Near-Source Computing and Cloud-Edge Collaboration: An Integrated Architecture for Sensor Cloud and Edge Computing (Invited)

Centralized computing exhibits diminishing returns under latency, bandwidth, energy, and privacy constraints in large-scale sensing and intelligent applications. Consequently, the architectural focus shifts from an ″everything to the cloud″ to near-source computing combined with cloud—edge collaboration. This paper reviews the stage-specific advantages and limitations of centralization. It characterizes edge computing as a near-data layer situated between endpoints and the cloud that uses local processing and closed-loop control to satisfy deterministic latency and resilience. From this perspective, the paper outlines a sensor-cloud—edge—device collaborative framework. This framework adopts upload-when-necessary data paths, Service Level Agreement (SLA)-aware task placement with two-tier scheduling, and a division of labor in which the edge closes loops instantly while the cloud performs policy and model governance. The paper then discusses the trajectory toward edge intelligence, including lightweight and on-device learning; federated learning and knowledge distillation; AIOps for Edge with multilevel degradation; and an evaluation regime oriented to end-to-end closed-loop efficiency, resilience, and auditability. Evidence from educational scenarios and current industral pratices demonstrate the the practical effectiveness of near-source computing and cloud—edge collaboration in ensuring deterministic latency, enhancing overall system resilience, and achieving cross-domain consistency, and accordingly identify the inevitable evolution of the computing paradigm from edge computing toward cloud—edge intelligent collaboration.