ArticleScientific reports2025
Exploration of contemporary modernization in UWSNs in the context of localization including opportunities for future research in machine learning and deep learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A lightweight hybrid framework for real-time data refinement in resource-constrained underwater and underground wireless sensor networks.Scientific reports · 2026Article
- Exploring oceanic depths: unveiling hidden treasures with IoT and ensembled deep hybrid learning model.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The exchange of information in Wireless Sensor Networks (WSNs) across different environments, whether they are above the ground, underground, underwater, or in space has advanced significantly over time. Among these advancements, precise localization of nodes within the network remains a key and vital challenge. In the context of Underwater Wireless Sensor Networks (UWSNs), localization plays a pivotal role in enabling the efficient execution of diverse underwater applications such as environmental monitoring, disaster management, military surveillance and many more. This review article is focusing on three primary aspects, the first section focuses on the fundamentals of localization in UWSNs, providing an in depth and comprehensive discussion on various localization methods. Where we have highlighted the two main categories that are anchor based and anchor free localization along with their respective subcategories. The second section of this article examines the diverse challenges that may emerge during the implementation of the localization process. To enhance clarity and structure, these challenges have been carefully analyzed and categorized into three main groups and that are, (i) Algorithmic challenges, (ii) Technical challenges, and (iii) Environmental challenges. The third section of this article begins by presenting the latest advancements in UWSNs localization, followed by an exploration of how Machine Learning (ML) and Deep Learning (DL) models can contribute in enhancing the localization process. To evaluate the potential benefits of the ML and DL techniques, we have assessed their performance through simulations, focusing on metrics such as localization error, velocity estimation error, Root Mean Square Error (RMSE), and energy consumption. This review also aims to provide actionable insights and a guideline for future research directions and opportunities for practitioners in the field of UWSNs localization. Which will ultimately help in enhancing the performance and reliability of underwater applications by advancing localization techniques and promoting seamless integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.