CLOUD-BASED HEALTHCARE IOT SYSTEM WITH SECURE DATA TRANSMISSION, REAL-TIME ALERTING, AND PERFORMANCE OPTIMIZATION

The rapid integration of Internet of Things (IoT) technology in healthcare has revolutionized patient monitoring by enabling continuous, real-time data collection. This development, however, brings significant challenges related to data security, patient privacy, and system performance. To address these concerns, this work proposes a comprehensive cloud-based healthcare IoT system that ensures secure and efficient data management. The system employs strong encryption methods such as SSL/TLS and Fernet to protect data during transmission between IoT devices and cloud servers, preventing unauthorized access and ensuring confidentiality. To enhance patient safety, real-time anomaly detection is implemented using Deep Neural Networks (DNN), which analyze incoming health data streams to identify critical health events promptly, allowing for timely intervention. Additionally, cloud infrastructure optimization is achieved through dynamic load balancing and auto-scaling, which help maintain system responsiveness and reliability by efficiently managing fluctuating workloads. The framework also incorporates multi-factor authentication and continuous security monitoring, aligning with regulatory standards like HIPAA to safeguard patient information. Experimental results demonstrate significant improvements in data security, system performance, and overall patient outcomes, making this solution well-suited for modern healthcare environments. Looking ahead, future enhancements may include integrating AI-driven predictive analytics and adopting quantum encryption techniques to further strengthen system security and expand its capabilities.