# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 2 Materials and Methods  
The overarching goal of this study is to develop and evaluate a comprehensive AIoT system that can enhance aircraft safety, efficiency, and maintenance practices. The different scientific methods for background analyses are categorized into several key areas including the following:  
1. IoT sensors are strategically deployed throughout the aircraft to collect real-time data on various parameters, such as engine performance, structural integrity, and environmental conditions. This empirical data collection process ensures that this study is based on real-world observations and measurements. 2. Methods of data pre-processing aim to enhance data quality, handle missing values, and ensure compatibility with AI algorithms, ultimately improving the reliability and accuracy of the analysis. 3. AI and machine learning methodologies are employed for predictive maintenance tasks. These algorithms can be trained on historical data and sensor readings to learn patterns and make predictions about potential failures and maintenance needs. 4. Before deploying the AIoT system in real-world scenarios, extensive analyses of health monitoring ecosystem should be conducted in simulated environments. This controlled setting will allow for rigorous experimentation and evaluation of the system's performance under various conditions. 5. Comparative analyses are one of the core components of this study. The performance of the AIoT system will be compared to traditional aircraft health monitoring approaches. This comparative analysis provides the opportunity to evaluate the improvements and benefits brought about by the integration of AI and IoT technologies. In the technologically driven world of aviation, the amalgamation of AI and the IoT provides unparalleled opportunities for advancing aircraft health monitoring systems (AHMS). Figure 1 depicts a streamlined process that is central to AIoT, illustrating how sensor data can be transformed through sophisticated machine learning models to provide actionable insights into an aircraft's health status. This paper focuses on the AIoT-driven process that underpins this transformation. The first stage of the process is data acquisition, where diverse sensors installed throughout an aircraft collect a vast array of data points. These sensors form a sensor network that captures everything from engine temperatures and vibrations to fuel efficiency and aerodynamic pressures. In the context of AIoT, the data acquisition process benefits from a highly interconnected network of sensors that communicate in real-time, providing a holistic view of the aircraft's operational status. This IoT infrastructure is the foundation for real-time health monitoring and predictive maintenance strategies. Figure 1: AIoT-based aircraft health management. The raw data from sensors are often noisy and voluminous. Therefore, pre-processing is a critical step where data fusion integrates information from multiple sources, creating a coherent dataset. Data rejection and filtering then remove outliers and irrelevant information, while feature extraction distills the most significant characteristics of the data. AIoT amplifies the potential of pre-processing by using edge computing--where data processing occurs near the data source--to handle these tasks efficiently. By processing data on the aircraft itself, AIoT reduces latency and bandwidth requirements, allowing for faster response times and less strain on the communication networks. The pre-processed data are then fed into diagnostic and prognostic machine learning models. These AI models are trained to identify condition indicators that signify the health of various aircraft systems. Machine learning models' efficiency is based on data: the more data, the better the models can learn and adapt. AIoT provides a seamless flow of high-quality data, ensuring that the AI models are always updated with the latest information. By incorporating historical fault data and trends, the models can predict potential system failures before they occur.