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New Challenges in Autonomous Underwater Networks

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 26464

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Special Issue Editors

Dr. Nils Morozs

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Guest Editor

Department of Electronic Engineering, University of York, Heslington, York YO10 5DD, UK
Interests: underwater acoustic communications; underwater networks; network protocols; internet of underwater things
Special Issues, Collections and Topics in MDPI journals

Dr. Filippo Campagnaro

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Guest Editor

Department of Information Engineering, University of Padova, Via Gradenigo 6/a, 35131 Padova, Italy
Interests: underwater acoustic; optical networks; underwater multimodal communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are cordially invited to submit your research articles for publication in this Special Issue of JMSE titled "New Challenges in Autonomous Underwater Networks".

The ocean covers 70% of the surface of the Earth and plays a key role in supporting all living organisms on our planet, yet the vast majority of the ocean remains unmapped and unexplored. Current advancements in autonomous underwater vehicle (AUV) and acoustic modem technologies will make the development of large-scale autonomous underwater networks feasible in the near future. However, this will involve significant technological challenges in wireless communication underwater (multimodal—acoustic, radio, optical), AUV design, sonar imaging, localization and navigation in the absence of GPS/GNSS, and many others.

This Special Issue focuses on studying the challenges involved in develo** future autonomous underwater networks. This includes, but is not limited to, the following topics:

Underwater network protocols;
Underwater localization, navigation and tracking;
Design of underwater sensors and sensor networks;
Underwater acoustic communication techniques;
Underwater multimodal communication techniques and protocols;
Devices, techniques and algorithms for underwater imaging;
Novel signal processing techniques for underwater communication and imaging;
Applications of AI/ML in autonomous underwater networks;
AUV/ROV design;
Results from sea trials.

We look forward to receiving your original research contributions, including review articles, to bring together the latest developments in this area within this Special Issue of JMSE.

Dr. Nils Morozs
Dr. Filippo Campagnaro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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

underwater networks
acoustic underwater communication
network protocols
AUV
autonomous networks

Published Papers (9 papers)

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Research

19 pages, 5585 KiB

Open AccessArticle

VGAE-AMF: A Novel Topology Reconstruction Algorithm for Invulnerability of Ocean Wireless Sensor Networks Based on Graph Neural Network

by Ying Zhang, Qi Zhang, Yu Zhang and Zhiyuan Zhu

J. Mar. Sci. Eng. 2023, 11(4), 843; https://doi.org/10.3390/jmse11040843 - 16 Apr 2023

Cited by 3 | Viewed by 1835

Abstract

Ocean wireless sensor networks (OWSNs) play an important role in marine environment monitoring, underwater target tracking, and marine defense. OWSNs not only monitor the surface information in real time but also act as an important relay layer for underwater sensor networks to establish data communication between underwater sensors and ship-based base stations, land-based base stations, and satellites. The destructive resistance of OWSNs is closely related to the marine environment where they are located. Affected by the dynamics of seawater, the location of nodes is extremely easy to shift, resulting in the deterioration of the connectivity of the OWSNs and the instability of the network topology. In this paper, a novel topology optimization model of OWSNs based on the idea of link prediction by cascading variational graph auto-encoders and adaptive multilayer filter (VGAE-AMF) was proposed, which attenuates the extent of damage after the network is attacked, extracts the global features of OWSNs by graph convolutional network (GCN) to obtain the graph embedding vector of the network so as to decode and generate a new topology, and finally, an adaptive multilayer filter (AMF) is used to achieve topology control at the node level. Simulation experiment results show that the robustness index of the optimized network is improved by 39.65% and has good invulnerability to both random and deliberate attacks. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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25 pages, 8266 KiB

Open AccessEditor’s ChoiceArticle

Survey on Low-Cost Underwater Sensor Networks: From Niche Applications to Everyday Use

by Filippo Campagnaro, Fabian Steinmetz and Bernd-Christian Renner

J. Mar. Sci. Eng. 2023, 11(1), 125; https://doi.org/10.3390/jmse11010125 - 6 Jan 2023

Cited by 16 | Viewed by 4864

Abstract

Traditionally, underwater acoustic modems and positioning systems were developed for military and Oil & Gas industries, that require deep water deployments and extremely reliable systems, focusing on high power expensive systems and leaving the use of low-cost devices only attractive for academic studies. Conversely, recent developments of low-cost unmanned vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), suitable for shallow water coastal missions, and the need of sensors network deployments for measuring water quality and studying the effect of climate change in coastal areas, called to the need of low-cost and low-power acoustic modems and positioning systems that are gaining more and more momentum to date. The use of these devices can enable a wide set of applications, often based on low-cost AUV swarm formations, where an acoustic link between the vehicles is required to coordinate the mission, perform the maneuvers, and maintain the formation along the time. Moreover, they can make environmental wireless sensor deployment cost effective by substituting wired systems. Underwater positioning systems, usually used in large-scale operations, can be finally applied to small-scale application thanks to the reduction in costs, at the price of a lower transmission and positioning range and precision. While in open-sea application this performance reduction is a huge limitation, in river, lagoon, port and lake deployments this is not an issue, given that the extremely shallow water and the presence of many obstacles would deteriorate the acoustic signal anyway, not allowing long range transmissions even with expensive and sophisticated acoustic devices. In this paper, we review the recent developments of low-cost and low-power acoustic communication and positioning systems, both analyzing University prototypes and new commercial devices available in the market, identifying advantages and limitations of these devices, and we describe potential new applications that can be enabled by these systems. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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20 pages, 1531 KiB

Open AccessArticle

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

by Yuzhi Zhang, Shumin Zhang, Bin Wang, Yang Liu, Weigang Bai and **aohong Shen

J. Mar. Sci. Eng. 2022, 10(12), 1920; https://doi.org/10.3390/jmse10121920 - 6 Dec 2022

Cited by 9 | Viewed by 2601

Abstract

Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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34 pages, 7496 KiB

Open AccessArticle

Data Gathering in UWA Sensor Networks: Practical Considerations and Lessons from Sea Trials

by Nils Morozs, Benjamin Sherlock, Benjamin T. Henson, Jeffrey A. Neasham, Paul D. Mitchell and Yuriy Zakharov

J. Mar. Sci. Eng. 2022, 10(9), 1268; https://doi.org/10.3390/jmse10091268 - 8 Sep 2022

Cited by 6 | Viewed by 1888

Abstract

Underwater acoustic (UWA) network protocol design is a challenging task due to several factors, such as slow propagation of acoustic waves, low frequency bandwidth and high bit error and frame error rates often encountered in real UWA environments. In this paper, we consider the design of a robust and scalable data gathering protocol for UWA sensor networks (UASNs), focusing on practical considerations and lessons learnt from multiple lake and sea trials. A cross-layer protocol is presented that integrates a network discovery process, intelligent routing, scheduling via Transmit Delay Allocation MAC (TDA-MAC) and multi-node Automatic Repeat Request (ARQ), to facilitate reliable data gathering in practical UASN deployments. Furthermore, this paper presents the details of a novel experimental testbed and underwater sensor node prototype that were used for the trials reported in this study. Based on the results of the trials, important conclusions are drawn on the protocol features required to achieve reliable networked communication in realistic UWA environments. The insights gained from the trials are valuable both for further development of the proposed data gathering protocol, and for the wider UWA networking research community concerned with develo** practical solutions for real-world UASN deployments. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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21 pages, 6411 KiB

Open AccessArticle

Classification of Underwater Fish Images and Videos via Very Small Convolutional Neural Networks

by Marius Paraschiv, Ricardo Padrino, Paolo Casari, Eyal Bigal, Aviad Scheinin, Dan Tchernov and Antonio Fernández Anta

J. Mar. Sci. Eng. 2022, 10(6), 736; https://doi.org/10.3390/jmse10060736 - 27 May 2022

Cited by 10 | Viewed by 3479

Abstract

The automatic classification of fish species appearing in images and videos from underwater cameras is a challenging task, albeit one with a large potential impact in environment conservation, marine fauna health assessment, and fishing policy. Deep neural network models, such as convolutional neural networks, are a popular solution to image recognition problems. However, such models typically require very large datasets to train millions of model parameters. Because underwater fish image and video datasets are scarce, non-uniform, and often extremely unbalanced, deep neural networks may be inadequately trained, and undergo a much larger risk of overfitting. In this paper, we propose small convolutional neural networks as a practical engineering solution that helps tackle fish image classification. The concept of “small” refers to the number of parameters of the resulting models: smaller models are lighter to run on low-power devices, and drain fewer resources per execution. This is especially relevant for fish recognition systems that run unattended on offshore platforms, often on embedded hardware. Here, established deep neural network models would require too many computational resources. We show that even networks with little more than 12,000 parameters provide an acceptable working degree of accuracy in the classification task (almost 42% for six fish species), even when trained on small and unbalanced datasets. If the fish images come from videos, we augment the data via a low-complexity object tracking algorithm, increasing the accuracy to almost 49% for six fish species. We tested the networks with images obtained from the deployments of an experimental system in the Mediterranean sea, showing a good level of accuracy given the low quality of the dataset. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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19 pages, 4424 KiB

Open AccessArticle

Back Projection Algorithm for Multi-Receiver Synthetic Aperture Sonar Based on Two Interpolators

by Xuebo Zhang and Peixuan Yang

J. Mar. Sci. Eng. 2022, 10(6), 718; https://doi.org/10.3390/jmse10060718 - 24 May 2022

Cited by 14 | Viewed by 2012

Abstract

The back projection (BP) algorithm is characterized by its high performance for multi-receiver synthetic aperture sonar (SAS). For this reason, it is usually used to evaluate the imaging performance of Fourier-domain methods. However, this algorithm suffers from a large computation load, and the imaging efficiency is seriously lowered. In order to improve the imaging performance, this paper proposes focusing the multi-receiver SAS data using the BP algorithm based on two interpolators, including the linear interpolation and nearest-neighbor interpolation. The former interpolation is used to decrease the interpolation error based on adjacent sampled data; the latter estimates the data at the desired moment by assigning the data value of the nearest sample as estimated data. Then, the imaging performance of the presented method is discussed in detail based on simulations and real-data processing. With the presented method, the imaging performance can be improved without a loss of efficiency compared to nearest-neighbor interpolation without an upsampling operation. In comparison with the traditional BP algorithm, the presented method can be used to improve the imaging efficiency without any loss of performance. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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12 pages, 2859 KiB

Open AccessArticle

Design of a Broadband Cavity Baffle Bender Transducer

by Zhiwei Zhao, **qiu Wu, **aofei Qi, Gang Qiao, Wenbo Zhang, Chaofan Zhang and Kang Guo

J. Mar. Sci. Eng. 2022, 10(5), 680; https://doi.org/10.3390/jmse10050680 - 16 May 2022

Cited by 3 | Viewed by 2108

Abstract

As low-frequency and broadband acoustic emission capability is beneficial to the detection range and acoustic communication speed of small scale autonomous underwater vehicles (AUV), this type of transducer is required, especially in cases of complex acoustic environments. A broadband bender transducer with cavity baffle that suits small scale AUV is proposed. Rather than additional benders, a passive cavity baffle, which would be capable of providing mutual radiation and a fluid cavity mode, is introduced to a single bender. The bending resonant frequency is reduced by the mutual radiation between the bender and the cavity baffle, the cavity baffle extends the lower limit of the available frequency band of the transducer, the liquid resonant frequency behind the former expands the higher limit, then the cavity baffle bender transducer fills the role of radiating low-frequency and broadband emissions through multimode coupling. The finite element method is used to analyze the acoustic performance of the transducer under different baffle conditions. Then, a prototype of the broadband cavity baffle bender transducer is developed according to the optimized parameters of simulation. The acoustic parameters of the prototype were measured in an anechoic pool. The resonant frequency measured in water of the bender itself is 3 kHz, and the −3dB bandwidth is 560 Hz. The prototype test results show that the cavity baffle scheme can improve the −3dB bandwidth of the bender from 560 Hz to 1000 Hz, which fundamentally realizes the expectations of the prototype design. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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19 pages, 4916 KiB

Open AccessArticle

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

by Yubing Mao, Farong Gao, Qizhong Zhang and Zhangyi Yang

J. Mar. Sci. Eng. 2022, 10(3), 383; https://doi.org/10.3390/jmse10030383 - 7 Mar 2022

Cited by 15 | Viewed by 3254

Abstract

This study aims to solve the problem of sparse reward and local convergence when using a reinforcement learning algorithm as the controller of an AUV. Based on the generative adversarial imitation (GAIL) algorithm combined with a multi-agent, a multi-agent GAIL (MAG) algorithm is proposed. The GAIL enables the AUV to directly learn from expert demonstrations, overcoming the difficulty of slow initial training of the network. Parallel training of multi-agents reduces the high correlation between samples to avoid local convergence. In addition, a reward function is designed to help training. Finally, the results show that in the unity simulation platform test, the proposed algorithm has a strong optimal decision-making ability in the tracking process. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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35 pages, 29941 KiB

Open AccessArticle

Passive Acoustic Detection of Vessel Activity by Low-Energy Wireless Sensors

by Gavin James Lowes, Jeffrey Neasham, Richie Burnett, Benjamin Sherlock and Charalampos Tsimenidis

J. Mar. Sci. Eng. 2022, 10(2), 248; https://doi.org/10.3390/jmse10020248 - 12 Feb 2022

Cited by 5 | Viewed by 2700

Abstract

This paper presents the development of a low-energy passive acoustic vessel detector to work as part of a wireless underwater monitoring network. The vessel detection method is based on a low-energy implementation of Detection of Envelope Modulation On Noise (DEMON). Vessels produce a broad spectrum modulated noise during propeller cavitation, which the DEMON method aims to extract for the purposes of automated detection. The vessel detector design has different approaches with mixtures of analogue and digital processing, as well as continuous and duty-cycled sampling/processing. The detector re-purposes an existing acoustic modem platform to achieve a low-cost and long-deployment wireless sensor network. This integrated communication platform enables the detector to switch between detection/communication mode seamlessly within software. The vessel detector was deployed at depth for a total of 84 days in the North Sea, providing a large data set, which the results are based on. Open sea field trial results have shown detection of single and multiple vessels with a 94% corroboration rate with local Automatic Identification System (AIS) data. Results showed that additional information about the detected vessel such as the number of propeller blades can be extracted solely based on the detection data. The attention to energy efficiency led to an average power consumption of 11.4 mW, enabling long term deployments of up to 6 months using only four alkaline C cells. Additional battery packs and a modified enclosure could enable a longer deployment duration. As the detector was still deployed during the first UK lockdown, the impact of COVID-19 on North Sea fishing activity was captured. Future work includes deploying this technology en masse to operate as part of a network. This could afford the possibility of adding vessel tracking to the abilities of the vessel detection technology when deployed as a network of sensor nodes. Full article

(This article belongs to the Special Issue New Challenges in Autonomous Underwater Networks)

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