Learning to Stay Fresh: A Self-Learning Semantic Framework for Underwater Internet of Things

Abstract

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.