Secure IoT Architecture for Predictive Maintenance of Medical Diagnostic Devices: From Edge to Cloud

Abstract

This article presents a comprehensive framework for implementing real-time fleet monitoring with predictive maintenance capabilities for medical diagnostic devices. The proposed architecture leverages cloud-based IoT platforms to capture telemetry data from distributed over-the-counter diagnostic devices, employing secure MQTT and HTTPS protocols with Zero Trust security principles to ensure HIPAA compliance. The system utilizes sophisticated data pipelines for ingestion, processing, and storage, while machine learning models analyze both historical and real-time data to predict potential device failures before they occur. Feature engineering techniques transform raw telemetry into meaningful predictive indicators, while specialized model training methodologies address the inherent challenges of medical device failure prediction. The implementation demonstrates significant operational improvements, including reduced downtime, accelerated support workflows through automated ticketing, and enhanced decision support through real-time dashboard visualizations. This article explores the technical architecture, predictive model development, operational impact, and future research directions for IoT-enabled predictive maintenance in regulated medical environments.