[论文解读] Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation

Kenechi G. Omeke, Attai Ibrahim Abubakar+4 more|arXiv (Cornell University)|Mar 8, 2026

Underwater Vehicles and Communication Systems被引用 0

一句话总结

一个全面的教程式综述，详细说明机器学习如何在所有协议层实现物联网在水下通信中的应用，并提供实现指南与未来方向。

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.

研究动机与目标

• Motivate the use of machine learning to tackle the unique challenges of underwater comunicaciones and networks.
• Systematically review ML techniques across the IoUT protocol stack from physical to application layers.
• Provide practical guidelines for implementing ML in resource-constrained underwater platforms.
• Highlight high-impact research directions and a technology roadmap toward 2035 and beyond.

提出的方法

• Present a structured taxonomy of ML techniques (supervised, unsupervised, reinforcement, deep learning) tailored to underwater contexts.
• Layer-by-layer analysis of ML applications across physical, MAC, network, transport, and application layers with illustrative mechanisms.
• Synthesize implementation guidelines addressing data requirements, computational constraints, and deployment strategies.
• Discuss cross-layer optimization, energy efficiency gains, and performance improvements through ML-based designs.
• Identify future directions such as PINNs, federated learning, and transformer architectures for IoUT.

实验结果

研究问题

• RQ1 How can ML techniques be effectively tailored to the unique propagation, energy, and mobility challenges of IoUT?
• RQ2 What are the most impactful ML approaches for each protocol layer in IoUT (physical, MAC, network, transport, application)?
• RQ3 What are the practical implementation challenges and data requirements for deploying ML in resource-constrained underwater platforms?
• RQ4 What future ML directions (e.g., PINNs, federated learning, transformers) hold the most promise for IoUT deployment by 2035?
• RQ5 How do ML-based solutions compare to traditional methods in terms of energy efficiency, throughput, and reliability in IoUT?

主要发现

• ML can yield substantial energy efficiency gains (7–29×) in specific scenarios.
• Cross-layer ML optimization provides around 42% additional performance beyond layer-isolated approaches.
• Transport layer ML approaches can achieve up to 91% packet loss reduction.
• Application-layer ML tasks enable up to 10× data compression and 92% object detection accuracy.
• Long-range improvements include 200–300% throughput gains and significant channel utilization improvements over baselines.