AI 中文总结
本文提出SAGSIN中基于多层网络架构（MLNA）和多级信息处理（MLIP）的边缘计算范式，通过分层精炼数据降低延迟与能耗，延长网络寿命，并以海上监测案例验证其增益。
AI 中文摘要
海事物联网（IoT）部署日益依赖空天地海一体化网络（SAGSIN）将水下传感器与地面及空间骨干网连接起来。然而，该路径上的异构链路，从带宽受限且能耗高的水下声学信道到海面以上的高容量光纤链路，使得海量原始传感数据的传输在延迟和能量方面代价高昂，并且难以维持无人值守、电池供电节点的持续运行。本文提出了一种基于两个耦合思想的SAGSIN边缘计算范式：多层网络架构（MLNA）组织水下、水面、空中和地面/空间层级，以及多级信息处理（MLIP）在数据沿网络上行时，逐步将原始测量数据精炼为紧凑的事件级表示。我们刻画了每一层级的计算、传输和存储能量，并展示了跨层分配精炼如何以本地处理成本换取传输和存储的节省。随后，我们讨论了MLNA-MLIP如何降低延迟、增强数据隐私、提高服务可靠性，并管理能量以延长网络寿命。一项关于海上监测的案例研究量化了由此产生的寿命增益，并确定了最优处理深度。最后概述了开放挑战和未来方向。
英文摘要
Maritime Internet of Things (IoT) deployments increasingly rely on the space-air-ground-sea integrated network (SAGSIN) to connect underwater sensors with terrestrial and space backbones. However, the heterogeneous links along this path, ranging from bandwidth-limited and energy-hungry underwater acoustic channels to high-capacity optical links above the sea, make the transport of massive raw sensing data costly in both latency and energy, and difficult to sustain for unattended, battery-powered nodes. This article presents an edge-computing paradigm for SAGSIN built on two coupled ideas: a Multi-Layer Network Architecture (MLNA) that organizes the underwater, surface, aerial, and ground/space tiers, and Multi-Level Information Processing (MLIP) that progressively refines data from raw measurements toward compact, event-level representations as they ascend the network. We characterize the computation, transmission, and storage energy at each tier and show how distributing refinement across layers trades local processing cost against transmission and storage savings. We then discuss how MLNA-MLIP reduces latency, strengthens data privacy, improves service reliability, and manages energy to prolong network lifetime. A case study on offshore monitoring quantifies the resulting lifetime gains and identifies the optimal processing depth. Open challenges and future directions are outlined.
