摘要：

针对传统传感云(SC)在处理海量实时感知数据时面临的高时延、带宽瓶颈及隐私泄露等挑战, 边缘计算(EC)通过将计算与存储能力下沉至网络边缘, 成为提升物联网系统效能的关键。本文系统综述了传感云与边缘计算的融合技术与演进范式。首先, 解析了"云-边-端"三层协同架构的演进逻辑, 详述了基于边缘侧的数据预处理、冗余消除与协同存储机制。其次, 重点探讨了智能化资源优化技术, 对比分析了传统启发式算法与深度强化学习(DRL)在动态任务卸载及跨层资源调度中的性能表现。随后, 深入剖析了联邦学习(FL)与边缘智能(EI)在隐私保护与自主决策中的应用, 探讨了分层模型聚合、模型轻量化压缩及基于知识蒸馏的协作推理机制。此外, 本文结合系统工程实践, 阐述了利用Kubernetes容器编排与Kafka消息中间件构建高效、可扩展边缘协作系统的实现路径。最后, 归纳了异构资源管理、网络动态性等共性挑战, 展望了系统向绿色化、语义通信及通感算一体化发展的趋势, 为构建新一代高效物联网感知体系提供了理论参考与工程指导。

关键词: 传感云, 边缘计算, "云-边-端"协同, 边缘智能, 联邦学习

Abstract:

To address the critical challenges of high latency, bandwidth constraints, and privacy vulnerabilities faced by traditional Sensor—Cloud (SC) systems in processing massive amounts of real-time sensing data, Edge Computing (EC) has emerged as a promising solution by extending computational and storage capabilities to the network periphery. This study provides a systematic survey of integration technologies and evolutionary paradigms of SC and EC. First, the evolutionary logic of the "Cloud—Edge—End" collaborative architecture is analyzed, and edge-based data preprocessing, redundancy elimination, and collaborative storage mechanisms are discussed. Second, intelligent resource optimization techniques are investigated by comparing the performance of traditional heuristic algorithms and Deep Reinforcement Learning (DRL) in dynamic task offloading and cross-layer resource scheduling. Furthermore, the application paradigms of Federated Learning (FL) and Edge Intelligence (EI) in privacy preservation and autonomous decision-making are analyzed, focusing on hierarchical model aggregation, lightweight model compression, and collaborative inference based on knowledge distillation. Additionally, by incorporating systems engineering practices, this study elucidates the implementation path for building efficient and scalable edge collaborative systems using Kubernetes container orchestration and Kafka messaging middleware. Finally, common challenges such as heterogeneous resource management and network dynamics are summarized, and future trends toward green computing, semantic communication, and integrated sensing—communication—computation are envisioned, providing theoretical references and engineering guidance for constructing next-generation efficient Internet-of-Things (IoT) sensing systems.

Key words: Sensor—Cloud (SC), Edge Computing (EC), "Cloud—Edge—End" collaboration, Edge Intelligence (EI), Federated Learning (FL)