## 4 Discussion
### Future Research Activities in Study Domain  
The integration of the IoT and AI into aviation health management systems presents vast potential for revolutionizing aircraft maintenance, safety, and operational efficiency. However, realizing this potential requires targeted research efforts to address existing gaps and harness these technologies fully. There are several directions for future research in this field, as follows:  
1. IoT device integration and standardization.  
Research focus: developing standards for the integration of IoT devices in aircraft systems to ensure interoperability, reliability, and data accuracy across different platforms and manufacturers.  
Objective: create a unified framework for IoT device communication, data exchange protocols, and security standards that can be adopted industry-wide.  
2. AI algorithms for predictive maintenance.  
Research focus: designing and refining AI algorithms specifically tailored for predictive maintenance in aviation, capable of processing vast amounts of data from IoT sensors to accurately predict potential failures before they occur.  
Objective: enhance the predictive capabilities of HMGT, reducing unplanned down-time and extending the lifespan of aircraft components.  
3. Real-time data processing and analytics.  
Research focus: investigating efficient methods for the real-time processing and analysis of the massive data streams generated by IoT devices in aircraft, leveraging edge computing and AI techniques.  
Objective: enable immediate, actionable insights directly from data collected in flight, improving decision-making processes for maintenance and operational efficiency.  
4. Cybersecurity for aviation IoT and AI systems.  
Research focus: developing robust cybersecurity measures to protect the integrity and confidentiality of data within HMGT, addressing the unique challenges posed by the integration of the IoT and AI in aviation.  
Objective: ensure that HMGT is secure from cyber threats, preserving the safety and reliability of aviation operations.  
5. Regulatory compliance and certification.  
Research focus: exploring the implications of IoT and AI integration on regulatory compliance and certification processes for aircraft maintenance and operations.  
Objective: provide guidelines and frameworks that align with aviation regulations, facilitating the adoption of IoT and AI technologies in AHMS while ensuring safety and compliance.  
Human-AI interaction in maintenance operations.  
Research focus: studying the dynamics of human-AI interaction within the context of aviation maintenance, focusing on user interfaces, decision-support systems, and the impact of AI recommendations on maintenance practices.  
Objective: optimize the collaboration between human operators and AI systems in HMGT, enhancing the efficiency and effectiveness of maintenance activities.  
Scalability and adaptability of AI models.  
Research focus: investigating methods to ensure the scalability and adaptability of AI models in HMGT, allowing them to evolve with changing aircraft technologies, operational practices, and emerging data patterns.  
Objective: develop AI models that can be easily updated and scaled, maintaining their accuracy and relevance over time.  
Impact of AI and the IoT on aviation sustainability.  
Research focus: assessing how the implementation of AI and the IoT in AHMS can contribute to aviation sustainability efforts, including fuel efficiency, emissions reduction, and resource optimization.  
Objective: identify strategies in which HMGT can support the aviation industry's sustainability goals through more efficient operations and maintenance practices.  
These research directions aim to address the technical, operational, and regulatory challenges associated with implementing the IoT and AI in aviation health management systems. By focusing on these areas, the aviation industry can move closer to realizing the full potential of these technologies, enhancing safety, efficiency, and sustainability.