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Underwater Wireless Sensing and Wireless Sensor Networks
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Underwater Wireless Sensing and Wireless Sensor Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 12251

Special Issue Editors

Dr. Zhi Sun

E-Mail Website
Guest Editor
Department of Electrical Engineering, University at Buffalo, State University of New York, NY, USA
Interests: wireless communication and networking in extreme environments; wireless physical-layer security; wireless sensing; and cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Miao Pan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA
Interests: AI for/over wireless networking; cybersecurity; underwater communications and networking underwater IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Pu Wang

E-Mail Website
Guest Editor
Department of Computer Science, University of North Carolina at Charlotte, NC, USA
Interests: software defined networking; cyber-physical systems; Internet of Things; wireless sensor networks; cognitive radio networks; and electromagnetic nanonetworks
Dr. Rui Campos

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, 98122 Messina, Italy
Interests: wireless networks; airborne networks; flying networks; maritime networks; underwater networks

Special Issue Information

Dear Colleagues,

Although more than 70 percent of the Earth’s surface is covered by water, our understanding of the underwater world is still very limited due to it being a complicated and harsh environment. Wireless sensing techniques and underwater wireless sensor networks can greatly enhance humans’ cognitive capability under the ocean and lay the foundation for future smart ocean applications. Recently, wireless sensing in terrestrial environments has attracted significant attention from both academia and industry; it is based on various emerging wireless networks, such as millimeter wave wireless networks and Internet of Things (IoT). Meanwhile, recent advances in deep learning have greatly changed the way sensing systems process raw measurements; they now have highly complex and superior sensing capabilities. While terrestrial wireless sensing and sensor network solutions are inspiring the design of underwater wireless sensing, there are numerous unique challenges imposed by the nature of an underwater environment.

This Special Issue aims to report high-quality research in recent advances in underwater wireless sensing and sensor network domains. Topics of interest include but are not limited to those covered by the keyword list below.

Dr. Zhi Sun
Dr. Miao Pan
Dr. Pu Wang
Dr. Rui Campos
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. Sensors is an international peer-reviewed open access semimonthly 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

  • Underwater wireless sensing
  • Detection, localization, and tracking of underwater objects
  • Underwater activity recognition
  • Underwater wireless sensor networks
  • Underwater robotic swarms for wireless sensing
  • Underwater distributed radar systems
  • Model-based and learning-based underwater sensing systems and algorithms
  • Acoustic wave-based techniques for underwater sensing and networking
  • Visible light-based techniques for underwater sensing and networking
  • Magnetic induction-based techniques for underwater sensing and networking
  • Hybrid techniques for underwater sensing and networking.

Published Papers (4 papers)

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Research

25 pages, 23065 KiB  
Article
Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments
by Sizhen Bian, Peter Hevesi, Leif Christensen and Paul Lukowicz
Sensors 2021, 21(6), 2218; https://doi.org/10.3390/s21062218 - 22 Mar 2021
Cited by 10 | Viewed by 3597
Abstract
Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, [...] Read more.
Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration. Full article
(This article belongs to the Special Issue Underwater Wireless Sensing and Wireless Sensor Networks)
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18 pages, 4543 KiB  
Article
An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine
by Xinwei Luo and Yulin Feng
Sensors 2020, 20(18), 5399; https://doi.org/10.3390/s20185399 - 21 Sep 2020
Cited by 20 | Viewed by 2594
Abstract
This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording [...] Read more.
This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method. Full article
(This article belongs to the Special Issue Underwater Wireless Sensing and Wireless Sensor Networks)
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13 pages, 2209 KiB  
Article
A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images
by Jun Liu, Benyuan Li, Wenxue Guan, Shenghua Gong, Jiaxin Liu and Junhong Cui
Sensors 2020, 20(15), 4226; https://doi.org/10.3390/s20154226 - 29 Jul 2020
Cited by 12 | Viewed by 2784
Abstract
Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, [...] Read more.
Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images. Full article
(This article belongs to the Special Issue Underwater Wireless Sensing and Wireless Sensor Networks)
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19 pages, 1107 KiB  
Article
Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks
by Qingyan Ren, Yanjing Sun, Yu Huo, Liang Zhang and Song Li
Sensors 2020, 20(11), 3317; https://doi.org/10.3390/s20113317 - 10 Jun 2020
Cited by 6 | Viewed by 2352
Abstract
In traditional underwater wireless sensor networks (UWSNs), it is difficult to establish reliable communication links as the acoustic wave experiences severe multipath effect, channel fading, and ambient noise. Recently, with the assistance of magnetic induction (MI) technique, cooperative multi-input-multi-output (MIMO) is utilized in [...] Read more.
In traditional underwater wireless sensor networks (UWSNs), it is difficult to establish reliable communication links as the acoustic wave experiences severe multipath effect, channel fading, and ambient noise. Recently, with the assistance of magnetic induction (MI) technique, cooperative multi-input-multi-output (MIMO) is utilized in UWSNs to enable the reliable long range underwater communication. Compared with the acoustic-based UWSNs, the UWSNs adopting MI-assisted acoustic cooperative MIMO are referred to as heterogeneous UWSNs, which are able to significantly improve the effective cover space and network throughput. Due to the complex channel characteristics and the heterogeneous architecture, the connectivity of underwater MI-assisted acoustic cooperative MIMO networks is much more complicated than that of acoustic-based UWSNs. In this paper, a mathematical model is proposed to analyze the connectivity of the networks, which considers the effects of channel characteristics, system parameters, and synchronization errors. The lower and upper bounds of the connectivity probability are also derived, which provide guidelines for the design and deployment of underwater MI-assisted acoustic cooperative MIMO networks. Monte Carlo simulations were performed, and the results validate the accuracy of the proposed model. Full article
(This article belongs to the Special Issue Underwater Wireless Sensing and Wireless Sensor Networks)
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