Deep Learning-Enabled Hybrid Sensor Network for Intelligent Underwater Monitoring

The increasing prevalence of marine pollution and the escalating impacts of climate change have intensified the need for advanced underwater monitoring systems capable of operating reliably in harsh subsea environments. However, underwater environmental monitoring remains a challenging task due to factors such as high hydrostatic pressure, limited visibility, dynamic water conditions, and the inherent constraints of underwater communication channels. To address these challenges, this study proposes a Hybrid Underwater Wireless Sensor Network (UHWSN) that integrates acoustic and optical sensing modalities with advanced deep learning techniques to enable efficient underwater debris detection, tracking, and classification. The proposed framework combines mean-shift-based object tracking, lossless arithmetic coding, Convolutional Neural Networks (CNNs), and Deep Belief Networks (DBNs) within a hierarchical processing architecture. At the sensor-node level, mean-shift tracking is employed for region-of-interest (ROI) localization, while arithmetic coding provides efficient lossless data compression, achieving a data-volume reduction of approximately 55–60