  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.
