A Modular Edge-to-Cloud Architecture for Remote Monitoring and Condition-Based Maintenance in Scalp Cooling Cyber-Physical Systems

Scalp cooling (SC) systems constitute refrigeration-based therapeutic cyber-physical systems in which embedded control, closed-loop thermal regulation, and multi-sensor acquisition must satisfy strict reliability and medical software compliance requirements. Existing embedded architectures for SC devices function as isolated controllers providing local temperature regulation but lack system-level telemetry, condition-based health monitoring, and traceable compliance verification across treatment cycles. This paper presents a modular edge-to-cloud architecture for connected medical refrigeration cyber-physical systems that preserves deterministic embedded control while enabling device observability and fleet-level analytics. The proposed framework integrates heterogeneous sensor acquisition with differentiated sampling cadences: 3-second scheduled paired monitoring of coolant tank temperature and flow rate, event-driven treatment stage capture, and low-frequency coolant pH monitoring. Fault-tolerant local persistence is achieved through an embedded SQLite datastore with periodic batch synchronisation to AWS cloud infrastructure. The architecture also enables automated compliance verification against Instructions for Use (IFU) treatment-stage constraints and condition-based maintenance analytics derived from thermal performance, coolant quality, and device utilisation metrics. The architecture was evaluated across fifteen controlled deployment sessions at two independent sites under ambient conditions ranging from 14.7°C to 26.0°C. All fifteen sessions successfully achieved the target operational temperature of −4°C, confirming that the monitoring layer does not interfere with the original device thermal control. The proposed framework provides a scalable retrofit pathway for legacy refrigeration-based medical devices to transition into connected, observable, and compliance-verifiable cyber-physical system fleets suitable for large-scale clinical deployment.