Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation
The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered by severe acoustic attenuation, propagation delays far exceeding those of terrestrial wireless systems, strict energy constraints, and dynamic topologies shaped by ocean currents. Machine learning (ML) has emerged as a key enabler for addressing these limitations, offering data driven mechanisms that enhance performance across all layers of underwater wireless sensor networks. This tutorial survey synthesises ML methodologies supervised, unsupervised, reinforcement, and deep learning specifically contextualised f...
| Authors | Kenechi Omeke, Attai Abubakar, Michael Mollel et al. |
|---|---|
| Primary category | eess.SY |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.