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Virual Oral Question and Answer
IEEE/ICACT20250032 Question.3
Questioner: benjikone@yahoo.fr    2025-02-18 오전 1:59:06 		IEEE/ICACT20250032 Answer.3
Answer by Auhor mamadouba92@gmail.com   2025-02-18 오전 1:59:06
Your study highlights the advantages of using Fledge for real-time health data management. Given the growing role of AI in predictive healthcare, do you plan to integrate machine learning algorithms directly within the Fog layer to enhance anomaly detection and patient monitoring? What challenges do you foresee in such an integration? Thank you for this relevant question. Indeed, we plan to integrate machine learning algorithms at the Fog layer, in line with the research perspectives outlined in our paper. Implementing ML algorithms at the Fog level will enable immediate anomaly detection on vital data before transmission to the cloud, thus providing a faster response to critical situations. Furthermore, our study shows that Fledge already demonstrates excellent performance in edge data processing. Adding ML capabilities would further optimize this process by filtering relevant data. However, several challenges arise. Fog devices have limited computing resources compared to the cloud, which will require fine-tuned optimization to maintain the real-time performance demonstrated in our results. Monitoring critical parameters such as hypertension also requires a delicate balance between accuracy and processing speed. The integration of these new capabilities must be done while preserving the efficiency of our current microservices architecture. Our experimentation with real sensors provides a solid foundation for this evolution. We are considering a gradual approach, starting with targeted applications such as blood pressure anomaly detection before extending to more complex models. This approach aligns with our vision of a more efficient and patient-centered urban healthcare system, which is particularly crucial in the Senegalese context.

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