Edge-Centric IoT Health Monitoring: Optimizing Real-Time Responsiveness, Data Privacy, and Energy Efficiency

The innovation of IoT and edge computing has significantly advanced real-time health monitoring systems by providing timely data processing, improved responsiveness, and enhanced data privacy. This study introduces the Edge-Centric IoT Health Monitoring System (EC-HMS), which overcomes limitations observed in cloud-based and hybrid models, including high latency, data privacy vulnerabilities, and excessive energy consumption. EC-HMS leverages localized edge processing to reduce latency, safeguard sensitive data, and optimize energy efficiency, thereby enabling real-time health interventions. The proposed architecture integrates data acquisition, filtering, and anomaly detection at the edge layer, facilitating immediate decision-making and secure data transmission. Experimental evaluations highlight that EC-HMS achieves a 50 ms average latency, consumes 40% less energy compared to cloud-based systems, and ensures robust privacy through localized data handling. This research contributes to the advancement of scalable, responsive, and secure healthcare monitoring frameworks suitable for remote and resource-constrained environments. Future enhancements aim to incorporate predictive analytics and synchronization mechanisms to improve performance and adaptability further.