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Underwater Wireless Communications: Recent Advances and Challenges
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Underwater Wireless Communications: Recent Advances and Challenges

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 (25 May 2024) | Viewed by 27642

Special Issue Editors

Prof. Dr. Hongxi Yin

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: underwater optical communication
Dr. Lianyou Jing

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: underwater acoustic communication

Special Issue Information

Dear Colleagues,

The ocean is the cradle of life, a treasure trove of resources and the origin of human survival. The underwater information network (UIN) plays a crucial role in ocean exploration and protection, as well as in resource development and utilization. Due to the ocean’s vast volume, complex environment and dynamic and changeable nature, as well as the motion of underwater targets, the acquisition, storage, processing and transmission of underwater information faces great challenges. Therefore, the exploration and study of new underwater communication and networking technologies have become urgent tasks in fields such as information and communication, marine science and engineering. At present, there are three types of underwater wireless communication: acoustic, wireless optical, and magnetic induction. Underwater technologies each have unique advantages and flaws; hence, they can be used independently based on specific application requirements, or they can be combined to complement each other and provide more flexible and diversified services with efficient use of resources, resulting in higher cost performance. Thus, it is necessary to conduct new research and put forth proposals of new system architecture, concepts, methods and technologies for underwater communications and networks. Therefore, this journal aims to present the latest research results, progress and reviews in the field of underwater communication and networks, and to provide new ideas and new technologies to further promote the research and development of underwater information networks. The scope of this Special Issue includes, but is not limited to:

  • Underwater acoustic communication;
  • Underwater wireless optical communication;
  • Underwater magnetic induction communication;
  • Underwater wireless sensor networking;
  • Underwater acoustic/wireless–optical hybrid networking;
  • Underwater positioning and tracking;
  • Physical topology, architecture and protocols for underwater information networks;
  • Deep-learning-based underwater communication and networking.

Prof. Dr. Hongxi Yin
Dr. Lianyou Jing
Guest Editors

Manuscript Submission Information

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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.

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Published Papers (16 papers)

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Research

27 pages, 2484 KiB  
Article
Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks
by Lei Yan, Lei Wang, Guanjun Li, Jingwei Shao and Zhixin Xia
J. Mar. Sci. Eng. 2024, 12(9), 1656; https://doi.org/10.3390/jmse12091656 - 16 Sep 2024
Viewed by 646
Abstract
Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous [...] Read more.
Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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19 pages, 15739 KiB  
Article
Extraction of Underwater Acoustic Signals across Sea–Air Media Using Butterworth Filtering
by Tengyuan Cui, Xiaolong Cao, Yiguang Yang, Qi Tan, Yuchen Du, Tongchang Zhang, Jiaqi Yuan, Zhenyuan Zhu and Jianquan Yao
J. Mar. Sci. Eng. 2024, 12(9), 1469; https://doi.org/10.3390/jmse12091469 - 23 Aug 2024
Viewed by 780
Abstract
Direct wireless communication through sea–air media is essential for constructing an integrated communication network that spans space, air, land, and sea. The amplitude of acoustically induced micromotion surface waves is much smaller than the noise interference in complex sea states, making the accurate [...] Read more.
Direct wireless communication through sea–air media is essential for constructing an integrated communication network that spans space, air, land, and sea. The amplitude of acoustically induced micromotion surface waves is much smaller than the noise interference in complex sea states, making the accurate extraction of these signals from the raw signals detected by an FMCW millimeter-wave radar a major challenge. In this paper, Butterworth filtering is used to extract underwater acoustic signals from the surface waves detected by radar. The physical processes of the channel were simulated theoretically and verified experimentally. The results demonstrate a fitting coefficient of 0.99 between the radar-detected water surface waves and the simulation outcomes, enabling the effective elimination of noise interference and the extraction of acoustically induced micromotion signals in environments with a signal-to-noise ratio (SNR) of −20 dB to −10 dB. Experiments modifying frequency and linear frequency modulation have verified that the usable frequency range for underwater acoustic signals is at least 400 Hz, meeting the frequency requirements of Binary Frequency Shift Keying (2FSK) modulation encoding methods. This research confirms the accuracy of the simulation results and the feasibility of filtering and extracting underwater acoustic signals, providing a theoretical basis and an experimental foundation for building cross-media communication links. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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24 pages, 8365 KiB  
Article
Node Adjustment Scheme of Underwater Wireless Sensor Networks Based on Motion Prediction Model
by Han Zheng, Haonan Chen, Anqi Du, Meijiao Yang, Zhigang Jin and Ye Chen
J. Mar. Sci. Eng. 2024, 12(8), 1256; https://doi.org/10.3390/jmse12081256 - 25 Jul 2024
Viewed by 816
Abstract
With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure’s destruction, [...] Read more.
With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure’s destruction, communication performance decline, and node life-shortening. Therefore, a Node Adjustment Scheme based on Motion Prediction (NAS-MP) is proposed, which integrates the layered model of the ocean current’s uneven depth, the layered ocean current prediction model based on convolutional neural network (CNN)–transformer, the node trajectory prediction model, and the periodic depth adjustment model based on the Seagull Optimization Algorithm (SOA), to improve the network coverage and connectivity. Firstly, the error threshold of the current velocity and direction in the layer was introduced to divide the depth levels, and the regional current data model was constructed according to the measured data. Secondly, the CNN–transformer hybrid network was used to predict stratified ocean currents. Then, the prediction data of layered ocean currents was applied to the nodes’ drift model, and the nodes’ motion trajectory prediction was obtained. Finally, based on the trajectory prediction of nodes, the SOA obtained the optimal depth of nodes to optimize the coverage and connectivity of the UWSN. Experimental simulation results show that the performance of the proposed scheme is superior. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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23 pages, 5691 KiB  
Article
uw-WiFi: Small-Scale Data Collection Network-Based Underwater Internet of Things
by Jifeng Zhu, Xiaohe Pan, Zheng Peng, Mengzhuo Liu, Jingqian Guo and Jun-Hong Cui
J. Mar. Sci. Eng. 2024, 12(3), 481; https://doi.org/10.3390/jmse12030481 - 13 Mar 2024
Cited by 1 | Viewed by 1277
Abstract
The establishment of the Underwater Internet of Things (UIoT) and the realization of interconnection between heterogeneous underwater intelligent devices are urgent global challenges. Underwater acoustic networking is the most suitable technology to achieve UIoT for medium to long ranges. This paper presents an [...] Read more.
The establishment of the Underwater Internet of Things (UIoT) and the realization of interconnection between heterogeneous underwater intelligent devices are urgent global challenges. Underwater acoustic networking is the most suitable technology to achieve UIoT for medium to long ranges. This paper presents an underwater Wi-Fi network, called uw-WiFi, that utilizes a master–slave mode architecture. uw-WiFi is dedicated to solving the problem of underwater acoustic networking with limited coverage range and number of nodes. To ensure the reliability of different types of data in the network, a reliable segmentation transmission protocol based on data type is designed. Additionally, on-demand scheduling based on the reservation MAC protocol is developed to solve the channel resource sharing problem. The uw-WiFi system has undergone shallow sea tests, and the experimental results demonstrate that the uw-WiFi network is capable of achieving a network throughput of 500 bps or higher, indicating superior network performance. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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16 pages, 1471 KiB  
Article
Cross-Domain Contrastive Learning-Based Few-Shot Underwater Acoustic Target Recognition
by Xiaodong Cui, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu and Jing Han
J. Mar. Sci. Eng. 2024, 12(2), 264; https://doi.org/10.3390/jmse12020264 - 1 Feb 2024
Cited by 2 | Viewed by 1361
Abstract
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through [...] Read more.
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the unique features of ship radiation noise. This study proposes a novel cross-domain contrastive learning-based few-shot (CDCF) method for UATR to alleviate overfitting issues. This approach leverages self-supervised training on both source and target domains to facilitate rapid adaptation to the target domain. Additionally, a base contrastive module is introduced. Positive and negative sample pairs are generated through data augmentation, and the similarity in the corresponding frequency bands of feature embedding is utilized to learn fine-grained features of ship radiation noise, thereby expanding the scope of knowledge in the source domain. We evaluate the performance of CDCF in diverse scenarios on ShipsEar and DeepShip datasets. The experimental results indicate that in cross-domain environments, the model achieves accuracy rates of 56.71%, 73.02%, and 76.93% for 1-shot, 3-shot, and 5-shot scenarios, respectively, outperforming other FSL methods. Moreover, the model demonstrates outstanding performance in noisy environments. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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18 pages, 2093 KiB  
Article
Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
by Ronglin Xing, Yuhang Zhang, Yizhi Feng and Fei Ji
J. Mar. Sci. Eng. 2024, 12(1), 43; https://doi.org/10.3390/jmse12010043 - 23 Dec 2023
Cited by 2 | Viewed by 1204
Abstract
Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the [...] Read more.
Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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16 pages, 500 KiB  
Article
An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT
by Wentao Shi, Yongqi Tang, Mingqi Jin and Lianyou Jing
J. Mar. Sci. Eng. 2023, 11(12), 2279; https://doi.org/10.3390/jmse11122279 - 30 Nov 2023
Viewed by 1082
Abstract
The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use [...] Read more.
The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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17 pages, 440 KiB  
Article
Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise
by Zhiqiang Liu, Yongqing Zhang, Weidong Wang, Xiangshui Li, Hui Li, Wentao Shi and Wasiq Ali
J. Mar. Sci. Eng. 2023, 11(9), 1798; https://doi.org/10.3390/jmse11091798 - 14 Sep 2023
Cited by 1 | Viewed by 1095
Abstract
Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector [...] Read more.
Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector sensor array (AVSA). Firstly, to remove high outliers from the array output data, the output information of the AVSA is weighted by using the infinite norm. To further suppress outliers, a p-order cost function is formulated by extending the Frobenius norm to lower order, and then the expression of the signal power is quantified. Lastly, the DOA is approximated on the signal power by a spectral peak search mechanism. DOA estimation results based on Monte Carlo simulations validate the accuracy and robustness of the proposed techniques herein compared to the current, available methods in the context of intense impulsive noise, low generalized signal–to–noise ratio (GSNR), and a smaller number of snapshots. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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23 pages, 28213 KiB  
Article
A Distributed Intelligent Buoy System for Tracking Underwater Vehicles
by Mengzhuo Liu, Jifeng Zhu, Xiaohe Pan, Guolin Wang, Jun Liu, Zheng Peng and Jun-Hong Cui
J. Mar. Sci. Eng. 2023, 11(9), 1661; https://doi.org/10.3390/jmse11091661 - 24 Aug 2023
Cited by 2 | Viewed by 1860
Abstract
Underwater vehicles play a crucial role in various underwater applications, such as data collection in underwater sensor networks, target detection and tracking, and underwater pipeline monitoring. Real-time acquisition of their states, particularly their location and velocity, is vital for their operation and navigation. [...] Read more.
Underwater vehicles play a crucial role in various underwater applications, such as data collection in underwater sensor networks, target detection and tracking, and underwater pipeline monitoring. Real-time acquisition of their states, particularly their location and velocity, is vital for their operation and navigation. Consequently, the development of a remote tracking system to monitor these states is essential. In this paper, we propose a system that can track the underwater vehicle’s location and velocity. We take a systematic approach that encompasses the system architecture, system composition, signal processing, and mobility state estimation. We present the system architecture and define its components, along with their relationships and interfaces. The beacon signal employed in the system features dual-hyperbolic-frequency-modulated (HFM) waveform and an OFDM symbol with cyclic prefix (CP). Based on this beacon signal, we demonstrate how signal processing techniques are utilized to precisely determine the time of arrival and reduce false alarm rates in underwater acoustic channels affected by impulsive noise. Additionally, we explain how the CP-OFDM symbol is used to measure the Doppler scaling factor and transmit essential information for localization and velocity estimation purposes. Utilizing the measurements obtained through signal processing, least squares estimators are used for estimating both the location and velocity. To validate the effectiveness of our approach, we implement the system and conduct field trials. Two separate experiments were conducted in which the diagonal lengths of the square topology were designed to be 1000 m and 800 m. The minimum/maximum root mean square error of localization in the first and second experiment is 2.36/2.91 m and 1.47/2.49 m, respectively. And the minimum/maximum root mean square error of velocity estimation in the first and second experiment is 0.16/0.47 m/s and 0.21/0.76 m/s, respectively. Results confirm the effectiveness of the proposed method in estimating the location and velocity of the underwater vehicle. Overall, this paper provides a practical and effective design of a system to track the location and velocity of underwater vehicles. By leveraging the proposed system, signal processing, and mobility state estimation methods, our work offers a systematic solution. And, the successful field experiment serves as evidence of the feasibility and effectiveness of the proposed system, making it a valuable contribution to the field of tracking underwater vehicles. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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32 pages, 12519 KiB  
Article
A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning
by Run Zhang, Chengbing He, Lianyou Jing, Chaopeng Zhou, Chao Long and Jiachao Li
J. Mar. Sci. Eng. 2023, 11(8), 1632; https://doi.org/10.3390/jmse11081632 - 21 Aug 2023
Cited by 8 | Viewed by 1882
Abstract
Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be [...] Read more.
Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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18 pages, 1530 KiB  
Article
Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
by Yuzhi Zhang, Shumin Zhang, Yang Wang, Qingyuan Liu and Xiangxiang Li
J. Mar. Sci. Eng. 2023, 11(8), 1537; https://doi.org/10.3390/jmse11081537 - 1 Aug 2023
Cited by 1 | Viewed by 1819
Abstract
Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet [...] Read more.
Accurate channel estimation is the fundamental requirement for recovering underwater acoustic orthogonal time–frequency space (OTFS) modulation signals. As the Doppler effect in the underwater acoustic channel is much more severe than that in the radio channel, the channel information usually cannot strictly meet the compressed sensing sparsity assumption in the orthogonal matching pursuit channel estimation algorithm. This deviation ultimately leads to a degradation in system performance. This paper proposes a novel approach for OTFS channel estimation in underwater acoustic communications, utilizing a model-driven deep learning technique. Our method incorporates a residual neural network into the OTFS channel estimation process. Specifically, the orthogonal matching pursuit algorithm and denoising convolutional neural network (DnCNN) collaborate to perform channel estimation. The cascaded DnCNN denoises the preliminary channel estimation results generated by the orthogonal matching pursuit algorithm for more accurate OTFS channel estimation results. The use of a lightweight DnCNN network with a single residual block reduces computational complexity while still preserving the accuracy of the neural network. Through extensive evaluations conducted on simulated and experimental underwater acoustic channels, the outcomes demonstrate that our proposed method outperforms traditional threshold-based and orthogonal matching pursuit channel estimation techniques, achieves superior accuracy in channel estimation, and significantly reduces the system’s bit error rate. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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21 pages, 5250 KiB  
Article
A uw-Cellular Network: Design, Implementation and Experiments
by Jifeng Zhu, Xiaohe Pan, Zheng Peng, Mengzhuo Liu, Jingqian Guo, Tong Zhang, Yu Gou and Jun-Hong Cui
J. Mar. Sci. Eng. 2023, 11(4), 827; https://doi.org/10.3390/jmse11040827 - 13 Apr 2023
Cited by 1 | Viewed by 2090
Abstract
The most significant increase of current task is in the desire for operational flexibility and agility in large-scale underwater network application scenarios in recent years. In order to address the challenging problems in Underwater Wireless Sensor Networks (UWSNs), we propose a large-scale UWSN [...] Read more.
The most significant increase of current task is in the desire for operational flexibility and agility in large-scale underwater network application scenarios in recent years. In order to address the challenging problems in Underwater Wireless Sensor Networks (UWSNs), we propose a large-scale UWSN based on the cellular network architecture called Underwater Cellular (uw-Cellular) network. It is designed especially for application scenarios where a large number of both fixed and mobile network nodes exist in a wide area to monitor the underwater environment. We also design protocols in each network layer in order to ensure reasonable channel sharing, data forwarding path selection and data reliability. The purpose of the simulation study we implement is to evaluate the performance of the CLA routing strategy compared to the VBF and the MFLOOD protocols in the uw-Cellular network. We also deploy a twenty-node uw-Cellular network in the real-world environment as the field case study. The experimental results showed that the Data Rate between any nodes could reach above 500 bps, and the network Average Throughput was no less than 550 bps under various load situations. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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20 pages, 2910 KiB  
Article
An On-Demand Scheduling-Based MAC Protocol for UW-WiFi Networks
by Xiaohe Pan, Jifeng Zhu, Mengzhuo Liu, Xiaoyu Wang, Zheng Peng, Jun Liu and Junhong Cui
J. Mar. Sci. Eng. 2023, 11(4), 765; https://doi.org/10.3390/jmse11040765 - 31 Mar 2023
Cited by 5 | Viewed by 1728
Abstract
Underwater Internet-of-Things (UIoT) is an extension of Internet-of-Things technology in underwater. The underwater acoustic network with WiFi architecture (UW-WiFi), as a specific deployment of UIoT, has been proved to be a promising technique for wide-ranging marine applications. However, due to the unique features [...] Read more.
Underwater Internet-of-Things (UIoT) is an extension of Internet-of-Things technology in underwater. The underwater acoustic network with WiFi architecture (UW-WiFi), as a specific deployment of UIoT, has been proved to be a promising technique for wide-ranging marine applications. However, due to the unique features of underwater acoustic channel, such as long and variable propagation delay, low available bandwidth and high bit error rate, conventional medium access control (MAC) protocols designed for terrestrial WiFi networks need an overhaul to work efficiently for UW-WiFi networks. In consideration of the aforementioned channel features, different demands of nodes to occupy channel resources and the reliability of data transmission, a time sequence-based dynamic on-demand assignment (SDDA) MAC protocol for UW-WiFi networks is proposed in this paper. In SDDA, the collision-free scheduling is integrated with the reservation mechanism, aiming to address the issue of various access requirements of diverse task nodes on channel resources in the UW-WiFi network system. The designed protocol employs propagation delays and the amount of data to be sent by terminal nodes to achieve non-conflict transmissions of control packets and on-demand scheduling of data packets. Additionally, the scheme does not require extra overhead for time synchronization. Comparison simulations demonstrate that SDDA provides considerable benefits in terms of channel utilization, end-to-end delay and packet delivery ratio. At last, the SDDA protocol is implemented and a UW-WiFi network system is set up in the marine environment. The ocean field experiment results agree well with the simulated ones and also verify that the proposed protocol achieve conflict-free on-demand scheduling, fairness among terminal nodes at different ranges and the practicability in actual environments. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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17 pages, 562 KiB  
Article
Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
by Yao Sun, Wei Ge, Yingsong Li and Jingwei Yin
J. Mar. Sci. Eng. 2023, 11(3), 666; https://doi.org/10.3390/jmse11030666 - 21 Mar 2023
Cited by 5 | Viewed by 1948
Abstract
The long propagation delay of acoustic links leads to the complex randomness of packet collision, which reduces the network packet delivery rate (PDR) and aggravates network congestion. A single vector hydrophone with directional reception characteristics can concentrate the reception gain on a certain [...] Read more.
The long propagation delay of acoustic links leads to the complex randomness of packet collision, which reduces the network packet delivery rate (PDR) and aggravates network congestion. A single vector hydrophone with directional reception characteristics can concentrate the reception gain on a certain direction, which can increase spatial reuse, reduce packet collision, and help to improve the performance of the underwater acoustic wireless sensor networks (UASNs). Herein, this paper proposes a cross-layer protocol with low interference and low congestion (CLIC) based on directional reception. An integrated routing-medium access control (MAC) design is also devised in the CLIC scheme to use the directional beams to create the least-interfering, highest-capacity data transmission links, weighing key factors affecting network performance to obtain routes with low collisions and low congestion. Simulation results show that the CLIC has a higher packet delivery rate (PDR) and higher energy efficiency compared to the QELAR, CITP, and VBF protocols. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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16 pages, 3268 KiB  
Article
Performance Analysis of MIMO-mQAM System with Pointing Errors and Beam Spreading in Underwater Málaga Turbulence Channel
by Jianying Wang, Hongxi Yin, Xiuyang Ji and Yanjun Liang
J. Mar. Sci. Eng. 2023, 11(3), 633; https://doi.org/10.3390/jmse11030633 - 17 Mar 2023
Cited by 5 | Viewed by 1568
Abstract
Both the long-term beam spreading caused by ocean turbulence and the pointing errors induced by the jitter of transmitters and receivers degrade the performance of underwater wireless optical communication (UWOC) links. To effectively alleviate their effects, an in-depth study was carried out over [...] Read more.
Both the long-term beam spreading caused by ocean turbulence and the pointing errors induced by the jitter of transmitters and receivers degrade the performance of underwater wireless optical communication (UWOC) links. To effectively alleviate their effects, an in-depth study was carried out over the Málaga turbulence channel with pointing errors and beam spreading in multiple-input and multiple-output (MIMO) UWOC. First, we analyzed the long-term beam spreading and the received light power for the finite receiving aperture in the presence of pointing error displacements. Based on this, the relationship between beam spreading, pointing errors, and signal power was established. Second, the approximate expressions of the average bit error rate (BER) and the communication outage probability were derived theoretically for this MIMO system using maximal-ratio combining (MRC) diversity. Third, the effects of the pointing errors on the coding and the diversity gains were explored for the MIMO links. Finally, using the observed ocean data from the Global Ocean Argo gridded dataset, we numerically verified the combined effects of ocean turbulence strength, beam spreading, and pointing errors on the average BER and outage probability of this system. These results also proved that adjusting the size of the receiving aperture or the order of the multiple quadrature amplitude modulation (mQAM) could effectively mitigate their effects. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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13 pages, 4114 KiB  
Article
Optimization of LED Array Spatial Coverage Characteristics in Underwater Wireless Optical Communication
by Anliang Liu, Yingming Yuan, Hongxi Yin, Haobo Zhao and Xianping Fu
J. Mar. Sci. Eng. 2023, 11(2), 253; https://doi.org/10.3390/jmse11020253 - 20 Jan 2023
Cited by 5 | Viewed by 1813
Abstract
To achieve uniform spatial coverage characteristics in optical signals in an underwater wireless optical communication (UWOC) system, and therefore reduce the requirement of the alignment between the receiver and the transmitter, we propose an optimized scheme of optical signal coverage based on a [...] Read more.
To achieve uniform spatial coverage characteristics in optical signals in an underwater wireless optical communication (UWOC) system, and therefore reduce the requirement of the alignment between the receiver and the transmitter, we propose an optimized scheme of optical signal coverage based on a light-emitting diode (LED) array in this paper. For high-efficiency coverage of the optical signals, the pitch angle of the LED light source is first optimized on the basis of the light beam geometry. Then, the layout of the LED array and the horizontal deflection angle of the light source are jointly optimized by an improved particle swarm optimization (PSO) algorithm. Taking a 16-LED array as an example, the performances of the spatial coverage characteristics with three different LED array layouts are analyzed in detail under four typical seawater environments. The results show that the LED array with the PSO-optimized layout can achieve better uniformity in the power distribution for the received optical signals, and enhance the robustness of the UWOC system in complex seawater environments. Full article
(This article belongs to the Special Issue Underwater Wireless Communications: Recent Advances and Challenges)
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