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Medisanté Medical IoT Platform

Problem Space

Deliver an end-to-end device-to-cloud architecture for medical IoT devices. Handle intermittently connected devices via digital twins ("shadow devices"). Provide real-time access control with fine-grained role-based permissions. Refactor from monolith to event-driven microservices under ISO 62304 Class C requirements. Enable anomaly detection and device state drift monitoring via observability.

Architecture & Patterns

  • End-to-end device-to-cloud architecture with embedded Java + serverless AWS backend
  • Digital twins ("shadow devices") for intermittently connected devices
  • Real-time access control with fine-grained role-based permissions
  • Event-driven microservices refactor from monolith, ISO 62304 Class C
  • Observability via AWS CloudWatch for anomaly detection and device state drift

Tools & Stack

Embedded Java, AWS Lambda, DynamoDB, API Gateway, S3, CloudWatch, JavaScript, MQTT

Business Outcomes

  • Successful monolith-to-microservices refactor under ISO 62304 Class C compliance
  • Real-time access control with fine-grained role-based permissions
  • Anomaly detection and device state drift monitoring via AWS CloudWatch observability

Regulated / Domain Context

ISO 62304 Class C — highest safety class for medical device software

Reusable Narrative Snippets

Designed an end-to-end device-to-cloud architecture with embedded Java and a serverless AWS backend, using digital twins ("shadow devices") to manage intermittently connected medical IoT devices.

Led an event-driven microservices refactor from a monolith under ISO 62304 Class C requirements, with fine-grained role-based access control and AWS CloudWatch observability for anomaly detection and device state drift.

Source Notes

  • Derived from role responsibilities and achievements in config/madu_profile.json; reconciled with JobVia export (madu_alikor_export.json).
  • Confidence: high