Exploring oceanic depths: unveiling hidden treasures with IoT and ensembled deep hybrid learning model

The fast evolution of underwater and underground Internet of Things (IoT) infrastructures has generated an urgent urgency to have precise, power-efficient, and real-time object detection systems that can be functioning within the extreme seawater conditions. The current solutions are characterized by noise, poor feature discrimination, and high cost of computation. Inspired by recent developments in the machine learning-driven localization, trustworthy routing in underwater wireless sensor networks, and contemporary sonar-based estimation methods, the presented work proposes a new Ensembled Deep Hybrid Learning (EDHL) system that combines the multi-modal IoT sensing with Inception-based deep feature extraction and Gradient Boosting classification. The proposed EDHL model as opposed to conventional CNN or signal-processing-only models integrates multi-scale visual, seismic, thermal, and electromagnetic along with radar features to enhance resistance to turbidity, multipath distortions, and dynamic environmental changes. The results of the experiment show accuracy of 98.39%, low memory usage, and consistent inference time, surpassing state-of-the-art models and complying with the current developments in adaptive filtering and DOA/DOD estimation, as well as lightweight deep detectors. The suggested system offers a future extension of autonomous marine exploration, underwater mapping and intelligent UWSN deployments.