就地计算、云边协同:传感云与边缘计算的一体化框架(特邀)OA

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.