AquaIntellect：一种面向水下物联网连接的语义自学习框架
AquaIntellect: A Semantic Self-learning Framework for Underwater Internet of Things Connectivity
AI总结：
针对水下非传统物联网连接挑战，提出 AquaIntellect 语义自学习框架，通过语义消息选择、AoI 优化、DNN 贝叶斯传感器布置及 AEI 到达率预测提升传输效率与时效性。
摘要：
新兴的非传统物联网（Non-Conventional Internet of Things, NC IoT）范式关注信息的有用性，而非海量数据采集与传输的理念，在不久的将来将成为人类生活中重要且占主导地位的组成部分。本文提出一种新颖的基于语义的方法，以应对水下 NC IoT 带来的独特挑战。我们提出一种智能感知策略，通过审慎选择待传输数据来挖掘水下环境的语义，从而最大限度减少冗余，实现最相关数据的传输。我们引入一种进化函数，用于在最小信息年龄（Age of Information, AoI，即所采集信息的新鲜度度量）内选择与特定任务相关的语义赋能消息，并监测水下环境以优化性能。一种融合自适应代理模型优化的 DNN 赋能贝叶斯方法将确定传感器的最优布置策略以及水下地貌的不确定性水平。文中还引入自适应期望改进（Adaptive Expected Improvement, AEI）机制，用于预测最优到达率，以实现同步的数据感知与传输生态系统，确保效率和时效性。仿真结果表明，所提出的解决方案优于传统方法。
英文摘要：
The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which is focused on the usefulness of information as opposed to the notion of high volume data collection and transmission, will be an important and dominant part of human life in the near future. This paper proposes a novel semantic-based approach for addressing the unique challenges posed by underwater NC IoT. We present an intelligent sensing strategy for exploring the semantics of the underwater environment by judiciously selecting the data to transmit, thereby minimizing redundancy for utmost relevant data transmission. We introduce an evolutionary function for the selection of the semantic-empowered messages relevant to the specific task within a minimum Age of Information (AoI), a freshness metric of the collected information, and for monitoring the underwater environment for performance optimization. A DNN-empowered Bayesian integrated with an adaptive surrogate model optimization will determine the optimal placement strategy of the sensors and the uncertainty level of the underwater landscape. An Adaptive Expected Improvement (AEI) mechanism is introduced to predict the optimal arrival rate for enabling a synchronized data sensing and transmission ecosystem, ensuring efficiency and timeliness. Simulation results show that the proposed solution outperforms conventional approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
