Search Results (461)

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

Physics-informed neural networks (PINNs) have recently attracted considerable attention as a framework for solving partial differential equations. Underwater sound-field prediction fundamentally relies on solving acoustic wave equations, making PINNs a natural candidate for this application. This paper reviews recent developments in PINN-based modeling of underwater acoustic propagation, which we group into two main lines of research. The first introduces mathematically motivated simplifications of the governing equations and then employs PINNs as efficient solvers; examples include ray-based PINNs and PINN estimators of modal wavenumbers. The second focuses on improving computational performance by tailoring network architectures and hyperparameters, such as spatial domain-decomposition strategies. While PINNs demonstrate significant potential, challenges persist regarding computational efficiency and convergence in high-frequency regimes. Future research directions are identified, emphasizing a multi-faceted strategy that systematically addresses limitations at both the physical formulation level and the neural network architecture level. By integrating advanced hybrid physics-data modeling and scalable training algorithms, this review highlights the pathway toward bridging the gap between theoretical frameworks and realistic ocean applications. Full article

Wireless Sensor Networks (WSNs) are pivotal for data acquisition, yet reliability is severely constrained by routing voids induced by sparsity, uneven energy, and high dynamicity. To address these challenges, the Hybrid Acoustic-Optical Adaptive Void-handling Protocol (HAO-AVP) is proposed to satisfy the requirements for highly reliable communication in complex underwater environments. First, targeting uneven energy, a reinforcement learning mechanism utilizing Gini coefficient and entropy is adopted. By optimizing energy distribution, voids are proactively avoided. Second, to address routing interruptions caused by the high dynamicity of topology, a collaborative mechanism for active prediction and real-time identification is constructed. Specifically, this mechanism integrates a Markov chain energy prediction model with on-demand hop discovery technology. Through this integration, precise anticipation and rapid localization of potential void risks are achieved. Finally, to recover damaged links at the minimum cost, a four-level progressive recovery strategy, comprising intra-medium adjustment, cross-medium hopping, path backtracking, and Autonomous Underwater Vehicle (AUV)-assisted recovery, is designed. This strategy is capable of adaptively selecting recovery measures based on the severity of the void. Simulation results demonstrate that, compared with existing mainstream protocols, the void identification rate of the proposed protocol is improved by approximately 7.6%, 8.4%, 13.8%, 19.5%, and 25.3%, respectively, and the void recovery rate is increased by approximately 4.3%, 9.6%, 12.0%, 18.4%, and 24.2%, respectively. In particular, enhanced robustness and a prolonged network life cycle are exhibited in sparse and dynamic networks. Full article

This paper presents a modulation signal classification and recognition algorithm based on data augmentation and time–frequency joint attention (DA-TFJA) for underwater acoustic (UWA) communication systems. UWA communication, as an important means of marine information transmission, plays a key role in fields such as marine engineering, military reconnaissance, and marine science research. Accurate recognition of modulated signals is a core technology for ensuring the reliability of UWA communication systems. Traditional classification and recognition methods, mostly based on pure neural network algorithms, suffer from insufficient feature representation and limited generalization performance in complex and changing UWA channel environments. They also struggle to address complex factors such as multipath, Doppler shift, and noise interference, often resulting in scarce effective training samples and inadequate classification accuracy. To overcome these limitations, the proposed DA-TFJA algorithm simulates the characteristics of real UWA channels through two novel data augmentation strategies: the adaptive time–frequency transform enhancement algorithm (ATFT) and dynamic path superposition enhancement algorithm (DPSE). An end-to-end recognition network is developed that integrates a multiscale time–frequency feature extractor (MTFE), two-layer long short-term memory (LSTM) temporal modeling, and a time–frequency joint attention mechanism (TFAM). This comprehensive architecture achieves high-precision recognition of six modulation types, including 2FSK, 4FSK, BPSK, QPSK, DSSS, and OFDM. Experimental results demonstrate that compared with existing advanced methods, DA-TFJA achieves a classification accuracy of 98.36% on the measured reservoir dataset, representing an improvement of 3.09 percentage points, which fully verifies the effectiveness and practical value of the proposed approach. Full article

Direction-of-arrival (DOA) estimation is fundamental in array signal processing, yet classical algorithms suffer from significant performance degradation under low signal-to-noise ratio (SNR) conditions and require computationally intensive eigenvalue decomposition. This study presents a systematic comparative analysis of two backbone networks, a convolutional neural network (CNN) and long short-term memory (LSTM) for DOA estimation, addressing two critical research gaps: the lack of a mechanistic understanding of architecture-dependent performance under varying conditions and insufficient validation using real measured data. Both networks are trained using cross-spectral density matrices (CSDMs) from simulated uniform linear array (ULA) signals. Under baseline conditions (1° classification interval), both CNN and LSTM methods reach an accuracy (ACC) above 98%, in which the error is ±1° for CNN and ±2° for LSTM, only existing in the end-fire direction. Key findings reveal that LSTM maintains above 90% accuracy down to −20 dB SNR, demonstrating superior noise robustness, whereas CNN exhibits better angular resolution. Four performance boundaries are identified: optimal performance is achieved at half-wavelength element spacing; SNR crossover occurs at −20 dB below which accuracy drops sharply; the snapshot threshold of 32 marks the transition from snapshot-deficient to snapshot-sufficient conditions; the array size of 8 is the turning point for the performance variation rate. Comparative analysis against traditional methods demonstrates that deep learning approaches achieve superior resolution ability, batch processing efficiency, and noise robustness. Critically, models trained exclusively on single-target simulated data successfully generalize to multi-target experimental data from the Shallow Water Array Performance (SWAP) program, recovering primary target trajectories without domain adaptation. These results provide concrete engineering guidelines for architecture selection and validate the sim-to-real generalization capability of CSDM-based deep learning approaches in underwater acoustic environments. Full article

Automatic Target Recognition (ATR) in single-view sonar imagery is severely hampered by geometric distortions, acoustic shadows, and incomplete target information due to occlusions and the slant-range imaging geometry, which frequently give rise to misclassification and hinder practical underwater detection applications. To address these critical limitations, this paper proposes a Multi-View Deep Convolutional Neural Network (MVDCNN) based on feature-level fusion for robust sonar image target recognition. The MVDCNN adopts a highly modular and extensible architecture consisting of four interconnected modules: an input reshaping module that adapts multi-view images to match the input format of pre-trained backbone networks via dimension merging and channel replication; a shared-weight feature extraction module that leverages Convolutional Neural Network (CNN) or Transformer backbones (e.g., ResNet, Swin Transformer, Vision Transformer) to extract discriminative features from each view, ensuring parameter efficiency and cross-view feature consistency; a feature fusion module that aggregates complementary features (e.g., target texture and shape) across views using max-pooling to retain the most salient characteristics and suppress noisy or occluded view interference; and a lightweight classification module that maps the fused feature representations to target categories. Additionally, to mitigate the data scarcity bottleneck in sonar ATR, we design a multi-view sample augmentation method based on sonar imaging geometric principles: this method systematically combines single-view samples of the same target via the combination formula and screens valid samples within a predefined azimuth range, constructing high-quality multi-view training datasets without relying on complex generative models or massive initial labeled data. Comprehensive evaluations on the Custom Side-Scan Sonar Image Dataset (CSSID) and Nankai Sonar Image Dataset (NKSID) demonstrate the superiority of our framework over single-view baselines. Specifically, the two-view MVDCNN achieves average classification accuracies of 94.72% (CSSID) and 97.24% (NKSID), with relative improvements of 7.93% and 5.05%, respectively; the three-view MVDCNN further boosts the average accuracies to 96.60% and 98.28%. Moreover, MVDCNN substantially elevates the precision and recall of small-sample categories (e.g., Fishing net and Small propeller in NKSID), effectively alleviating the class imbalance challenge. Mechanism validation via t-Distributed Stochastic Neighbor Embedding (t-SNE) feature visualization and prediction confidence distribution analysis confirms that MVDCNN yields more separable feature representations and more confident category predictions, with stronger intra-class compactness and inter-class discrimination in the feature space. The proposed MVDCNN framework provides a robust and interpretable solution for advancing sonar ATR and offers a technical paradigm for multi-view acoustic image understanding in complex underwater environments. Full article

Underwater networks are crucial for monitoring the marine ecosystem, enabling data collection to support the preservation and protection of natural resources. Among the various technologies available, acoustic and optical communications stand out for their superior performance in underwater environments. Acoustic technologies are suitable for long-range communications, typically operating over hundreds of meters up to several kilometers, albeit with low data rates ranging from a few hundred bps to few tens of kbps. In contrast, optical technologies excel in providing high data rates, often between 1 and 10 Mbps, but only over short distances (e.g., 50 m) in controlled conditions. To leverage the strengths of these technologies, recent research has proposed multi-modal underwater systems; however, these solutions generally rely on single-level or at most dual-level architectures, limiting the benefits of a structured hierarchical approach. In this review paper, after discussing related work on multi-technology acoustic and optical networks, we highlight relevant design guidelines for multi-technology, multi-level underwater architectures, explicitly considering three layers: a deep acoustic layer, an intermediate optical layer, and an upper RF-enabled surface layer. For illustration, we also discuss a PoC of such a hierarchical architecture under development at the University of Catania, Italy, in the Area Marina Isole dei Ciclopi natural reserve. The PoC includes optical nodes capable of transmitting up to 10 Mbps over short ranges and acoustic nodes (both software defined and not) supporting rates of tens of kbps over hundreds of meters and being adaptive to network conditions, interconnected through hybrid multi-technology nodes deployed across the three network levels. By assigning specific technologies to appropriate layers, the architecture enhances scalability, robustness, and adaptability to dynamic underwater conditions. This design strategy not only improves data transmission efficiency but also ensures seamless operation across diverse marine scenarios, making it an effective solution for a wide range of underwater monitoring applications. Full article

Tsunamis and submarine earthquakes pose severe risks to coastal regions, demanding rapid and reliable monitoring systems. While the Deep-ocean Assessment and Reporting of Tsunamis (DART) system has been globally deployed, its dependence on pressure sensors and one-to-one communication limits its applicability to the Korean East Sea. This paper introduces the Korean Tsunami and Earthquake Monitoring System, which integrates seafloor seismometers and proposes a dedicated Medium Access Control (MAC) protocol optimized for multi-node underwater acoustic communication. The study performs a comprehensive analytical derivation of closed-form expressions for channel utilization and energy consumption under diverse node configurations and acoustic conditions. The analytical results confirm that the proposed MAC protocol maintains stable performance, supports multi-node operation, and enables long-term monitoring within the limited energy budget of underwater devices. The derived results also provide practical design implications for underwater network planning, including guidelines on node placement, frame duration, and control packet timing for efficient data delivery. Although empirical validation remains as future work, the findings establish theoretical benchmarks and engineering insights for the design of next-generation underwater monitoring systems tailored to Korean coastal environments. Full article

Reliable detection of unknown signals is essential for ensuring the robustness of underwater acoustic sensing systems, particularly in maritime security and autonomous navigation. However, Conventional deep learning models often exhibit overconfidence when encountering unknown signals and are unable to quantify predictive uncertainty due to their deterministic inference process. To address these limitations, this study proposes a novelty detection framework that integrates an out-of-distribution detector for neural networks (ODIN) with Monte Carlo (MC) dropout. ODIN mitigates model overconfidence and enhances the separability between known and unknown signals through softmax probability calibration, while MC dropout introduces stochasticity via multiple forward passes to estimate predictive uncertainty—an element critical for stable sensing in real-world underwater environments. The resulting probabilistic outputs are modeled using Gaussian mixture models fitted to ODIN-calibrated softmax distributions of known classes. The Kullback–Leibler divergence is then employed to quantify deviations of test samples from known class behavior. Experimental evaluations on the DeepShip dataset demonstrate that the proposed method achieves, on average, a 9.5% and 5.39% increase in area under the receiver operating characteristic curve, and a 7.82% and 2.63% reduction in false positive rate at 95% true positive rate, compared to the MC dropout and ODIN baseline, respectively. These results confirm that integrating stochastic inference with ODIN significantly enhances the stability and reliability of novelty detection in underwater acoustic environments. Full article

Depth-based probabilistic routing (DPR) is an efficient underwater acoustic network (UAN) routing protocol which resists the depth-spoofing attack. DPR’s optimal value of the unqualified forwarding probability depends on the UAN topology, condition, and threat state, which are highly dynamic. If the static forwarding probability used in DPR is set too low for the current state, packet delivery ratio (PDR) drops. If it is set too high, unnecessary forwarding occurs when the network is not under attack, thus wasting valuable energy. In this paper, we propose a novel routing protocol, which uses a feedback mechanism that allows the sink to continuously adapt the unqualified forwarding probability according to the current network state. The protocol aims to achieve an application-controlled desired delivery ratio using one of three proposed update algorithms developed in this work. We analyze the performance of the proposed algorithms through simulation. Results demonstrate that the proposed adaptive routing protocol achieves resilience to depth-spoofing attacks by successfully delivering more than 80% of generated packets in more than 95% of simulated networks, while avoiding unnecessary unqualified forwarding in normal conditions. Full article

Underwater Wireless Sensor Networks (UWSNs) are widely used technologies in aquatic environments. However, these types of networks face several constraints caused by the mobility of nodes, energy consumption, and constraints due to acoustic communication. In light of this, the location of nodes appears as a promising axis for improving the services expected from these networks. To address these, we suggest the LUCOTED approach—a Linear Constraint Optimization Technique for estimating unknown node positions by selecting anchor nodes with the highest energy and shortest distance, based on randomly initialized conditions. It achieves 98% accuracy, exceeding Gradient Descent and Trilateration methods. Moreover, our method LUCOTED outperforms the DEEC algorithm in terms of error when the number of anchor nodes is below 80 and achieves higher accuracy than the EPRP technique when the number exceeds 100. Full article

Underwater wireless networking is an emerging field for exploration and monitoring, enabling real-time data transmission and communication with both static sensors and submersibles. Current approaches mostly focus on utilizing acoustic waves. The use of optics for this purpose has been known to have several implementation challenges that have prevented it from being considered as a universal alternative. This study proposes that utilizing optics in an adaptive relay wireless network configuration can overcome its primary limitation of line-of-sight (LOS) propagation. In this paper, a network of strategically placed sensors is experimentally constructed with the ability to read and send modulated blue light, fit for extended submersion in water. This proposal represents a hypothetical aquatic drone swarm that is developed and programmed to follow adaptive relay logic. This network is able to demonstrate adaptation to obstructions in the LOS and maintain communication through configurations in which the sender and intended recipient would otherwise be unable to directly communicate. This finding allows the advantages of optical communications to be further explored for aquatic applications, primarily its higher potential data rate, which is inherently productive to a swarm. Full article

Accurate estimation of underwater sound source depth plays a crucial role in ocean acoustic monitoring, underwater target localization, and marine environment exploration. This study exploits the capability of vector hydrophones to simultaneously and co-locally acquire both scalar and vector components of the underwater sound field. Based on the study of the line spectrum interference structure characteristics of the underwater sound field, the vertical sound intensity flux of the underwater sound source is extracted. Additionally, a parallel BiLSTM and ResNet network structure is proposed to train this feature and achieve depth estimation of underwater sound sources. Experimental results show that under ±10% and ±15% errors in the source–hydrophone distance, the proposed model maintains stable performance within a signal-to-noise ratio (SNR) range of −15 dB to +15 dB. Compared with the LSTM model, the ResNet model, and the matched-field processing (MFP) algorithm, the average RMSE of our model is reduced by 72.4%, 54.0%, and 64.1%, respectively. Furthermore, under 5% and 10% frequency estimation errors, the average RMSE of the proposed model within the same SNR range is reduced by 47.7%, 20.3%, and 79.3%, respectively. It effectively estimates the depth of underwater sound sources, with estimation errors below 5 m under non-extreme SNR conditions. These results fully demonstrate the robustness and effectiveness of the proposed method under practical uncertainties in the ocean environment. Full article

Marine mammal vocalizations, such as those of the Northern Right Whale (NARW), are often masked by underwater acoustic noise. The acoustic vocalization signals are characterized by features such as their amplitude, timing, modulation, duration, and spectral content, which cannot be robustly captured by a single feature extraction method. These complex signals pose additional detection challenges beyond their low SNR. Consequently, this study proposes a novel low-SNR NARW classifier for passive acoustic monitoring (PAM). This approach employs an ideal binary mask with a bidirectional long short-term memory highway network (IBM-BHN) to effectively detect and classify NARW upcalls in challenging conditions. To enhance model performance, the reported literature limitations were addressed by employing a hybrid feature extraction method and leveraging the BiLSTM to capture and learn temporal dependencies. Furthermore, the integration of a highway network improves information flow, enabling near-real-time classification and superior model performance. Experimental results show the IBM-BHN method outperformed five considered state-of-the-art baseline models. Specifically, the IBM-BHN achieved an accuracy of 98%, surpassing ResNet (94%), CNN (85%), LSTM (83%), ANN (82%), and SVM (67%). These findings highlight the practical potential of IBM-BHN to support near-real-time monitoring and inform evidence-based, adaptive policy enforcement critical for NARW conservation. Full article

This paper discusses the method for extending the range of Magnetic Induction (MI) and its application in underwater networks for the Internet of Underwater Things (IoUT). In underwater communication, this technology would provide a wider frequency band than acoustic systems, shorter propagation delay, and increased conductivity, with the added benefit of underwater wireless power transfer. As a use case, we consider a system that allows energy to be transferred from one circuit to another without cables, as in an aerial environment. In this work, transmit and receive coils for underwater environments are designed and analyzed using ANSYS Maxwell v16.0 software. The results show an improvement in terms of underwater magnetic field propagation. We have conducted underwater experiments by applying a frequency range up to 100 kHz and 12 Volts with varied current, achieving a distance up to 80% greater than in air, as determined by parametric analysis. With an improved bit error rate, a delay of less than 2 microseconds, a packet delivery ratio near 100%, and a packet loss ratio less than 10%, the results show an improvement in magnetic field propagation underwater. This demonstrates that it is possible to conduct future research into other underwater applications by implementing MI for underwater communication. Full article