Abstract
This chapter explores the integration of digital twin technology (DTT) and artificial intelligence (AI) in advancing underwater wireless sensor networks (UWSN). The problem statement revolves around the challenges faced by UWSN in terms of data quality, real-time decision-making, and energy efficiency. Traditional UWSN systems lack the ability to adapt swiftly to changing underwater conditions and ensure reliable data transmission. This study addresses these challenges by proposing a novel approach that leverages DTT and AI for enhanced UWSN performance. Its methodology involves the design and implementation of a DTT-AI-based UWSN framework. DTT replicates the physical underwater environment, providing a virtual representation that continuously updates in real-time. AI algorithms process data from UWSN sensors within this digital twin, enabling intelligent decision-making and predictive analytics.
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