A Scalable Serverless Framework for Real-Time Analytics of Remote Patient Monitoring IoT Devices

The exponential growth of IoT-equipped remote patient monitoring (RPM) systems has generated enormous amounts of real-time health data with immense challenges related to latency, scalability, and cost of operation. Conventional cloud-based analytical platforms tend to be plagued with high overheads, sluggish response times, and inflexibility, rendering them inappropriate for critical healthcare applications. This research puts forward (SHEAF) Serverless Health Edge Analytics Framework, a light, scalable, and affordable Serverless architecture that is tailored for real-time data processing and analysis of health information from RPM IoT devices. The architecture uses MQTT for communication at the device level, Kafka for high-volume data ingestion, and AWS Lambda as the Serverless compute engine to analyze data streams in real time. On-site filtering and simple anomaly detection are done by an optional TinyML module embedded on edge devices, reducing transmission burden. Predictive analytics with light LSTM models are embedded in Serverless functions to identify anomalies and trigger alerts. Based on simulated trials with synthetic patient data, they exhibited a reduction of 40–60% in latency, 92–95% correct prediction of the occurrence of important events, and a 30% reduction of bandwidth consumption versus standard cloud systems. Moreover, the system held consistent performance using more than 1000 connections simultaneously on a device, confirming good scalability. Its Serverless core presents a low-latency, low-cost, and high-performance proposition against conventional frameworks, making it particularly well suited for under provisioned or rural health networks.