Author Identifier (ORCID)
Mainul Islam Chowdhury: https://orcid.org/0009-0004-9780-9777
Quoc Viet Phung: https://orcid.org/0000-0002-6138-3944
Iftekhar Ahmed: https://orcid.org/0000-0003-4441-9631
Walid K. Hasan: https://orcid.org/0009-0000-8012-0827
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Abstract
Designing accurate, reliable, and energy-efficient localization techniques for underwater acoustic networks is highly challenging due to factors such as large propagation delays, the absence of Global Positioning System (GPS), node mobility, and limited acoustic link capacity. In any underwater sensor network (UWSN) monitoring application, data collected by underwater nodes becomes more meaningful when accompanied by location information. However, traditional localization methods often rely on geometric models and statistical filters that are highly sensitive to sensor noise and communication constraints. Energy consumption is another primary concern in UWSNs, not only because replacing and recharging underwater batteries are challenging, but also due to the energy-hungry nature of underwater acoustic communications. To address these challenges, we provide a comprehensive literature review of research contributions on the integration of Artificial Intelligence (AI) and energy efficiency in underwater localization techniques. First, we introduce the recent advancements in AI-based approaches, including deep learning and machine learning models, which are promising for enhancing accuracy, robustness, and adaptability in complex underwater environments through learning-driven techniques. Subsequently, we review various energy-saving strategies integrated into the localization scheme to address the power constraints of underwater sensor nodes. Finally, we discuss future research directions and conclude with key insights.
Document Type
Journal Article
Date of Publication
12-1-2025
Volume
165
Publication Title
Applied Ocean Research
Publisher
Elsevier
School
School of Engineering
Funders
Department of Jobs, Tourism, Science and Innovation in Western Australia
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Chowdhury, M. I., Phung, Q. V., Ahmed, I., Hasan, W. K., & Habibi, D. (2025). Next-generation underwater localization: Artificial Intelligence-based and energy-aware approaches. Applied Ocean Research, 165, 104842. https://doi.org/10.1016/j.apor.2025.104842