Secure IoT-Based Health Monitoring with Cloud-Based Machine Learning Analytics

The development and deployment of an efficient and trustworthy Internet of Things-based health monitoring system are presented in this study. The system uses biometric authentication, a PKI framework, TLS, and MQTT for secure communication, as well as identification of users to guarantee data confidentiality. Real-time health data analysis with predictive analytics as well as anomaly detection is made possible by cloud-based machine learning analytics on platforms such as AWS. Evaluation in a range of healthcare environments demonstrates just how flexible and resilient the system is. Despite the achievements, there are still obstacles to overcome, such as problems with the current healthcare infrastructure's interoperability in addition to worries about the scalability of resource-intensive machine learning algorithms. It is imperative that these issues be resolved in order to continue enhancing and enhancing the system and ensuring that it has the capacity to completely transform healthcare procedures.