# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### AloT-Enabled Aircraft Communication Architecture: Enhancing Health Monitoring through WDAR  
The aircraft communication architecture depicted in Figure 4 illustrates a sophisticated network of data and voice communication channels that form the backbone of modern aviation systems. This architecture, particularly with the inclusion of the WDAR option, represents a significant step towards realizing the full potential of the AloT in aviation health monitoring. At the heart of this architecture is the WDAR, an optional system designed to transmit flight data for analysis. The WDAR serves as a crucial IoT component, collecting approximately 400 parameters from various aircraft systems. This extensive data collection forms the foundation for advanced health monitoring and predictive maintenance capabilities that are central to the AloT paradigm in aviation. The communication infrastructure supporting the WDAR is multi-faceted, ensuring robust and flexible data transmission:  
1. 2G/3G channel [60]. When the aircraft is on the ground, the WDAR utilizes a 2G/3G connection for data transmission. While this is a low-speed connection, it is sufficient for transferring the collected parameters to the airline's analytical system. The effectiveness of this channel depends on the airport's location and infrastructure, highlighting the importance of ground-based IoT networks in the overall AloT ecosystem. 2. Very-high-frequency (VHF) radio channel [61]. This channel supports both data and voice communication, providing a versatile link between the aircraft and ground stations. In the context of AloT, the data transmission capabilities of the VHF channel could be leveraged for real-time or near-real-time health monitoring updates during flight. 3. High-frequency (HF) radio channel [61]. Although limited to voice communication, the HF channel ensures long-range communication capabilities, which is crucial for maintaining connectivity in remote areas where VHF and cellular networks are unavailable. Figure 4: AloT-enabled aircraft communication architecture. The integration of these communication channels with the WDAR system exemplifies the IoT aspect of AIoT, creating a comprehensive data collection and transmission network. This network enables the continuous flow of information from the aircraft to the airline's analytical system, where AI algorithms can process and analyze the data. The airline analytical system, as the recipient of the WDAR data, represents the AI component of the AIoT framework. Here, machine learning algorithms can be applied to the vast amount of data collected by the WDAR to identify patterns, predict potential issues, and optimize maintenance schedules. This integration of real-time data collection with advanced analytics embodies the essence of AIoT in aviation health monitoring. The benefits of this AIoT-enabled architecture are numerous:  
* With access to 400 parameters [62], AI algorithms can develop highly accurate predictive models, allowing airlines to anticipate and address potential issues before they lead to failures or delays. * Real-time data transmission enables airlines to make informed decisions about flight operations, potentially reducing fuel consumption and optimizing flight paths. * Continuous monitoring of critical systems through the WDAR enhances overall flight safety by enabling rapid responses to any anomalies detected during flight. * By optimizing maintenance schedules and reducing unplanned downtime, airlines can significantly reduce operational costs. * The wealth of data provided by the WDAR, when analyzed using AI, can inform strategic decisions about fleet management, training programs, and long-term operational planning. However, the implementation of such an AIoT system is not without challenges. The reliance on ground-based infrastructure for data transmission when the aircraft is on the ground may limit the system's effectiveness in areas with poor cellular coverage.