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Underwater Sensing, Signal Processing and Communications
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Underwater Sensing, Signal Processing and Communications

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 15167

Special Issue Editors

Dr. Syed Agha Hassnain Mohsan

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Guest Editor
Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China
Interests: optical wireless communication; intelligent reflecting surfaces; unmanned aerial vehicles; NOMA; WPT; IoUT; 5G/6G
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nadeem Javaid

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Information Science and Technology, COMSATS University Islamabad, Islamabad 44000, Pakistan
Interests: ad hoc networks; vehicular networks; body area sensor networks; underwater sensor networks; renewable and sustainable energy; energy management; cloud and fog computing; data science; blockchain
Prof. Dr. Chien-Ming Chen

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
Interests: network security; blockchain; big data; AI; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Asghar Khan

E-Mail Website1 Website2
Guest Editor
Hamdard Institute of Engineering & Technology, Islamabad 44000, Pakistan
Interests: IoT; 5G; blockchain; machine learning; UAVs; wireless communication
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxing Li

E-Mail Website
Co-Guest Editor
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: fractal dimension; underwater signal processing; sensor signal processing; denoising; feature extraction; fault diagnosis; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The domain of underwater communication and networking is rapidly expanding due to its vital contributions in various commercial and military applications. Among these, a wide range of activities are real-time underwater sensing, oceanographic data collection, pollution monitoring, offshore exploration, ocean mapping, underwater communication, tactical surveillance, subsea search-and-rescue operations, subsea infrastructure inspection and disaster prevention. To support these applications, conventional underwater acoustic communication inherits several drawbacks, including high propagation delay, limited bandwidth and slow propagation. Moreover, it is severely degraded by multipath propagation, Doppler spread and fading. Recently, advanced sensing, signal processing and communication techniques using optical, electromagnetic and acoustic waves have emerged as promising alternatives to tackle these practical challenges. It is also needed to incorporate notable enabling technologies, such as medium access control (MAC) protocol designs, novel transceiver designs with spatial diversity and innovative modulation schemes to estimate the time-varying underwater channel, low-cost testbed deployments, accurate positioning, high data rate communications and great robustness of large-scale underwater networks.

This Special Issue aims to provide a forum to bring together latest the research innovations from both practitioners and leading researchers from diversified interests to unlock the potential and address breakthrough novelties in underwater sensing, signal processing and communications. The key objective is to highlight ongoing research activities, recent breakthroughs, novel applications and major technical uncertainties related to underwater communication and networking. We invite prospective authors to contribute articles of high-quality scientific research based on both theory and experiments. The key focus of this feature topic is to bridge the gap between theory, practice in design and applications for different underwater technologies. Submissions on simulations, real-time sea trials and testbed applications will be strongly considered for this Special Issue. We seek original research articles, surveys and reviews on the potential topics given, but not limited to:

  • Underwater wireless communications, including magneto-inductive, radio frequency (RF), optical and acoustic waves;
  • Underwater-communication-based channel modeling and signal processing techniques;
  • Artificial intelligence (AI) and machine learning (ML) algorithms for adaptive underwater communications;
  • Multiple-access techniques, such as non-orthogonal multiple access (NOMA);
  • Next-generation underwater wireless sensor networks (UWSNs);
  • Underwater Internet of Things (IoUT);
  • Underwater sensing, monitoring, tracking, navigation, surveillance, positioning and localization;
  • Underwater remote sensing technology;
  • Underwater network and cross-layer protocols;
  • Underwater communications for autonomous underwater vehicles (AUVs).

Dr. Syed Agha Hassnain Mohsan
Prof. Dr. Nadeem Javaid
Dr. Chien-Ming Chen
Dr. Muhammad Asghar Khan
Dr. Yuxing Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UWSN
  • IoUT
  • communication
  • sensing
  • localization
  • channel modeling
  • signal processing

Published Papers (4 papers)

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Research

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16 pages, 4134 KiB  
Article
Research on Feature Extraction of Ship-Radiated Noise Based on Multiscale Fuzzy Dispersion Entropy
by Yuxing Li, Yilan Lou, Lili Liang and Shuai Zhang
J. Mar. Sci. Eng. 2023, 11(5), 997; https://doi.org/10.3390/jmse11050997 - 08 May 2023
Cited by 2 | Viewed by 1406
Abstract
In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important information. In order [...] Read more.
In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important information. In order to overcome this drawback, on the basis of FDE, this paper introduces the concept of multiscale process and proposes multiscale FDE (MFDE), based on which an MFDE-based feature extraction method for ship-radiated noise is proposed. The experimental results of the simulated signals show that MFDE can reflect the changes in signal complexity, frequency, and amplitude, which can be applied in signal feature extraction; in addition, the measured experimental results demonstrate that the MFDE-based feature extraction method has a better feature extraction effect on ship-radiated noise, and the highest recognition rate is 99.5%, which is an improvement of 31.9% compared to the recognition rate of a single time scale. All the results show that MFDE can be better applied to the feature extraction and identification classification of ship-radiated noise. Full article
(This article belongs to the Special Issue Underwater Sensing, Signal Processing and Communications)
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17 pages, 6988 KiB  
Article
Underwater-YCC: Underwater Target Detection Optimization Algorithm Based on YOLOv7
by Xiao Chen, Mujiahui Yuan, Qi Yang, Haiyang Yao and Haiyan Wang
J. Mar. Sci. Eng. 2023, 11(5), 995; https://doi.org/10.3390/jmse11050995 - 07 May 2023
Cited by 9 | Viewed by 2348
Abstract
Underwater target detection using optical images is a challenging yet promising area that has witnessed significant progress. However, fuzzy distortions and irregular light absorption in the underwater environment often lead to image blur and color bias, particularly for small targets. Consequently, existing methods [...] Read more.
Underwater target detection using optical images is a challenging yet promising area that has witnessed significant progress. However, fuzzy distortions and irregular light absorption in the underwater environment often lead to image blur and color bias, particularly for small targets. Consequently, existing methods have yet to yield satisfactory results. To address this issue, we propose the Underwater-YCC optimization algorithm based on You Only Look Once (YOLO) v7 to enhance the accuracy of detecting small targets underwater. Our algorithm utilizes the Convolutional Block Attention Module (CBAM) to obtain fine-grained semantic information by selecting an optimal position through multiple experiments. Furthermore, we employ the Conv2Former as the Neck component of the network for underwater blurred images. Finally, we apply the Wise-IoU, which is effective in improving detection accuracy by assigning multiple weights between high- and low-quality images. Our experiments on the URPC2020 dataset demonstrate that the Underwater-YCC algorithm achieves a mean Average Precision (mAP) of up to 87.16% in complex underwater environments. Full article
(This article belongs to the Special Issue Underwater Sensing, Signal Processing and Communications)
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Review

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32 pages, 5289 KiB  
Review
Recent Advances, Future Trends, Applications and Challenges of Internet of Underwater Things (IoUT): A Comprehensive Review
by Syed Agha Hassnain Mohsan, Yanlong Li, Muhammad Sadiq, Junwei Liang and Muhammad Asghar Khan
J. Mar. Sci. Eng. 2023, 11(1), 124; https://doi.org/10.3390/jmse11010124 - 06 Jan 2023
Cited by 23 | Viewed by 7236
Abstract
Oceans cover more than 70% of the Earth’s surface. For various reasons, almost 95% of these areas remain unexplored. Underwater wireless communication (UWC) has widespread applications, including real-time aquatic data collection, naval surveillance, natural disaster prevention, archaeological expeditions, oil and gas exploration, shipwreck [...] Read more.
Oceans cover more than 70% of the Earth’s surface. For various reasons, almost 95% of these areas remain unexplored. Underwater wireless communication (UWC) has widespread applications, including real-time aquatic data collection, naval surveillance, natural disaster prevention, archaeological expeditions, oil and gas exploration, shipwreck exploration, maritime security, and the monitoring of aquatic species and water contamination. The promising concept of the Internet of Underwater Things (IoUT) is having a great influence in several areas, for example, in small research facilities and average-sized harbors, as well as in huge unexplored areas of ocean. The IoUT has emerged as an innovative technology with the potential to develop a smart ocean. The IoUT framework integrates different underwater communication techniques such as optical, magnetic induction, and acoustic signals. It is capable of revolutionizing industrial projects, scientific research, and business. The key enabler technology for the IoUT is the underwater wireless sensor network (UWSN); however, at present, this is characterized by limitations in reliability, long propagation delays, high energy consumption, a dynamic topology, and limited bandwidth. This study examines the literature to identify potential challenges and risks, as well as mitigating solutions, associated with the IoUT. Our findings reveal that the key contributing elements to the challenges facing the IoUT are underwater communications, energy storage, latency, mobility, a lack of standardization, transmission media, transmission range, and energy constraints. Furthermore, we discuss several IoUT applications while highlighting potential future research directions. Full article
(This article belongs to the Special Issue Underwater Sensing, Signal Processing and Communications)
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20 pages, 6131 KiB  
Review
Deep Learning-Based Classification of Raw Hydroacoustic Signal: A Review
by Xu Lin, Ruichun Dong and Zhichao Lv
J. Mar. Sci. Eng. 2023, 11(1), 3; https://doi.org/10.3390/jmse11010003 - 20 Dec 2022
Cited by 1 | Viewed by 2595
Abstract
Underwater target recognition is a research component that is crucial to realizing crewless underwater detection missions and has significant prospects in both civil and military applications. This paper provides a comprehensive description of the current stage of deep-learning methods with respect to raw [...] Read more.
Underwater target recognition is a research component that is crucial to realizing crewless underwater detection missions and has significant prospects in both civil and military applications. This paper provides a comprehensive description of the current stage of deep-learning methods with respect to raw hydroacoustic data classification, focusing mainly on the variety and recognition of vessels and environmental noise from raw hydroacoustic data. This work not only aims to describe the latest research progress in this field but also summarizes three main elements of the current stage of development: feature extraction in the time and frequency domains, data enhancement by neural networks, and feature classification based on deep learning. In this paper, we analyze and discuss the process of hydroacoustic signal processing; demonstrate that the method of feature fusion can be used in the pre-processing stage in classification and recognition algorithms based on raw hydroacoustic data, which can significantly improve target recognition accuracy; show that data enhancement algorithms can be used to improve the efficiency of recognition in complex environments in terms of deep learning network structure; and further discuss the field’s future development directions. Full article
(This article belongs to the Special Issue Underwater Sensing, Signal Processing and Communications)
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