AI 中文总结
针对水下物联网因缺乏标准协议等面临的互操作性挑战，提出自学习语义框架，利用三层架构及语义贝叶斯优化等方法，以信息年龄为新鲜度指标，经实验对比验证其在增强实时环境监测方面的优越性。
AI 中文摘要
新兴的非常规物联网（NC IoT）范式关注信息的有用性而非大量数据收集与传输，将成为下一代无线系统的主导范式。然而，缺乏标准化协议、水下节点的异构性以及声学通信的独特限制，给水下物联网（UIoT）网络带来重大互操作性挑战。本文引入一种开创性的自学习语义框架，通过利用三层架构，采用新颖的语义贝叶斯优化（SBO）方法并结合多传输贝叶斯优化（MTBO）来解决这些问题。该框架将信息年龄（AoI）用作新鲜度指标，纳入混合深度神经网络 - 高斯过程代理模型，并制定传播感知AoI指标以增强实时环境监测。给出了结果并与其他竞争方法比较以量化该方法的优越性。
英文摘要
The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which focuses on the usefulness of information rather than high-volume data collection and transmission, will be a dominant paradigm in the next generation of wireless systems. On the downside, the absence of standardized protocols and the heterogeneity of underwater nodes, coupled with the unique constraints of acoustic communication (e.g., propagation delays and bandwidth limitations), pose significant interoperability challenges for Underwater Internet of Things (UIoT) networks. In this paper, we introduce a pioneering self-learning semantic framework that addresses these issues by leveraging a three-layer architecture using a novel Semantic Bayesian Optimization (SBO) approach integrated with Multi-Transmission Bayesian Optimization (MTBO). This framework employs Age of Information (AoI) as a freshness metric, incorporates a hybrid deep neural network-Gaussian process surrogate model, and formulates a propagation-aware Aol metric to enhance real-time environmental monitoring. Results are provided and compared with other competing methods to quantify the proposed method's superiority.
DOI：10.23919/OCEANS59106.2025.11245169
