Serverless Edge Computing for Real-Time Processing of Wearable Medical Sensor Data

Abstract

The proliferation of Internet of Things (IoT)-enabled wearable medical devices has generated unprecedented volumes of real-time biosignal data, creating significant challenges for conventional cloud-based processing architectures. Latency, bandwidth consumption, and data privacy remain critical barriers in clinical deployment. This paper proposes a serverless edge computing framework specifically designed for the real-time processing of wearable medical sensor data, including electrocardiogram (ECG), blood oxygen saturation (SpO2), body temperature, and tri-axial accelerometer signals. The proposed architecture leverages AWS Lambda@Edge and Cloudflare Workers deployed at geographically distributed edge nodes to execute lightweight inference models and anomaly detection algorithms within milliseconds of data acquisition. Experimental results demonstrate a mean end-to-end latency of 42 ms an 86.9% improvement over traditional cloud pipelines while maintaining 99.7% data accuracy and reducing bandwidth consumption by 73.2%. The framework also ensures HIPAA-compliant data handling through on device preprocessing and selective data forwarding. This work advances the feasibility of real-time clinical decision support systems powered by wearable technology in resource-constrained environments.