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Special Issue "Smart Ocean: Emerging Research Advances, Prospects and Challenges"

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

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editors

Guest Editor
Dr. Guangjie Han

Department of Information and Communication System, Hohai University, Changzhou 213022, China
Website | E-Mail
Interests: wireless sensor networks; green computing; smart computing; mobile Internet; cloud computing
Guest Editor
Prof. Dr. Jaime Lloret Mauri

Department of Communications, Polytechnic University of Valencia, Camino de Vera 46022, Valencia, Spain
Website | E-Mail
Phone: +34609549043
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming

Special Issue Information

Dear Colleagues,

The ocean contains a rich variety of resources, such as biological resources, oil and gas resources, solid mineral resources, sea water resources, ocean energy, etc. These resources will provide tremendous material support for human resources shortage. However, except traditional marine biological resources, the development and utilization of other resources are basically in the initial stage. Therefore, the ocean has become the basic support and constraints that affect the sustainable development of humans Smart Oceans is a deep integration of industrialization and informatization in the ocean field, and provides an overall solution for comprehensively enhancing the capabilities of the ocean. Based on marine information collection and transmission systems, Smart Ocean engineering systematically integrates equipment and activities in the areas of marine sensing, control, and development, and, by using big data technology, it realizes the sharing of marine resources and the coordination of marine activities so as to explore new requirements and create new values in the marine field.

The strategy in Smart Oceans has led to emerging innovations in academia, industry, and government. It creates many scientific and engineering challenges that need ingenious research efforts in order to develop efficient, scalable and secure sensing, communication, networking, and data processing technologies.

This Special Issue aims to bring together researchers and practitioners to discuss various aspects of Smart Ocean, explore key technologies, and develop new applications in this research field. We solicit papers covering various topics of interest that include, but are not limited to, the following:

  • Underwater smart devices and equipment, including smart sensors, autonomous underwater vehicle (AUV), underwater robots for interoperable Smart Ocean environments
  • Underwater communicating for Smart Ocean, including acoustic, optical, RF, magneto-inductive, quantum and hybrid communication technologies
  • Underwater networking for Smart Ocean, including network theory, architecture, protocols;
  • Underwater deployment, synchronization, positioning technologies for Smart Ocean;
  • Cloud computing and edge computing for Smart Ocean;
  • Big data processing technologies and applications for Smart Ocean;
  • Reliability, security, privacy and trust theories and mechanism for Smart Ocean;
  • System design, modeling and evaluation for Smart Ocean;
  • Business and social issues and applications for Smart Ocean
  • Artificial Intelligence for the applications of Smart Ocean

Dr. Guangjie Han
Dr. Jaime Lloret Mauri
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. 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 1800 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

  • Smart Ocean 
  • Underwater Communication and Networks 
  • Underwater Systems

Published Papers (25 papers)

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Research

Open AccessArticle Prediction of Marine Pycnocline Based on Kernel Support Vector Machine and Convex Optimization Technology
Sensors 2019, 19(7), 1562; https://doi.org/10.3390/s19071562
Received: 26 January 2019 / Revised: 26 March 2019 / Accepted: 28 March 2019 / Published: 31 March 2019
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Abstract
With the explosive growth of ocean data, it is of great significance to use ocean observation data to analyze ocean pycnocline data in military field. However, due to natural factors, most of the time the ocean hydrological data is not complete. In this [...] Read more.
With the explosive growth of ocean data, it is of great significance to use ocean observation data to analyze ocean pycnocline data in military field. However, due to natural factors, most of the time the ocean hydrological data is not complete. In this case, predicting the ocean hydrological data by partial data has become a hot spot in marine science. In this paper, based on the traditional statistical analysis literature, we propose a machine-learning ocean hydrological data processing process under big data. At the same time, based on the traditional pycnocline gradient determination method, the open Argo data set is analyzed, and the local characteristics of pycnocline are verified from several aspects combined with the current research about pycnocline. Most importantly, in this paper, the combination of kernel function and support vector machine(SVM) is extended to nonlinear learning by using the idea of machine learning and convex optimization technology. Based on this, the known pycnocline training set is trained, and an accurate model is obtained to predict the pycnocline in unknown domains. In the specific steps, this paper combines the classification problem with the regression problem, and determines the proportion of training set and test formula set by polynomial regression. Subsequently, the feature scaling of the input data accelerated the gradient convergence, and a grid search algorithm with variable step size was proposed to determine the super parameter c and gamma of the SVM model. The prediction results not only used the confusion matrix to analyze the accuracy of GridSearch-SVM with variable step size, but also compared the traditional SVM and the similar algorithm. At the end of the experiment, two features which have the greatest influence on the Marine density thermocline are found out by the feature ranking algorithm based on learning. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions
Sensors 2019, 19(5), 1259; https://doi.org/10.3390/s19051259
Received: 28 December 2018 / Revised: 19 February 2019 / Accepted: 9 March 2019 / Published: 12 March 2019
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Abstract
This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the [...] Read more.
This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
Sensors 2019, 19(5), 1255; https://doi.org/10.3390/s19051255
Received: 30 December 2018 / Revised: 6 March 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
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Abstract
Aiming at the real-time observation requirements in marine science and ocean engineering, based on underwater acoustic communication and satellite communication technology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed. It consists of a seabed observation system, a [...] Read more.
Aiming at the real-time observation requirements in marine science and ocean engineering, based on underwater acoustic communication and satellite communication technology, a seabed real-time sensing system for in-situ long-term multi-parameter observation applications (SRSS/ILMO) is proposed. It consists of a seabed observation system, a sea surface relay transmission buoy, and a remote monitoring system. The system communication link is implemented by underwater acoustic communication and satellite communication. The seabed observation system adopts the “ARM + FPGA” architecture to meet the low power consumption, scalability, and versatility design requirements. As a long-term unattended system, a two-stage anti-crash mechanism, an automatic system fault isolation design, dual-medium data storage, and improved Modbus protocol are adopted to meet the system reliability requirements. Through the remote monitoring system, users can configure the system working mode, sensor parameters and acquire observation data on demand. The seabed observation system can realize the observation of different fields by carrying different sensors such as those based on marine engineering geology, chemistry, biology, and environment. Carrying resistivity and pore pressure sensors, the SRSS/ILMO powered by seawater batteries was used for a seabed engineering geology observation. The preliminary test results based on harbor environment show the effectiveness of the developed system. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Adaptive Node Clustering Technique for Smart Ocean under Water Sensor Network (SOSNET)
Sensors 2019, 19(5), 1145; https://doi.org/10.3390/s19051145
Received: 31 December 2018 / Revised: 7 February 2019 / Accepted: 8 February 2019 / Published: 6 March 2019
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Abstract
Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of [...] Read more.
Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Photon-Counting Underwater Optical Wireless Communication for Reliable Video Transmission Using Joint Source-Channel Coding Based on Distributed Compressive Sensing
Sensors 2019, 19(5), 1042; https://doi.org/10.3390/s19051042
Received: 31 December 2018 / Revised: 23 February 2019 / Accepted: 24 February 2019 / Published: 1 March 2019
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Abstract
To achieve long-distance underwater optical wireless communication, a single photon detector with single photon limit sensitivity is used to detect the optical signal at the receiver. The communication signal is extracted from the discrete single photon pulses output from the detector. Due to [...] Read more.
To achieve long-distance underwater optical wireless communication, a single photon detector with single photon limit sensitivity is used to detect the optical signal at the receiver. The communication signal is extracted from the discrete single photon pulses output from the detector. Due to fluctuation of photon flux and quantum efficiency of photon detection, long-distance underwater optical wireless communication has the characteristics that the link is easily interrupted, the bit error rate is high, and the burst error is large. To achieve reliable video transmission, a joint source-channel coding scheme based on residual distributed compressive video sensing is proposed for the underwater photon counting communication system. Signal extraction from single photon pulses, data frame and data verification are specifically designed. This scheme greatly reduces the amount of data at the transmitter, transfers the computational complexity to the decoder in receiver, and enhances anti-channel error ability. The experimental results show that, when the baud rate was 100 kbps and the average number of photon pulses per bit was 20, the bit error rate (BER) was 0.0421 and video frame could still be restored clearly. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems
Sensors 2019, 19(4), 821; https://doi.org/10.3390/s19040821
Received: 29 December 2018 / Revised: 13 February 2019 / Accepted: 14 February 2019 / Published: 17 February 2019
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Abstract
In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean [...] Read more.
In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS). Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Geographic and Opportunistic Recovery with Depth and Power Transmission Adjustment for Energy-Efficiency and Void Hole Alleviation in UWSNs
Sensors 2019, 19(3), 709; https://doi.org/10.3390/s19030709
Received: 16 November 2018 / Revised: 13 January 2019 / Accepted: 14 January 2019 / Published: 9 February 2019
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Abstract
Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end-to-end delay and maximum energy dissipation. The provisioning [...] Read more.
Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end-to-end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems which, due to which lifespan of the network increases. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform the counterparts. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Low-cost Electromagnetic Docking Guidance System for Micro Autonomous Underwater Vehicles
Sensors 2019, 19(3), 682; https://doi.org/10.3390/s19030682
Received: 29 December 2018 / Revised: 2 February 2019 / Accepted: 3 February 2019 / Published: 7 February 2019
Cited by 1 | PDF Full-text (9711 KB) | HTML Full-text | XML Full-text
Abstract
As important observational platforms for the Smart Ocean concept, autonomous underwater vehicles (AUVs) that perform long-term observation in fleets are beneficial because they provide large-scale sampling data with a sufficient spatiotemporal resolution. Therefore, a large number of low-cost micro AUVs with docking capability [...] Read more.
As important observational platforms for the Smart Ocean concept, autonomous underwater vehicles (AUVs) that perform long-term observation in fleets are beneficial because they provide large-scale sampling data with a sufficient spatiotemporal resolution. Therefore, a large number of low-cost micro AUVs with docking capability for power recharge and data transmission are essential. This study designed a low-cost electromagnetic docking guidance (EMDG) system for micro AUVs. The EMDG system is composed of a transmitter coil located on the dock and a three-axial search coil magnetometer acting as a receiver. The search coil magnetometer was optimized for small sizes while maintaining sufficient sensitivity. The signal conditioning and processing subsystem was designed to calculate the deflection angle (β) for docking guidance. Underwater docking tests showed that the system can detect the electromagnetic signal and successfully guide AUV docking. The AUV can still perform docking in extreme positions, which cannot be realized through normal optical or acoustic guidance. This study is the first to focus on the EM guidance system for low-cost micro AUVs. The search coil sensor in the AUV is inexpensive and compact so that the system can be equipped on a wide range of AUVs. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Motion Plan of Maritime Autonomous Surface Ships by Dynamic Programming for Collision Avoidance and Speed Optimization
Sensors 2019, 19(2), 434; https://doi.org/10.3390/s19020434
Received: 18 November 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
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Abstract
Maritime Autonomous Surface Ships (MASS) with advanced guidance, navigation, and control capabilities have attracted great attention in recent years. Sailing safely and efficiently are critical requirements for autonomous control of MASS. The MASS utilizes the information collected by the radar, camera, and Autonomous [...] Read more.
Maritime Autonomous Surface Ships (MASS) with advanced guidance, navigation, and control capabilities have attracted great attention in recent years. Sailing safely and efficiently are critical requirements for autonomous control of MASS. The MASS utilizes the information collected by the radar, camera, and Autonomous Identification System (AIS) with which it is equipped. This paper investigates the problem of optimal motion planning for MASS, so it can accomplish its sailing task early and safely when it sails together with other conventional ships. We develop velocity obstacle models for both dynamic and static obstacles to represent the potential conflict-free region with other objects. A greedy interval-based motion-planning algorithm is proposed based on the Velocity Obstacle (VO) model, and we show that the greedy approach may fail to avoid collisions in the successive intervals. A way-blocking metric is proposed to evaluate the risk of collision to improve the greedy algorithm. Then, by assuming constant velocities of the surrounding ships, a novel Dynamic Programming (DP) method is proposed to generate the optimal multiple interval motion plan for MASS. These proposed algorithms are verified by extensive simulations, which show that the DP algorithm provides the lowest collision rate overall and better sailing efficiency than the greedy approaches. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs
Sensors 2019, 19(2), 370; https://doi.org/10.3390/s19020370
Received: 23 December 2018 / Revised: 15 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
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Abstract
Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of [...] Read more.
Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network
Sensors 2019, 19(2), 350; https://doi.org/10.3390/s19020350
Received: 28 November 2018 / Revised: 10 January 2019 / Accepted: 11 January 2019 / Published: 16 January 2019
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Abstract
The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based [...] Read more.
The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 × 1080. This meets our needs for underwater identification tasks. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility
Sensors 2019, 19(2), 256; https://doi.org/10.3390/s19020256
Received: 17 December 2018 / Revised: 4 January 2019 / Accepted: 5 January 2019 / Published: 10 January 2019
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Abstract
Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so [...] Read more.
Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR). Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
Sensors 2019, 19(1), 183; https://doi.org/10.3390/s19010183
Received: 28 October 2018 / Revised: 26 December 2018 / Accepted: 26 December 2018 / Published: 7 January 2019
Cited by 1 | PDF Full-text (1855 KB) | HTML Full-text | XML Full-text
Abstract
A hybrid optical-acoustic underwater wireless sensor network (OA-UWSN) was proposed to solve the problem of high-speed transmission of real-time video and images in marine information detection. This paper proposes a novel energy-efficient contention-based media access control (MAC) protocol (OA-CMAC) for the OA-UWSN. Based [...] Read more.
A hybrid optical-acoustic underwater wireless sensor network (OA-UWSN) was proposed to solve the problem of high-speed transmission of real-time video and images in marine information detection. This paper proposes a novel energy-efficient contention-based media access control (MAC) protocol (OA-CMAC) for the OA-UWSN. Based on optical-acoustic fusion technology, our proposed OA-CMAC combines the postponed access mechanism in carrier sense multiple access with collision avoidance (CSMA/CA) and multiplexing-based spatial division multiple access (SDMA) technology to achieve high-speed and real-time data transmission. The protocol first performs an acoustic handshake to obtain the location information of a transceiver node, ensuring that the channel is idle. Otherwise, it performs postponed access and waits for the next time slot to contend for the channel again. Then, an optical handshake is performed to detect whether the channel condition satisfies the optical transmission, and beam alignment is performed at the same time. Finally, the nodes transmit data using optical communication. If the channel conditions do not meet the requirements for optical communication, a small amount of data with high priority is transmitted through acoustic communication. An evaluation of the proposed MAC protocol was performed with OMNeT++ simulations. The results showed that when the optical handshaking success ratio was greater than 50%, compared to the O-A handshake protocol in the literature, our protocol could result in doubled throughput. Due to the low energy consumption of optical communication, the node’s lifetime is 30% longer than that of pure acoustic communication, greatly reducing the network operation cost. Therefore, it is suitable for large-scale underwater sensor networks with high loads. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Orthogonal Frequency Division Multiplexing Techniques Comparison for Underwater Optical Wireless Communication Systems
Sensors 2019, 19(1), 160; https://doi.org/10.3390/s19010160
Received: 5 December 2018 / Revised: 23 December 2018 / Accepted: 29 December 2018 / Published: 4 January 2019
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Abstract
Optical wireless communication is an energy-efficient and cost-effective solution for high-speed and highly-secure wireless connections. In this paper, we compare, discuss, and analyze three popular optical orthogonal frequency division multiplexing (OFDM) techniques, such as DC-biased optical OFDM (DCO-OFDM), asymmetrically-clipped optical OFDM (ACO-OFDM), and [...] Read more.
Optical wireless communication is an energy-efficient and cost-effective solution for high-speed and highly-secure wireless connections. In this paper, we compare, discuss, and analyze three popular optical orthogonal frequency division multiplexing (OFDM) techniques, such as DC-biased optical OFDM (DCO-OFDM), asymmetrically-clipped optical OFDM (ACO-OFDM), and unipolar OFDM (U-OFDM), for underwater optical wireless communication systems. The peak power constraint, bandwidth limit of the light source, turbulence fading underwater channel, and the channel estimation error are taken into account. To maximize the achievable data propagation distance, we propose to optimize the modulation index that controls the signal magnitude, and a bitloading algorithm is applied. This optimization process trades off the clipping distortion caused by the peak power constraint and the signal to noise ratio (SNR). The SNR and clipping effects of the three compared OFDM techniques are modeled in this paper. From the numerical results, DCO-OFDM outperforms ACO- and U-OFDM when the transmitted bit rate is high compared to the channel bandwidth. Otherwise, U-OFDM can provide a longer propagation distance or requires less transmitted power. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Cluster Sleep-Wake Scheduling Algorithm Based on 3D Topology Control in Underwater Sensor Networks
Sensors 2019, 19(1), 156; https://doi.org/10.3390/s19010156
Received: 3 November 2018 / Revised: 22 December 2018 / Accepted: 29 December 2018 / Published: 4 January 2019
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Abstract
3D topology control in underwater sensor networks is of great significance to ensuring reliable and efficient operation of the network. In this paper, by analyzing the characteristics of an underwater sensor network, we take the cube as the basic unit to perform 3D [...] Read more.
3D topology control in underwater sensor networks is of great significance to ensuring reliable and efficient operation of the network. In this paper, by analyzing the characteristics of an underwater sensor network, we take the cube as the basic unit to perform 3D partition of the monitoring area, define the 3D partition unit and basic cluster structure of the underwater sensor network, and arrange rotating temporary control nodes in the cluster. Then, a cluster sleep-wake scheduling algorithm is proposed that compares the remaining node energy. It selects the node with the largest remaining energy as the working node, and the remaining nodes complete the transition of dormancy and waiting states as long as they reach the preset dormancy time. The node state settings of this phase are completed by the temporary control node. Temporary control nodes selecting and sleep-wake scheduling are used in the algorithm through 3D topology control, which reduces energy consumption and guarantees maximum sensing coverage of the entire network and the connection rate of active nodes. Simulation results further verify the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Two-Factor-Based Public Data Protection Scheme in Smart Ocean Management
Sensors 2019, 19(1), 129; https://doi.org/10.3390/s19010129
Received: 2 December 2018 / Revised: 19 December 2018 / Accepted: 26 December 2018 / Published: 2 January 2019
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Abstract
Nowadays, two-factor data security protection has become a research hotspot in smart ocean management. With the increasing popularity of smart ocean management, how to achieve the two-factor protection of public data resources in smart ocean management is a serious problem to be tackled. [...] Read more.
Nowadays, two-factor data security protection has become a research hotspot in smart ocean management. With the increasing popularity of smart ocean management, how to achieve the two-factor protection of public data resources in smart ocean management is a serious problem to be tackled. Furthermore, how to achieve both security and revocation is also a challenge for two-factor protection. In this paper, we propose a two-factor-based protection scheme with factor revocation in smart ocean management. The proposed scheme allows data owners (DOs) to send encrypted messages to users through a shipboard server (SS). The DOs are required to formulate access policy and perform attribute-based encryption on messages. In order to decrypt, the users need to possess two factors. The first factor is the user’s secret key. The second factor is security equipment, which is a sensor card in smart ocean system. The ciphertext can be decrypted if and only if the user gathers the key and the security equipment at the same time. What is more, once the security equipment is lost, the equipment can be revoked and a new one is redistributed to the users. The theoretical analysis and experiment results indeed indicate the security, efficiency, and practicality of our scheme. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
Sensors 2019, 19(1), 11; https://doi.org/10.3390/s19010011
Received: 21 November 2018 / Revised: 15 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
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Abstract
Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays [...] Read more.
Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays are challenging problems. Based on simulated annealing, the element locations of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints are optimized in this paper by minimizing the sum of PSLL. The robustness of the deployment-optimized spare planar array with mis-calibration is further considered. Numerical simulations show the effectiveness of the proposed solution. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Adaptive Kalman Filter-Based Single-Beacon Underwater Tracking with Unknown Effective Sound Velocity
Sensors 2018, 18(12), 4339; https://doi.org/10.3390/s18124339
Received: 9 November 2018 / Revised: 3 December 2018 / Accepted: 4 December 2018 / Published: 8 December 2018
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Abstract
In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine [...] Read more.
In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called “5-sv” model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the “5-sv” model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Self-Organized Fast Routing Protocol for Radial Underwater Networks
Sensors 2018, 18(12), 4178; https://doi.org/10.3390/s18124178
Received: 31 October 2018 / Revised: 20 November 2018 / Accepted: 21 November 2018 / Published: 28 November 2018
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Abstract
An underwater wireless sensor networks (UWSNs) is an emerging technology for environmental monitoring and surveillance. One of the side effects of the low propagation speed of acoustic waves is that routing protocols of terrestrial wireless networks are not applicable. To address this problem, [...] Read more.
An underwater wireless sensor networks (UWSNs) is an emerging technology for environmental monitoring and surveillance. One of the side effects of the low propagation speed of acoustic waves is that routing protocols of terrestrial wireless networks are not applicable. To address this problem, routing strategies focused on different aspects have been proposed: location free, location based, opportunistic, cluster based, energy efficient, etc. These mechanisms usually require measuring additional parameters, such as the angle of arrival of the signal or the depth of the node, which makes them less efficient in terms of energy conservation. In this paper, we propose a cross-layer proactive routing initialization mechanism that does not require additional measurements and, at the same time, is energy efficient. The algorithm is designed to recreate a radial topology with a gateway node, such that packets always use the shortest possible path from source to sink, thus minimizing consumed energy. Collisions are avoided as much as possible during the path initialization. The algorithm is suitable for 2D or 3D areas, and automatically adapts to a varying number of nodes, allowing one to expand or decrease the networked volume easily. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
Sensors 2018, 18(12), 4168; https://doi.org/10.3390/s18124168
Received: 1 November 2018 / Revised: 20 November 2018 / Accepted: 22 November 2018 / Published: 27 November 2018
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Abstract
Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid [...] Read more.
Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid obstacles is an urgent need. Moreover, the sensor nodes are typically powered by batteries, which are difficult to replace, restricting the network lifetime of UASNs. In this paper, an Energy-efficient and Obstacle-Avoiding Routing protocol (EOAR) is proposed not only to address the issue of marine animals acting as obstacles that interfere with communications, but also to balance the network energy according to the residual energy. In the EOAR protocol, when the current node perceives the existence of marine animals, the interference area of the animal-nodes is first calculated using the underwater acoustic channel model, and then the candidate forwarding relay set of the current node is obtained according to the constraint conditions. The optimal candidate forwarding relay is determined by a fuzzy logic-based forwarding relay selection scheme based on considering the three parameters of the candidate forwarding relay, which includes the propagation delay, the included angle between two neighbor nodes, and the residual energy. Furthermore, in order to solve the problem of energy waste caused by packet collision, we use a priority-based forwarding method to schedule the packet transmission from the candidate forwarding relay to the destination node. The proposed EOAR protocol is simulated on the Aqua-sim platform and the simulation results show that proposed protocol can increase the packet delivery ratio by 28.4% and 11.8% and can reduce the energy consumption by 53.4% and 32.7% and, respectively, comparing with the hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR). Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar
Sensors 2018, 18(11), 4013; https://doi.org/10.3390/s18114013
Received: 12 October 2018 / Revised: 12 November 2018 / Accepted: 13 November 2018 / Published: 17 November 2018
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Abstract
A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson–Lucy (R–L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible [...] Read more.
A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson–Lucy (R–L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible for real-time high-resolution beamforming in a multibeam sonar system. This paper applies the new fast deconvolved beamforming algorithm to a high-frequency multibeam sonar system to obtain a high bearing resolution and low side lobe. In the sounding mode, it restrains the tunnel effect and makes the topographic survey more accurate. In the 2D acoustic image mode, it can obtain clear images, more details, and can better distinguish two close targets. Detailed implementation methods of the fast deconvolved beamforming are given, its computational complexity is analyzed, and its performance is evaluated with simulated and real data. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction
Sensors 2018, 18(11), 3797; https://doi.org/10.3390/s18113797
Received: 10 October 2018 / Revised: 27 October 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
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Abstract
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths [...] Read more.
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle A Combined Ray Tracing Method for Improving the Precision of the USBL Positioning System in Smart Ocean
Sensors 2018, 18(10), 3586; https://doi.org/10.3390/s18103586
Received: 31 August 2018 / Revised: 19 October 2018 / Accepted: 19 October 2018 / Published: 22 October 2018
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Abstract
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error [...] Read more.
The ultra-short baseline positioning system (USBL) has the advantages of flexible application and easy installation, and it plays an extremely important role in the underwater positioning and communication. The error of the USBL in underwater positioning is mainly caused by a ranging error due to ray tracing, a phase difference error of the USBL, and acoustic noise in the underwater communication. Most of these errors are related to the changes in the sound speed during its propagation through the ocean. Therefore, when using the USBL for underwater detection, it is necessary to correct the sound speed profile in the detection area and optimize the ray tracing. Taking into account the actual conditions, this paper aims at correcting the model of underwater sound speed propagation and improving the tracking method of sound lines when the marine environment in the shallow sea area changes. This paper proposes a combined ray tracing method that can adaptively determine whether to use the constant sound speed ray tracing method or the equal gradient ray tracing method. The theoretical analysis and simulation results show that the proposed method can effectively reduce the error of slant distance in USBL compared with the traditional acoustic tracking method and the constant sound speed ray tracing method. The proposed sound ray correction algorithm solves the contradiction between the number of iterations and the reduction of positioning error and has engineering application value. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Integration of Underwater Radioactivity and Acoustic Sensors into an Open Sea Near Real-Time Multi-Parametric Observation System
Sensors 2018, 18(8), 2737; https://doi.org/10.3390/s18082737
Received: 17 July 2018 / Revised: 16 August 2018 / Accepted: 17 August 2018 / Published: 20 August 2018
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Abstract
This work deals with the installation of two smart in-situ sensors (for underwater radioactivity and underwater sound monitoring) on the Western 1-Mediterranean Moored Multisensor Array (W1-M3A) ocean observing system that is equipped with all appropriate modules for continuous, long-term and real-time operation. All [...] Read more.
This work deals with the installation of two smart in-situ sensors (for underwater radioactivity and underwater sound monitoring) on the Western 1-Mediterranean Moored Multisensor Array (W1-M3A) ocean observing system that is equipped with all appropriate modules for continuous, long-term and real-time operation. All necessary tasks for their integration are described such as, the upgrade of the sensors for interoperable and power-efficient operation, the conversion of data in homogeneous and standard format, the automated pre-process of the raw data, the real-time integration of data and metadata (related to data processing and calibration procedure) into the controller of the observing system, the test and debugging of the developed algorithms in the laboratory, and the obtained quality-controlled data. The integration allowed the transmission of the acquired data in near-real time along with a complete set of typical ocean and atmospheric parameters. Preliminary analysis of the data is presented, providing qualitative information during rainfall periods, and combine gamma-ray detection rates with passive acoustic data. The analysis exhibits a satisfactory identification of rainfall events by both sensors according to the estimates obtained by the rain gauge operating on the observatory and the remote observations collected by meteorological radars. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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Open AccessArticle Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
Sensors 2018, 18(8), 2568; https://doi.org/10.3390/s18082568
Received: 20 May 2018 / Revised: 31 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
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Abstract
Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total [...] Read more.
Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme. Full article
(This article belongs to the Special Issue Smart Ocean: Emerging Research Advances, Prospects and Challenges)
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