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

Abstract

Scalp cooling (SC) systems operate as refrigeration-based therapeutic cyber-physical systems in which deterministic embedded control, closed-loop thermal regulation, and multi-sensor acquisition must satisfy strict real-time performance, reliability, and medical software compliance requirements. Existing embedded architectures for such systems primarily function as isolated controllers that provide local temperature regulation but lack system-level telemetry, condition-based health monitoring, and traceable compliance verification across treatment cycles. This work introduces a modular edge-to-cloud architecture for connected medical refrigeration cyber-physical systems that preserve deterministic embedded control while enabling device observability and fleet-level analytics. The proposed framework integrates heterogeneous sensor acquisition with differentiated sampling cadences, including 3-second synchronous 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 synchronization to AWS cloud infrastructure. In addition, the system enables automated compliance verification against Instructions for Use (IFU) treatment-stage constraints and supports condition-based maintenance analytics derived from thermal performance, coolant quality, and device utilization metrics. The architecture was evaluated across fifteen controlled deployments at two sites under ambient conditions ranging from 14.7-26.0°C. All deployments successfully achieved the operational temperature of −4°C, demonstrating reliable and deterministic thermal control under heterogeneous environmental conditions. The proposed framework provides a scalable retrofit pathway that enables legacy refrigeration-based medical devices to transition into connected, observable, and compliance-verifiable cyber-physical system fleets suitable for large-scale clinical deployment.

Keywords: Internet of Things (IoT), Cyber-Physical Systems, Edge Computing, Medical Device Engineering

DOI: 10.54941/ahfe1008095