## 4 Discussion
### Limitations of IoT and AI Implementation in Aviation Health Management Systems  
The study of integrating the IoT and AI into aircraft health monitoring and safety is a rapidly evolving field, facing several limitations and challenges that must be addressed to fully realize its potential. These limitations encompass technological, regulatory, and practical aspects, each influencing the pace and direction of advancements in this area. There are some key limitations of IoT and AI implementation in aviation health management systems:  
* One of the most significant concerns with the IoT and AI in aviation is the security and privacy of the data collected. Aircraft generate vast amounts of data, and ensuringthese data are securely transmitted, stored, and processed is paramount. There is also the challenge of protecting these data against cyberattacks, which could potentially compromise the safety of flight operations. * Integrating new IoT and AI technologies into existing aviation systems poses significant challenges. These include compatibility with legacy systems, ensuring interoperability among diverse systems and components, and standardizing protocols and data formats. The aviation industry operates globally under stringent regulatory standards, making the harmonization of new technologies with existing frameworks a complex and time-consuming process. * The sheer volume of data generated by IoT devices on aircraft is overwhelming. As noted in recent discussions by professional organizations such as Cappemini and AWS, by mid-life, a large passenger aircraft can amass hundreds of thousands of documents, underscoring the critical role of sophisticated data management strategies for lifecycle optimization platforms with additional capabilities for image recognition and analysis of aviation data [76]. By applying complex algorithms and AI, it is possible to predict potential system failures before they occur, thus allowing for timely interventions that can prevent costly downtimes and enhance aircraft performance and safety [77; 78]. * The aviation industry is heavily regulated to ensure the utmost safety and reliability of flight operations. Introducing new technologies like the IoT and AI into aircraft health monitoring systems involves navigating a maze of regulatory approvals and certification processes. These processes can be lengthy and costly, potentially hindering rapid innovation and implementation. * While AI has made significant strides in predictive analytics, there are limitations to the reliability and accuracy of its predictions, particularly in complex environments like aviation. Ensuring that AI algorithms can accurately predict potential issues without generating false positives or missing critical signals is an ongoing challenge. This requires continuous refinement of AI models and validation against real-world scenarios. * The integration of advanced technologies into aviation maintenance and operations also brings human factors into play. There is a need for extensive training for maintenance personnel and flight crews to effectively interact with and interpret the information provided by IoT and AI systems. Balancing the reliance on automated systems with the need for human oversight and decision-making is a nuanced challenge that requires careful consideration. * The cost of implementing and maintaining advanced IoT and AI systems can be prohibitive for some operators, particularly smaller airlines or those in developing regions. Additionally, the infrastructure required to support these technologies, both on aircraft and on the ground, requires significant investment, which can be a limitation for widespread adoption. Despite the promise IoT and AI hold for transforming aircraft health monitoring and enhancing flight safety, these limitations highlight the complexities and challenges that need to be addressed.