Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation

TL;DR

This paper reviews how machine learning techniques can address the unique challenges of underwater wireless sensor networks, improving performance in localization, communication, routing, and data processing for ocean monitoring.

Contribution

It provides a comprehensive synthesis of ML methodologies tailored for underwater communication, analyzing their algorithmic principles, performance conditions, and practical benefits across network layers.

Findings

ML improves localization and channel estimation accuracy.

Packet loss reduced by up to 91% with ML-based transport mechanisms.

Energy efficiency gains of 7 to 29 times achieved through ML techniques.

Abstract

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 for underwater communication environments. It outlines the algorithmic principles of each paradigm and examines the conditions under which particular approaches deliver superior performance. A layer wise analysis highlights physical layer gains in localisation and channel estimation, MAC layer adaptations that improve channel utilisation, network layer routing strategies that extend operational lifetime, and transport layer mechanisms capable of reducing packet loss by up to 91 percent. At the application layer, ML enables substantial data compression and object detection accuracies reaching 92 percent. Drawing on 300 studies from 2012 to 2025, the survey documents energy efficiency gains of 7 to 29 times, throughput improvements over traditional protocols, and cross layer optimisation benefits of up to 42 percent. It also identifies persistent barriers, including limited datasets, computational constraints, and the gap between theoretical models and real world deployment. The survey concludes with emerging research directions and a technology roadmap supporting ML adoption in operational underwater networks.