerging issues. * With more data points, faster transmission, and integrated analyses, airlines can make more informed decisions about flight operations, maintenance scheduling, and resource allocation. * The comprehensive monitoring and analyses enabled by this system can lead to earlier detection of potential safety issues, allowing for proactive interventions. * The external analytical IT system, facilitated by the G-FMDE, opens possibilities for industry-wide data sharing and collaborative problem-solving. Figure 6: Comprehensive AIoT integration in aviation. The main challenges of the considered architecture are as follows:  
* Handling and securing the transmission of over 20,000 parameters requires robust data management strategies and cybersecurity measures. * The effectiveness of the system still relies on ground-based infrastructure and internet connectivity, which may vary between different airports and regions. * Combining data from multiple systems (WDAR, FMDE, and G-FMDE) and analyzing them across different platforms require careful system integration and data standardization.