# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### AloT and 6G Convergence as Transformative Paradigm for AHMS
* With 5G, the system could provide near-real-time updates multiple times per second, significantly improving response times to potential issues. * In a 6G environment, the system could theoretically transmit continuous, real-time data streams for all parameters simultaneously, enabling predictive analytics and instantaneous anomaly detection. The improved spectrum efficiency and network energy efficiency of 5G and 6G also have important implications for aviation. Higher spectrum efficiency allows for more data to be transmitted using the same bandwidth, crucial in the congested aviation communication spectrum. The enhanced energy efficiency will be particularly beneficial for onboard systems, potentially extending the operational range of battery-powered sensors and devices. The ultra-low latency of 6G opens up possibilities for advanced applications such as  
* Real-time digital twinning of aircraft systems for enhanced predictive maintenance;
* Instantaneous communication between aircraft and ground control for improved safety protocols;
* Seamless integration of augmented reality for maintenance procedures  
These advancements will be crucial in supporting the increasing complexity of modern aircraft systems and the growing demands for safety, efficiency, and reliability in aviation. The core for this framework (Figure 8) is the AIoT Platform, serving as the nexus of a complex, interconnected system. The AIoT Platform is pivotal, serving as the central hub where data ingestion, processing, and analysis occur. It leverages advanced AI algorithms to interpret complex datasets from diverse sources, enabling intelligent decision-making. By integrating AI with IoT, the platform can execute complex analytical tasks in real-time, providing insights that are both predictive and prescriptive. The proposed AIoT framework for aviation health monitoring is built upon a three-tiered cloud architecture, a widely adopted approach in cloud computing that ensures scalability, flexibility, and efficient data management [69]. This architecture consists of three distinct layers. 1. The presentation tier is the topmost level of the application, responsible for the user interface and user experience. In the context of aviation health monitoring, this tier includes dashboards, alerts, and visualization tools that present processed data and insights to pilots, maintenance crews, and ground staff. It allows users to interact with the system, view real-time aircraft health status, and access predictive maintenance recommendations. 2. The application tier (or logic tier) is the middle layer contains the core computational logic of the system. It processes the data received from the data tier, applies machine learning algorithms and AI models, and generates actionable insights. In our AIoT framework, this tier includes the predictive maintenance algorithms, anomaly detection systems, and decision-support tools that analyze aircraft sensor data to forecast potential issues and optimize maintenance schedules. 3. The data tier is the foundation of the architecture. This layer is responsible for data storage, management, and retrieval. It handles the vast amounts of data generated by aircraft sensors and IoT devices. In the aviation health monitoring context, this tier includes databases storing historical flight data, maintenance records, and real-time sensor readings. It ensures data integrity, facilitates efficient data access, and supports the high-volume, high-velocity data processing required for comprehensive aircraft health monitoring. This three-tiered architecture offers several advantages for AIoT in aviation:  
* Each tier can be scaled independently to meet changing demands, allowing for efficient resource allocation. * New features or technologies can be integrated into specific tiers without overhauling the entire system. * Data can be secured more effectively with clear boundaries between tiers.