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

doi:10.19678/j.issn.1000-3428.0253308

Abstract

Abstract:

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.

Key words: cloud—edge collaboration, sensor cloud, edge intelligence, near-source computing, small—large model collaboration

摘要：

面向大规模感知与智能应用场景, 集中式计算在时延、带宽、能耗与隐私保护的多重约束下逐渐呈现边际效益递减, 计算范式因此由单一的"万物上云"模式, 逐步转向"就地计算与云边协同"的新形态。在此背景下, 本文首先梳理集中化计算路径在不同发展阶段所具备的优势及其适用边界, 进而界定边缘计算在端-云之间所扮演的关键角色。在此基础上, 进一步概述"传感云-边缘-端"协同计算框架, 重点分析其中的核心机制, 包括数据"必要即上行"的传输原则、面向服务级别协议(SLA)感知的任务分配与双层调度策略, 以及边侧即时闭环执行与云侧全局策略治理之间的分工与协同关系。随着计算与智能能力向边缘侧持续下沉, 本文进一步讨论边缘智能的发展方向, 涵盖模型轻量化与本地学习机制、联邦学习与知识蒸馏的协同范式, 以及面向边缘环境的智能运维(AIOps for Edge)与多级降级机制所支撑的自治能力。同时, 强调构建以端到端闭环效率、系统韧性与可追责性为导向的综合评价体系的重要性。最后, 结合教育等典型应用场景以及产业实践, 论证就地计算与云边协同在保障确定性时延、提升系统整体韧性以及实现跨域一致性方面的现实有效性, 并据此指出计算范式由边缘计算向云边智能协同演进的必然趋势与发展方向。

关键词: 云边协同, 传感云, 边缘智能, 就地计算, 大小模型协同

WANG Tian, LI Yuting, WANG Wenhua. Near-Source Computing and Cloud-Edge Collaboration: An Integrated Architecture for Sensor Cloud and Edge Computing (Invited)[J]. Computer Engineering, 2026, 52(2): 7-12.

王田, 李雨婷, 王文华. 就地计算、云边协同: 传感云与边缘计算的一体化框架(特邀)[J]. 计算机工程, 2026, 52(2): 7-12.

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