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The detection of minerals (mines) or rocks would have been extremely difficult without the expansion of the Sound Navigation Ranging methodology, which uses specific parameters to determine if a barrier or a surface is a mine or rock. Hence, this proposed work is concerned with the progression of distinctive among metal cylinder which is named as mines and cylindrical shape material which is named as rocks using deep learning based algorithms. Moreover, this work introduced novel technique as Rock or mine Detection Neural Network for performing rock/mine prediction and classification in underwater acoustics. The proposed RDNN method outperforms the outcomes by attaining high accuracy as 92.85% mean accuracy that makes better model performance.

Object detection in underwater acoustics especially sea floor object has been overwhelming mission chiefly owing to strident environment of sonar images as well as because of visibly existing sonar images. Side Scan Sonar is the primary sensor for Autonomous Underwater Vehicles to perform survey on sea water. Hence, we are using this SSS images for categorizing several objects like sand, mud, clay, graves, ridges and sediments in underwater sea through any size subsequent to training. We applied two-layer CNN architecture to train the model as well as we utilized three pre-trained network models such as VGG-19, ResNet50 and EfficientNet model for evaluating the performance of the model based on training and validation accuracy measures. Moreover, we utilized deep learning based transfer learning approach in which the parameters are tuned for classifying the images into sediments, clay, mud, stones etc. Our experimental outcomes shows that pre-trained EfficientNet model generates better accuracy of 100% after fine tuning the parameter in object recognition along with classification using SSS images.

Underwater acoustic sensor networks (UASNs) have gradually received attention due to their widespread applications, such as in disaster prevention, environmental monitoring and military activities, and they simultaneously face security challenges. In recent years, trust management mechanisms have become important tools for responding to internal attackers. However, most of the existing trust management mechanisms do not consider the adverse influence of complex underwater environment on node evaluation, and do not deal with the case that normal nodes are misjudged as malicious nodes. To achieve accurate trust evaluation of nodes and reduce the possibility of misjudgment of normal nodes, a redeemable Support Vector Machine-Dempster-Shafer (SVM-DS) fusion-based trust management mechanism (SDFTM) is proposed for UASNs in this paper. First, according to the characteristics of attacks, three kinds of trust evidence are selected: packet-based evidence, data-based evidence and energy-based evidence. Then the support vector machine (SVM) is applied to classify the trust of node from the aspect of each kind of trust evidence, while Dempster-Shafer (DS) evidence theory is used to fuse the different trust classification results of nodes. Second, to deal with cases where a normal node may be misclassified as a malicious node, trust redemption process is carried out based on the historical performance and environmental influence (unreliable acoustic channel, weak link connectivity). Finally, the trust value is calculated and updated. The simulation results prove that the proposed scheme yields satisfactory performance in malicious detection and reduce the possibility of misjudgment of normal nodes.

Applying passive sonar to classify underwater acoustic targets at different depths is a challenging task. Although the self-contained hydrophone array can ensure the normal operation of most units in various environments, it is arduous to achieve precise time synchronization between each hydrophone, which results in difficulties in data fusion between hydrophones. For a vertical sonar array composed of self-contained units, a deep learning-based data compression and multihydrophone fusion (DCMF) model is proposed to quickly extract acoustic propagation interference features, which are used for underwater acoustic target classification. Unlike the frequency-range domain striation features acquired by long-term accumulation, this paper exploits the depth difference between multiple hydrophones to obtain the frequency-depth domain joint striation features in a short time. The proposed DCMF conducts efficient feature compression and fusion via parallel stacked sparse autoencoders and a multi-input fusion network. The experimental results illustrate that the compressed features have strong robustness, a low mean square error with the simulation results, and shorter signal length requirements, which improves the classification efficiency and real-time performance of DCMF. In the case of the experimental dataset, DCMF is compared with several state-of-the-art multiscale fusion models, and the experiments indicate that DCMF has the best performance and smallest computational complexity.

In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal, this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected.

Acquiring accurate channel state information and mitigating severe intersymbol interference are challenging for underwater acoustic communications with moving transceivers due to the rapid changes of the underwater acoustic channels. In this work, we address the issue using a superimposed training (ST) scheme with a powerful channel estimation method. Different from the conventional time-multiplexed training, training sequences with a small power are superimposed with symbol sequences. The training signals are transmitted over all time, leading to enhanced tracking capability to deal with time-varying channels at the cost of only a small power loss. To realize this, based on the belief propagation, we develop a message-passing-based bidirectional channel estimation (BCE) algorithm, where all messages are Gaussian, enabling efficient implementation. In particular, the channel correlations are fully exploited through a forward recursion and a backward recursion, thereby achieving accurate channel estimation. Moreover, the ST-based BCE is combined with channel equalization (in the frequency domain) and decoding, and they are performed jointly in an iterative manner to significantly enhance the overall system performance. Field experiments were carried out in Jiaozhou Bay in 2019, and the results verify the effectiveness of the proposed scheme and algorithm.

In order to better explore the ocean, autonomous underwater vehicles (AUVs) have been widely applied to facilitate the information collection. However, considering the extremely large-scale deployment of sensor nodes in the Internet of Underwater Things (IoUT), a homogeneous AUV-enabled information collection system cannot support timely and reliable information collection considering the time-varying underwater environment as well as AUV’s energy and mobility constraints. In this article, we propose a multi-AUV-assisted heterogeneous underwater information collection scheme for the sake of optimizing the peak Age of Information (AoI). Moreover, the limited service M/G/1 vacation queueing model is utilized to model the process of information exchange, where the optimal upper limit of the number of AUVs served in the queueing system as well the steady-state distribution of the queue length are derived. A low-complexity adaptive algorithm for adjusting the upper limit of the queuing length is also proposed. Finally, simulation results validate the effectiveness of our proposed scheme and algorithm, which outperform traditional methods in terms of the peak AoI.

With the extensive application of underwater acoustic sensor networks (UANs) in various fields such as commerce, marine environmental research, and national defense, the need for an autonomous and well-organized underwater acoustic network has been increasing. Topology discovery is a crucial step in constructing an underwater acoustic network, and node discovery and topology establishment are the essential components of the topology discovery process in UANs. This paper introduces the characteristics of underwater acoustic channels and networks and highlights their influences on topology discovery. We discuss the topology discovery protocol development in terrestrial networks (i.e., duty-cycle ad hoc network, Internet of things). The main focus of this paper is to study the topology discovery protocols of UANs. This paper also classifies and introduces the existing topology discovery protocols and compared their advantages and disadvantages to understand the current topology discovery methods. Furthermore, we also discuss the topology discovery protocol’s influence on different layers’ functions in the UAN protocol stack. Analyze the current research challenges in this field, followed by important open issues in UAN protocol development, which provide new opportunities for further research.

The black box plays an important role in transportation security, and the underwater acoustic localization (UWAL) methods are the most effective way to find the black box sunken undersea. The traditional UWAL methods suffer from a significant decrease in the localization precision due to the signal period drift of the black box in the cold and high-pressure undersea environment. In our former work, we carried out the second-order time-difference-of-arrival (STDOA) algorithm to address the problem. However, for a real maneuvering ship, the localization precision has the potential to be further improved with the help of the uniformly distributed localization nodes. In this article, a generalized STDOA (GSTDOA) algorithm is proposed. First, we establish the general form of the STDOA information, where the localization nodes can be selected arbitrarily. Second, we analyze the localization precision of the GSTDOA algorithm, and it shows that the localization precision is closely related to the selected nodes. Third, we optimize the localization nodes for maximum localization precision. Finally, experiments in different scenes validate the effectiveness of the proposed method.

Underwater acoustic communication (UAC) modem is an important infrastructure of underwater network construction. In recent years, with the performance improvement of the STM32 processor, the realization of reliable UAC through high-performance STM32 processor is conducive to reduce the system power consumption, cheapen the hardware cost, and ease the development difficulty. In this letter, we propose an embedded UAC system with the STM32H743 processor as the core and the peripheral sending/receiving circuit as the signal conditioning circuit module. The system can support a variety of modulation and demodulation methods, including single/multi-carrier frequency-shift keying and orthogonal frequency division multiplexing. Furthermore, in order to reduce the computational cost of the system, a fast and robust frame synchronization algorithm based on the segmented fast Fourier transform is applied. The sea trials show that the system can realize the reliable UAC transmission of 100 b/s–1 kb/s in the distance of 5–8 km in the shallow water area.

In this article, we present an unmanned aerial vehicle (UAV)-aided ocean monitoring network for remote oceanic data collection, in which monitoring data are transmitted first from battery-powered underwater sensor nodes (USNs) to sea surface sink nodes (SNs) in a data collection cycle using underwater acoustic communication, and then a UAV hovering in air collects all the data from SNs and relays them to a ground base station via wireless communication links. Aiming at maximizing network lifetime, we model the resource allocation, USN-to-SN access, and SN-to-UAV access issues as a mixed-integer nonconvex optimization problem. To efficiently solve it, we decompose the optimization into two stages. The first stage is to minimize time consumption in an SN-to-UAV nonorthogonal multiple access process and we solve it by designing a UAV deployment scheme, a subchannel matching scheme, and a joint power and time allocation scheme, based on which, the second stage is to maximize the residual energies of USNs in USN-to-SN transmissions under a modified frequency-division multiple access strategy in each collection cycle. The second-stage optimization is further decomposed into some similar subproblems, and each of them is considered as a bipartite graph matching problem between USNs and underwater acoustic channels. For each subproblem, we propose improved weight-based matching and bisection-based searching algorithms. Finally, we design a low-complexity iteration algorithm to approximate the optimal solution of the original problem by solving these subproblems. The simulation results validate the effectiveness of our proposals.

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models.

The long propagation delay presents a nonnegligible impact on medium access control (MAC) in underwater acoustic networks (UANs), leading to the coupling of spatial propagation delay and transmission time in determining frame collisions at receivers. To this end, this article first reveals the space-time coupling relationship for collision-free transmissions. We find that the interference region for simultaneous transmissions is an annulus, which is much smaller than the whole transmission range. Thus, UANs have more spatial multiplexing opportunities than terrestrial radio networks. By deriving the collision probabilities of the ALOHA-based protocols, we show that the annulus interference region in UANs provides potential to improve random access protocols. We further derive the heuristics for optimal scheduling for periodical transmissions under space-time coupling, which is formulated as a mixed-integer linear programming problem. The obtained heuristics are then used to design low-complexity algorithms, which are verified by simulation results. Our proposed algorithms can achieve the optimal scheduling in star networks and outperform the existing MAC in mesh networks.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime. This work was supported in part by the National Natural Science Foundation of China under Grant 61972424; in part by JSPS KAKENHI under Grant JP20F20080; and in part by the Special Fund for Basic Scientific Research of Central College, SCUEC, under Grant CZT20025.

Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique--Deep Belief Network (DBN)-- to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at $10^{-3}$ Bit Error Rate (BER).

Underwater acoustic sensor networks (UASNs) have been proposed as a promising way in supporting the Underwater-Internet-of-Things (UIoT) applications. However, to guarantee and improve Quality of Service (QoS), they are still facing great challenges especially when it comes to enabling reliable data communication for the UIoT applications and meanwhile protecting the marine ecosystems for sustainable underwater monitoring and exploration; this is because the acoustic signals used by UASNs can do harmful interference to vocalizing marine mammals. Therefore, the article proposed an ecologically friendly data transmission scheme for UASNs, which adopts an interference-aware opportunistic route discovery method and a frequency-division multiplexing (FDM)-based full-duplex communication scheme. First, in the route discovery phase, a virtual-void zone scheme, an FDM-based channel allocation approach and a Bayesian network-based mammal avoidance strategy is introduced to establish interference-free routing paths for enabling reliable and environmentally friendly data transmissions. Then during the data transmission phase, an FDM-based full-duplex communication technique is adopted to enable high-speed data flow from the seabed to the surface. Extensive simulations indicate that the proposed scheme has excellent advantages in terms of QoS while also taking marine mammals into account since the signal interference to both underwater nodes and nearby vocalizing mammals is significantly mitigated.

Multiple features can be extracted from time-frequency representation (TFR) of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time-frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals, and large sequences of harmonics and pulsed bursts from common whales.

International audience; As the mining of deep-sea natural deposits is becoming cost competitive compared to similar land-based mining, companies have started to dig into the seabeds to collect minerals. However, the acoustic contribution of this activity in the surrounding environment can be significant. To predict the impact of such noise, the starting point is to localize and quantify the sources that create it. In this study, a 3-D prototype acoustic array to perform this localization and quantification is designed, built, and deployed at sea for validation of its localization capacities. The design method performs a two-step study to define the array shape and select the hydrophone arrangement over it, under harsh constraints. Each step relies on two metrics to rank the candidates: the maximum sidelobe level, and the spatial resolution. These are computed on conventional beamforming maps for simulated sources that represent excavation machines on the ground. The shape is first determined to be the one that yields steady maximum sidelobe value levels over frequency. Second, the hydrophone arrangement that achieves the lowest maximum sidelobe level while limiting the spatial resolution is selected. This leads to a tip down conical array with 21 hydrophones, of about 3 m in height and diameter, and this is manufactured and used during an experimental campaign in the Mediterranean Sea. The experimental localization maps show strong agreement between the estimated source position and its ground truth. A more detailed comparison between simulated and real performances confirms accurate array conception and realization. Thus, this design procedure provides an efficient underwater acoustic array for monitoring deep-sea mining, the localization capacities of which are validated in a real-life setting.

Security is a critical consideration in Underwater Acoustic Networks (UANs) due to the importance of the applications in which these types of networks are often employed, from military applications to marine natural disaster prevention. Furthermore, even simple Denial of Service (DoS) attacks such as jamming can be very effective in disrupting the communication, with significant negative consequences for these critical applications. While jamming has been widely studied in the context of terrestrial networks, the peculiarities of propagation in UANs, such as the low propagation speed, the multipath, and the high delay spread, need to be considered: the relative positions of the jammer, transmitter, and receiver can have a huge impact on the feasibility and impact of reactive jamming, opening the way for the exploitation of other jamming models. In this paper, we analyze the effectiveness of a reactive and a blind jammer through a game theoretical framework, comparing them for different geometries of the scenario. We assess the impact of the different jammers, employing different active and evasive strategies, where the first type of countermeasure implies the use of additional energy to protect the communication, while the second tries to avoid the jamming signals by randomizing the transmission pattern.

In this work, an angle of arrival (AoA) based statistical model for the path gain in an underwater (UW) channel is proposed considering the randomness of the line of sight and specular paths. The channel is assumed to be wide sense stationary (WSS), time-variant, and frequency-selective. The proposed model is used to obtain a novel characterization of a multi-hop multi-branch (MHMB) cooperative framework for a frequency-selective UW channel. In this general system architecture, multipath exists between any pair of consecutive hops. It is observed that when AoA is Gaussian distributed, then the path gain has a log-normal distribution. We use the Gauss Quadrature Rule (GQR) representation of the Moment Generating Function (MGF) of a log-normal distribution to derive the expressions for average bit error rate (BER) and ergodic capacity (EC) for a MHMB cooperative framework. Results for i) Pressure sensor (PS) as receiver and ii) Vector sensor (VS) as receiver for the dual-hop cooperative model are obtained and compared. Excellent matching between results acquired from Monte Carlo (MC) simulation and numerical integration (NI) expressions validate the accuracy of the derived expressions. Expectedly VS based communication systems perform better.

A key technology in the movement tracking of marine animals is localization using acoustic transmitters. These are attached to marine animals and are detected by an array of receivers. Then, offline localization is performed by multilateration. However, due to the transmitter's low power and environmental conditions, emissions may be detected by only a limited number of receivers, causing localization ambiguities to arise. This work proposes a solution for such localization ambiguities. The proposed method assumes that the position of acoustically-tagged marine animals follows a hidden Markov model, such that localization ambiguities can probabilistically be resolved using a Forward-Backward algorithm. Our method is able to extrapolate the positions in a data series, as long as one sample in that series is picked up by three receivers, or if the identity of the receivers changes during tracking. Performance analysis shows that the localization accuracy of our method approaches the Cramer-Rao lower bound. Furthermore, to demonstrate the suitability of our method in a real sea environment, we have established a testbed that operated for three months, demonstrating localization of 20 acoustically-tagged sandbar sharks. Compared to the available solutions, roughly 20 times more location estimates were made; thereby, significantly increasing the impact of the test-site.

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Underwater Acoustic Networks (UANs) use acoustic waves for communications, which consume excessive energy. Conversely, the nodes in UANs have scarce energy resources, thus energy saving is a vital objective of routing protocol design for UANs. Due to the high cost, nodes are usually deployed sparsely. Moreover the nodes in water can move with water current or other underwater activities. Consequently, the data transmission with traditional routing protocol is subject to the routing problem of “void area” in UANs. In this paper, we propose a Connectivity-and-Energy-aware Layering Routing (CELR) protocol to achieve two objectives of energy-saving and reliable routing. Addressing the routing problem of “void area”, a connectivity-aware layering algorithm is presented based on the minimum hop-count from the sink to underwater nodes. To reduce the energy consumption, on the basis of connectivity aware layering algorithm, a connectivity and energy aware layering algorithm is proposed. Further, taking the residual energy and hop-distance into account, we present a CELR routing protocol for UANs. Experiments results show that the CELR protocol outperforms the depth-based routing (DBR) and layer-based and energy-efficient routing (LEER) protocol in terms of packet delivery rate, average energy consumption and end-to-end delay.

Despite the potential benefits of Internet of Underwater Things, a number of issues hinder its realization, including the need for communication reliability and cost-effectiveness. This article aims to optimize network design to implement cost-effective underwater acoustic sensor networks (UASNs) with 3D topology while supporting diverse communication quality of service (QoS) requirements. First, we present an analytical framework based on a queueing system that evaluates communication performances of UASNs, wherein each underwater sensor distributed within a 3D space under the sea surface performs fountain code (FC)-based automatic repeat request (ARQ) transmissions under the slotted-Aloha medium access control protocol. Under the proposed framework, we evaluate communication performances given in terms of successful FC-based ARQ transmission probability and the average queueing delay of an underwater sensor. When evaluating the performances, we formulate the service time of each underwater sensor as a function of network parameters, i.e., the density of data sink and amount of redundancy for FC-based ARQ transmission, before solving a function for accurate service time, such that each sensor can be represented by an M/G/1 queue. Further, our analysis can formulate an optimization problem that aims at minimizing total cost incurred to install and operate 3D UASNs, without compromising two communication QoS requirements. To solve this problem, we propose a recursive algorithm to approach an optimal solution in reasonable time. Numerical evaluations demonstrate the validity of the proposed algorithm.

Received signal strength (RSS)-based ranging is a promising distance estimation approach in underwater acoustic sensor networks (UASNs). However, the multipath-rich underwater environment complicates acoustic propagations and derails the RSS-based ranging. To address the challenges, this article provides a novel ranging method, called channel state information (CSI)-based ranging for UASNs (CRUN). Instead of RSS, the measured CSI is modeled as a set of power-loss-based equations. Then, the ranging process under multipath scenarios is transformed as a multivariate optimization problem which involves parameters of all propagation paths. This optimization problem aims to simultaneously realize distance estimation and multipath mitigation. Noticing the large number of variables makes the solution numerically unstable, a threshold-window-based algorithm is proposed to simplify the multivariate optimization problem. In specific, the proposed algorithm extracts relative amplitude attenuations and relative time delays between the line-of-sight (LOS) path and each of the non-line-of-sight paths from CSI. The extracted parameters, being as equality constraints, simplify the multivariate optimization problem to a univariate optimization problem only concerning the desired LOS distance. Then, the simplified problem can be efficiently solved by the gradient descent algorithm. Statistical-channel-model-based simulations and lake experiments demonstrate that CRUN significantly improves the ranging accuracy and robustness compared with RSS-based approaches.

Cross-ice acoustic information transmission is an effective means of communication in polar sea areas covered by ice. However, the channel is extremely complicated because of the combined influence of water, ice, and air. Based on the normal-mode theory, this paper establishes a cascade acoustic channel (CAC) model for the transmission of under-water acoustic waves across ice layer. The model cancalculate the displacement response of the ice layer's upper surface by separating the upward waves from normal modes in the water and multiplying it by at ransmission coefficient matrix. The relationship between the displacement response of the upper surface of ice layer and the acoustic frequency is calculated by the finite-element method, and the calculation result was consistent with that of the CAC model. To verify the applicability of the model, a cross-ice acoustic communication experiment was conducted in Songhua River in January 2019. Experimental results show the energy of the acoustic signals received by geo-phones is closely related to sound frequency and cross-ice acoustic communication is feasible. The result of present research is important for understanding cross-ice acoustic channel characteristics and developing future cross-ice acoustic communication in polar sea areas.

Underwater sound in the oceans has been significantly rising in the past decades due to an increase in human activities, adversely affecting the marine environment. In order to assess and limit the impact of underwater noise, the European Commission’s Marine Strategy Framework Directive (MSFD) included the long-term monitoring of low-frequency underwater sound as a relevant indicator to achieve a good environmental status. There is a wide range of commercial hydrophones and observing platforms able to perform such measurements. However, heterogeneity and lack of standardization in both hydrophones and observing platforms makes the integration and data management tasks time-consuming and error-prone. Moreover, their power and communications constraints need to be addressed to make them suitable for long-term ocean sound monitoring. Measured underwater sound levels are challenging to compare because different measurement methodologies are used, leading to a risk of misunderstandings and data misinterpretation. Furthermore, the exact methodology applied is not always public or accessible, significantly reducing ocean sound data re-usability. Within this work, a universal architecture for ocean sound measurement is presented, addressing hydrophone integration, real-time in situ processing and data management challenges. Emphasis is placed on generic and re-usable components, so it can be seamlessly replicated and deployed in new scenarios regardless of the underlying hardware and software constraints (hydrophone model, observing platform, operating system, etc.). Within the proposed architecture, a generic implementation of an underwater sound algorithm based on underwater noise measurement best practices is provided. Standardized and coherent metadata with emphasis on strong semantics is discussed, providing the building blocks for FAIR (Findable, Accessible, Interoperable, Reusable) ocean sound data management. This work has been funded by the Spanish government project RESBIO (grant agreement TEC2017-87861-R), the EU projects ENVRI-FAIR (grant agreement 824068), the JONAS INTERREG Atlantic Area-funded project (EAPA_52/2018), partly by the Generalitat de Catalunya SARTI-MAR project (grant agreement 2017 SGR 371) and partly by the FPI-UPC_2015 scholarship program.

Reliable acoustic communication between submersibles and surface vessels plays a critical role in deep-sea exploration. Adaptive turbo equalization can effectively combat the selective fading of underwater acoustic channels, thereby becoming one of the enabling technologies for single-carrier deep-sea vertical acoustic communications. Existing adaptive turbo equalizer designs are usually based on a minimum-mean-squared-error criterion or a minimum-mean-absolute-error criterion. These criteria are inherently suboptimal with respect to the achievable symbol error rate (SER). In this article, an improved proportionate normalized minimum-SER (IPNMSER) algorithm is proposed for adaptive turbo equalization in deep-sea vertical acoustic communications. The proposed algorithm utilizes the minimum-SER (MSER) criterion to derive the equalizer update equations, aiming to minimize the system's SER directly. In addition, because the deep-sea vertical channel has a sparse structure, which leads to a sparse equalizer, a sparsity-aware proportionate-type approach is therefore incorporated into the framework of the MSER criterion to achieve faster convergence. To investigate the effectiveness of the proposed algorithm, we conducted a deep-sea vertical acoustic-communication experiment in the Challenger Deep of the Mariana Trench. The results demonstrated that the proposed IPNMSER algorithm can outperform a conventional normalized MSER algorithm and other well-known proportionate-type algorithms, achieving error-free detection for all data blocks over a vertical communication range of approximately 10500 m.

Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.

In this article, a novel robust Gaussian approximation smoother based on expectation–maximization (EM) algorithm is proposed for cooperative localization (CL) with faulty Doppler velocity log (DVL) and heavy-tailed measurement noise. In our model, the autonomous underwater vehicle (AUV) velocity information that is not available due to DVL failure and the bias in the underwater acoustic modem are considered as unknown inputs. Then, the Student’s t distribution is used to model the heavy-tailed measurement noise. An EM algorithm is also developed for the state-space model with heavy-tailed measurement noise. The state, noise covariance matrices, and auxiliary random variables are regarded as hidden variables to obtain the maximum likelihood estimation of unknown inputs. Gaussian smoother where modified process and measurement noise covariance are inferred by variational Bayesian (VB) approach is applied to estimate the state. The experimental results illustrate that given the heavy-tailed noise, the proposed method estimates the unknown input, and the state with a high level of accuracy. The proposed algorithm can be used as a backup algorithm for CL in cases where DVL is not available due to failure.

Contention-basedmedium access control (MAC) protocols for underwater acoustic sensor networks are designed to handle packet collisions that are caused by long propagation delays. However, existing protocols are known to suffer from relatively high collisions, which decrease system performance. To enhance system performance, we propose a contention-based MAC protocol that employs a widely-popular machine learning technique, namely, Q-learning. Using Q-learning, the proposed protocol allows the sensor nodes to intelligently select the back-off slots and accordingly schedule the transmission of data packets such that collisions are minimized at the receiver. Unlike in existing protocols, the sensor nodes are not required to exchange scheduling information, which implies that the proposed protocol has low complexity and overhead. Under varying traffic loads and node numbers, the proposed protocol is compared with the state-of-the-art ALOHA-Q for underwater environment (UW-ALOHA-Q), multiple access collision avoidance for underwater (MACA-U) and exponential increase exponential decrease (EIED) protocols. Results demonstrate the effectiveness of the proposed protocol in terms of energy efficiency, channel utilization, and latency.

Acquiring channel state information and mitigating multi-path interference are challenging for underwater acoustic communications under time-varying channels. We address the issues using a superimposed training (ST) scheme with a least squares (LS) based channel estimation algorithm. The training sequences with a small power are linearly superimposed with the symbol sequences, and the training signals are transmitted over all time, resulting in enhanced tracking capability to deal with time-varying underwater acoustic channels at the cost of only a small power loss. To realize the full potentials of the ST scheme, we develop a LS based channel estimation algorithm with superimposed training, where the Toeplitz matrix is used, which is formed by the training sequences, enabling channel estimation with superimposed training. In particular, a low-complexity channel equalization algorithm based on generalized approximate messaging passing (GAMP) is proposed, where the a priori, a posteriori, extrinsic means and variances of interleaved coded bits are computed, and then convert them into extrinsic log likelihood ratios for BCJR decoding. Its computational complexity is only in a logarithmic order per symbol. Moreover, the channel estimation, GAMP equalization and decoding are performed jointly in an iterative manner, so that the estimated symbol sequences can also be used as virtual training sequences to improve the channel estimation and tracking performance, thereby remarkably enhance the overall system performance. Moving communication experiments in Jiaozhou Bay (communication frequency 12 kHz, bandwidth 6 kHz, sampling frequency 96 kHz, symbol transmission rate 4 ksym/s) were carried out, and the experimental results verify the effectiveness of the proposed technique.

A novel fully quadrature subcarrier-index shift keying orthogonal frequency division multiplexing (FQSISK-OFDM) modulation scheme for underwater acoustic communications (UWAC) is proposed in this paper. In the FQSISK, the data is conveyed by active and non-active OFDM-subcarriers indices. Moreover, the active odd and even subcarriers are marked with separable predefined dummy variables (DV). Herein, the DV $\pm \delta $ (i.e., it can take any predefined value), and its quadrature $\pm j\delta $ are exploited to distinguish between active even and odd subcarriers. Hence, the possibility of power leakage from active to non-active subcarriers is reduced, which minimizes the effect of severe UWAC inter-carrier-interface (ICI). Moreover, the spectral efficiency of the FQSISK hardly depends on the subcarriers grouping size, which reduces the proposed FQSISK decoder complexity significantly. The simulation results demonstrate the proposed FQSISK superiority, as it achieves higher spectral and energy efficiencies than the plain OFDM and the recently introduced OFDM index modulation (OFDM-IM) techniques.

In this work, we focus on the model-mismatch problem for model-based subspace channel tracking in the correlated underwater acoustic channel. A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model. Even though the data support the assumption that the model is slow-varying and uncorrelated to some degree, to improve the tracking performance further, we can not ignore the model-mismatch problem because most channel models encounter this problem in the underwater acoustic channel. Therefore, in this work, we provide a dynamic time-variant state-space model for underwater acoustic channel tracking. This model is tolerant to the slight correlation after decorrelation. Moreover, a forward-backward Kalman filter is combined to further improve the tracking performance. The performance of our proposed algorithm is demonstrated with the same at-sea data as that used for conventional channel tracking. Compared with the conventional algorithms, the proposed algorithm shows significant improvement, especially in rough sea conditions in which the channels are fast-varying., Comment: Submitted to IEEE Access

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

Underwater acoustic communication plays an important role in underwater wireless sensor network. Different from terrestrial radio channel, modeling of underwater acoustic channel is very challenging due to its unique and dynamic characteristics. In order to verify the proposed acoustic communication algorithms, a general emulation platform which can reduce the verification cost has a wide application prospect. In this paper, we design a general hardware in the loop underwater communication emulation system based on the LabVIEW and MATLAB software and real acoustic transducer. In the emulation system, LabVIEW calls MATLAB script to achieve algorithm implementation, and then uses DAQmx to send and receive data through the acquisition card. The proposed system supports a variety of modulation and demodulation methods, such as multiple frequency-shift-keying(MFSK), multi-carrier frequency-shift-keying(MCFSK), orthogonal frequency division multiplexing(OFDM), etc. In addition, in order to detect frame efficiently, we propose a light-weight frame synchronous algorithm. To verify the function of the systems, lake test and sea test are conducted for different algorithms.

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe absorption of light in the medium, the communication range is short in underwater optics. Conversely, acoustics suffers from low data rate and high power consumption, but provides longer communication ranges. Since most underwater equipment relies on battery power, energy-efficient communication is critical for reliable underwater communications. In this work, we derive analytical models for both underwater acoustics and optics, and calculate the required transmit power for reliable communications in various underwater communication environments. We then formulate an optimization problem that minimizes the network power consumption for carrying data from underwater nodes to surface sinks under varying traffic loads and weather conditions. The proposed optimization model can be solved offline periodically, hence the additional computational complexity to find the optimum solution for larger networks is not a limiting factor for practical applications. Our results indicate that the proposed technique yields up to 35% power savings compared to existing opto-acoustic solutions.

Underwater acoustic target recognition is one of the main functions of the SONAR systems. In this paper, a target recognition method based on combined features with automatic coding and reconstruction is proposed to classify ship radiated noise signals. In the existing underwater acoustic target recognition systems, the target category features are mostly constructed based on the power spectrum according to a certain presupposed model, and some useful information in the data is discarded artificially. In the proposed recognition method, a feature extractor based on auto-encoding is designed. The feature extractor uses the restricted Boltzmann machine (RBM) to automatically encode the combined data of the power spectrum and demodulation spectrum of ship radiated noise without supervision and extracts the deep data structure layer by layer to obtain the signal feature vector. The extracted feature vector is sent to a Back Propagation (BP) neural network to realize target recognition. Due to the high cost of ship radiated noise acquisition, the sample size of ship radiated noise signals is often hard to meet the needs of neural network training. A method of data augmentation is designed by RBM auto-encoder to construct the expanded sample set, which improves the performance of the recognition system. The experimental results based on the actual ship’s radiated noise show that the proposed method has better performance than the traditional methods.

The localization and tracking of underwater objects have many applications. In a proactive underwater sensor array, some nodes will periodically broadcast linear frequency modulated (LFM) signals, which will hit the targets, get reflected and received by the other nodes. Depending on the target's position and velocity, the received signals will also be LFM signals of different frequencies and frequency rates. We can use the Fractional Fourier Transform (FrFT) to analyze the received signal's spectrum and find the peak. Based on the location of the peak, the target's distance and radial velocity can be estimated. However, the accuracy is highly dependent on the sampling interval of the spectrum. Smaller sampling interval leads to higher accuracy but also induces considerable complexity. To overcome this issue, we propose a machine learning-based approach to automatically detect the existence of the target, and roughly estimate the peak's location if targets exist. Then over-sampling can be conducted for a small area around the peak, leading to improved accuracy and reduced complexity. The idea is based on the following observation: if a target exists, we will be able to observe an “X” pattern on the spectrum. Extensive simulations are conducted to verify the effectiveness of the proposed architecture.

The technology of underwater acoustic communication plays an important role in linking the underwater sensors for data collection and networking. Due to simultaneous presence of direct path as well as reflections upon static or dynamic boundaries, underwater acoustic (UWA) channel exhibits hybrid sparse, i.e., consists of relatively static multipath arrivals and time varying ones. While the classic sparse channel estimation algorithms generally do not take this type of hybrid sparsity into account, dynamic compressed sensing (DCS) technique enables the exploitation of time varying sparsity by incorporating CS with Kalman filtering, namely, Kalman Filtered Compressed Sensing (KF-CS). However, by assuming the whole UWA channel as sparse set with time varying support, static sparse components will unavoidably lead to model mismatch and performance degradation. In this paper, by formulating the hybrid multipath UWA channels as sparse set consisting of static and time varying supports, a static-dynamic discriminative compressed sensing (SDD-CS) approach is proposed to explore the hybrid sparsity. Hybrid multipath arrivals are firstly decomposed into static and time varying components by discriminative orthogonal matching pursuit (DOMP), and then the magnitude of time varying and static multipath is estimated by KF-CS and SOMP respectively, after that estimated results of static and time varying component are summed up to obtain the whole channel response. Numerical simulations verify the superiority of the proposed SDD-CS algorithm. Finally experimental results obtained from a shallow water acoustic communication scenario are provided to demonstrate the effectiveness of the proposed algorithm, compared to conventional algorithms.

Node deployment plays a vital role in Underwater Acoustic Sensor Networks (UASNs) and is closely related to many network tasks, such as routing protocol design and topology control. Coverage and connectivity are two important indicators for node deployment. However, separately improving one of the indicators could lead to the decline of another indicator. Therefore, coverage and connectivity need to be jointly optimized. In this paper, we propose a Voronoi-based Optimized Depth Adjustment (VODA) deployment scheme to deploy sensor nodes in a target water space. First, gateway nodes collect the coordinates of sensor nodes to establish a Voronoi diagram. Based on this diagram, some of sensor nodes are selected to remain on the water surface, which are called leader nodes. The remaining nodes need to sink to different depths so that reduce the coverage overlap between the nodes. According to graph coloring theory, each sensor node is assigned a layer label (LID) which represents a relative position. Finally, the nodes calculate their depths in the order of LIDs from small to large. Simulation results show that compared with three other schemes, VODA can always get higher coverage in different cases, while the connectivity is maintained at 100 % in the proposed scenarios. Meanwhile, VODA can help the networks get a longer lifetime.

Internet of Underwater Things (IoUT) is regarded as the network of interconnected smart underwater objects that enables marine monitoring and exploration. Underwater acoustic sensor networks (UASNs) is an ideal network infrastructure to support IoUT. Usually, network-level analysis requires a simplified model for channel interference. Existing research on UASNs usually applies channel interference models from terrestrial wireless networks. Its effectiveness is questionable. In this article, we investigate two specific features of acoustic channels and propose a new channel interference model. First, based on the fact that acoustic signals have inconsistent transmission range in vertical and horizontal directions, we propose an ellipsoid sensing model and derive the probability that a communication pair will have an effective link. Second, we investigate the receiving time inconsistency in time-slotted acoustic networks and derive the analytical probability of collision-free transmission. Finally, a new protocol model is proposed to describe the acoustic channel interference. Its theoretical throughput, in terms of delivery rate, is also presented. Numerical examples illustrate the effectiveness of the theoretical analysis.

Long-range underwater acoustic communication (LR-UWAC) refers to the peer-to-peer transmission of messages for distances of tens to hundreds of km. It is a key enabling technique for applications such as control over unmanned underwater vehicles for long-term surveying. While underwater acoustic communication over shorter ranges is an established technique, this is not the case for LR-UWAC. This gap is mostly due to channel uncertainties: in the absence of feedback from the receiver and due to the long transmission range, channel state information (CSI) at the transmitter may not reflect the actual channel. In this paper, we propose an adaptive approach to pre-set the modulation scheme for LR-UWAC. This is a channel classification approach which, based on environmental information and on prior training on various channel types, predicts the best modulation scheme for the expected channel. Our classification procedure is trained to identify the channel’s important features. Thus, compared to a direct decision approach, it becomes less sensitive to possible mismatches of environmental information. Our numerical simulation and sea experiment show that our approach successfully identifies the best modulation scheme based on the environmental information - even when the information is biased or only partially available.

Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramer–Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works.

The sparse direct adaptive equalization technique recently received many attentions for single-carrier underwater acoustic communications. By taking advantage of the sparse (nonuniform) structure of a direct adaptive equalizer (DAE), one obtains a sparse DAE with improved performance and/or reduced complexity compared with its nonsparse counterpart. In this article, the sparse DAE is revisited with two contributions made: First, a comprehensive comparison is made for existing sparse DAEs designed with the proportionate-updating (PU) or the zero-attracting (ZA) adaptive filtering principles. The comparison concludes that the PU-type DAE outperforms the ZA-type DAE in terms of complexity, performance, and operability, thus shall be favored in practical use. Moreover, it motivates the design of a sparse DAE, named the selective ZA improved proportionate normalized least mean squares DAE (SZA-IPNLMS-DAE), based on the combination of the PU and ZA principles. The SZA-IPNLMS-DAE outperforms existing sparse DAEs armed with only one sparsity-promoting mechanism; second, a partial tap update (PTU) scheme via hard thresholding is introduced to sparse DAEs for reducing their complexity without sacrificing performance. The resulting low-complexity and high-performance sparse PTU-DAE schemes are strong candidates for single-carrier UWA communications. Experimental results of multiple-input multiple-output UWA communications are presented to corroborate all above claims.

The aim of this article is to develop and implement an optimal method for relay node placement (RNP) to increase lifetime of underwater acoustic sensor networks. Employing a relay node to extend the lifetime of critical node is an important problem in network lifetime maximization, whereas energy consumption of relay node is also a limiting factor. Consequently, maximizing the lifetime of critical node and minimizing consumption of relay node are the two main conflicting objectives that are met by applying the multiobjective optimization. To transform the above objectives into a single objective, weighted sum method is applied, which uses weights. Moreover, the RNP problem investigated in this article is a nonconvex problem, which difference convex algorithm (DCA), is developed in order to compute the optimal solution. Application of DCA offers the advantage of finding a global optimal solution. Simulation results backed by theoretical analysis confirm that the proposed approach is superior to the relay node adjustment method in terms of extending the network lifetime and efficiency, whereas their computational complexity is the same.