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

2026-03-08

AI Summary

This study addresses the challenges of severe acoustic attenuation, long propagation delays, energy constraints, and dynamic topology in underwater Internet of Things (IoUT) networks by systematically reviewing and integrating supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques. For the first time, it presents a cross-layer analysis of machine learning applicability and efficacy spanning the physical to application layers. The authors propose a unified cross-layer optimization framework that applies these methods to critical tasks including channel estimation, localization, medium access control, routing, transport control, and data compression. Experimental results demonstrate significant performance improvements: a 91% reduction in packet loss at the transport layer, 92% accuracy in application-layer object detection, 7–29× enhancement in energy efficiency, and a 42% gain in throughput attributable to cross-layer optimization.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Machine learning and data science for the Web

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.

Problem

• Internet of Underwater Things
• acoustic attenuation
• propagation delay
• energy constraints
• dynamic topology

Innovation

• Machine Learning
• Internet of Underwater Things
• Cross-layer Optimization
• Underwater Acoustic Communication
• Energy Efficiency