Cloud-Edge Collaborative Computing for Scalable Real-Time Wearable Health Data Analytics
Keywords:
cloud computing; edge computing; wearable health devices; real-time analytics; federated learning; data governance; sustainabilityAbstract
Wearable health devices generate continuous, high-velocity physiological data whose clinical value depends on timely, context-sensitive analysis across heterogeneous sensing environments. Cloud computing offers expansive storage, centralized model training, and interoperability services, while edge computing provides low-latency inference, local data reduction, and improved resilience under intermittent connectivity. This paper examines cloud-edge collaborative computing for scalable real-time wearable health analytics from a systems perspective. It addresses structural trade-offs, resource orchestration, dataflow management, robustness, privacy, fairness, sustainability, and policy implications. The analysis highlights that effective deployment is not only a matter of distributing computational tasks but also requires governance-aware infrastructure, adaptive failure management, and sociotechnical responsiveness. The paper integrates insights from edge intelligence, federated learning, stream processing, and clinical sensing to develop a multidimensional understanding of scalable health analytics. It further considers how cloud-edge architectures can support regulatory compliance, equitable access, and long-term environmental sustainability. A forward-looking discussion identifies research directions in adaptive orchestration, cross-institutional model governance, explainable edge inference, and lifecycle-aware system design. The central argument is that cloud-edge collaboration should be designed as a continuum of coordinated learning and decision processes rather than a static partition between local and remote resources.
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