Cloud-Edge Collaborative Intelligence for Scalable Real-Time Physiological Signal Analytics
Keywords:
cloud-edge computing; physiological signal processing; federated learning; real-time health analytics; data governance; fairnessAbstract
The expanding use of wearable sensors and ambulatory monitoring devices has created new opportunities for continuous physiological signal analysis, but it has also exposed the limits of cloud-only processing models. Streams from electrocardiography, photoplethysmography, electroencephalography, and related modalities require low-latency inference, persistent connectivity awareness, energy efficiency, and strict privacy protection. This paper develops a system-level examination of cloud-edge collaborative intelligence for scalable real-time physiological signal analytics. The discussion focuses on architectural trade-offs across wearable devices, edge gateways, and cloud data centers, with particular attention to local inference, adaptive offloading, federated coordination, longitudinal learning, and operational governance. Rather than presenting a single algorithmic solution, the paper synthesizes structural patterns and deployment risks that shape large-scale health analytics systems. Key concerns include device heterogeneity, motion artifact resilience, communication intermittency, model drift, data sovereignty, regulatory alignment, energy sustainability, fairness, and clinical accountability. The analysis draws on illustrative scenarios from hospital telemetry, remote patient monitoring, and consumer wellness applications to demonstrate how infrastructure choices produce different reliability, privacy, and equity outcomes. The paper argues that sustainable and trustworthy physiological intelligence depends less on isolated model performance than on coherent governance across the entire edge-cloud continuum. Future research directions are identified in context-aware resource orchestration, cross-institutional policy interoperability, auditable streaming model updates, and the integration of fairness constraints into real-time inference pipelines.
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