AquaIntellect: A Semantic Self-learning Framework for Underwater Internet of Things Connectivity

TL;DR

AquaIntellect introduces a semantic, self-learning framework for underwater IoT that optimizes data relevance, sensor placement, and transmission timing to improve efficiency and freshness of environmental monitoring.

Contribution

It presents a novel semantic-based, self-learning approach with adaptive optimization techniques for underwater IoT connectivity and data management.

Findings

Outperforms conventional methods in simulations

Reduces data redundancy and improves freshness

Optimizes sensor placement and data transmission timing

Abstract

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.