Search Results (558)

Optical cameras and imaging sonars are widely used sensors in autonomous underwater vehicles. However, their different imaging mechanisms introduce substantial cross-modal discrepancies in the acquired data. In addition, underwater optical images are often degraded by low illumination, scattering, and turbidity, whereas sonar images commonly suffer from speckle noise and low spatial resolution. As a result, object detection based on a single optical or acoustic modality is often insufficient in challenging underwater environments. To address this problem, this paper proposes an acoustic–optical fusion network for underwater object detection, termed an Acoustic–Optical Proposal Query Network (AOPQ-Net). First, a Sonar Position Encoding (SPE) module is designed to explicitly encode the geometric priors in sonar images. Second, a Bi-directional Discrepancy-aware Spatial Alignment (BDSA) module is introduced to alleviate spatial misalignment between the two modalities at the feature level. Third, a Proposal Query Transformer (PQT) module performs target-oriented cross-modal interaction at the proposal level. Furthermore, this study constructs a dedicated dataset for underwater acoustic–optical fusion object detection, named Haiqin Underwater Fusion (HUF), and conducts systematic experiments on this dataset. The experimental results show that AOPQ-Net outperforms single-modality baselines and representative multimodal fusion methods in both optical and acoustic image spaces, which demonstrate the effectiveness of the proposed method. Full article

Underwater wireless optical communication (UWOC) has emerged as a key technology for high-speed, low-latency data transmission in aquatic environments, enabling applications in autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), subsea sensor networks, and the Internet of Underwater Things (IoUT). This paper reviews photodetector technologies that shape UWOC system performance, covering both mature and emerging detector classes. We discuss the operating principles, key parameters, and practical trade-offs of photomultiplier tubes (PMTs), p-i-n photodiodes (PINs), avalanche photodiodes (APDs), single-photon avalanche diodes (SPADs), and silicon photomultipliers (SiPMs/MPPCs). We also present emerging photodetector technologies, including perovskite-based structures, SiC photoelectrochemical devices, scintillating optical fibers, and photovoltaic solar cells. A comparative analysis of reported UWOC experiments reveals a clear sensitivity–bandwidth trade-off among detector technologies: PIN-based receivers achieve the highest data rates (up to 25 Gbps) but are generally restricted to short-range links, whereas SPAD- and SiPM-based receivers provide sensitivities below −80 dBm and support transmission distances exceeding 200 m, at the cost of moderate data rates. The findings indicate that SiPM/MPPC arrays currently offer the most promising compromise between sensitivity and data rate for long-range UWOC applications. Full article

Electromagnetic underwater communications are strongly limited by water conductivity and depth, particularly at ISM frequencies above 900 MHz. Although LoRa technology has been widely adopted in low-power wireless sensor networks, experimental studies of LoRa-based underwater-to-overwater (UW2OW) links at 915 MHz in real freshwater environments remain scarce. This work presents an experimental evaluation of a 915 MHz LoRa UW2OW communication link conducted in three freshwater scenarios with different conductivity conditions: a controlled swimming pool and two natural lakes. The experimental campaign analyzes the influence of water depth and horizontal distance on key performance metrics, including received signal strength indicator (RSSI), signal-to-noise ratio (SNR), packet loss rate, and end-to-end latency. Measurements were carried out at depths between 0.25 m and 0.8 m. Multiple spreading factors were evaluated in the pool scenario, while SF12 was selected for the natural-lake experiments. The results demonstrate that short-range UW2OW communication is feasible, achieving effective distances of up to 13 m at shallow depths. However, increased depth and higher water conductivity lead to significant performance degradation, with packet loss rates exceeding 80% at longer distances. The obtained results provide empirical insights and practical design criteria for short-range freshwater underwater wireless sensor networks based on LoRa technology. Full article

This article investigates sensor placement strategies for 3-D time-of-arrival (TOA)-based target localization in underwater acoustic sensor networks (UASNs). To prevent the overestimation of localization performance common in idealized marine models, we derive an exact acoustic propagation time and estimate the TOA measurement variance using a non-linear ray acoustic model. Leveraging this formulation, we establish a realistic 3-D TOA measurement model that incorporates depth-dependent sound speed profiles (SSP) and spatially heterogeneous noise, where the trace of the Cramér-Rao lower bound (CRLB) serves as the optimization criterion. To solve the resulting non-convex optimization problem, we propose a MinMax k-Means algorithm to determine the optimal sensor configuration by minimizing the average of the trace of CRLB. Extensive numerical simulations demonstrate that the proposed placement strategy significantly enhances localization accuracy and robustness compared to conventional benchmarks, proving its effectiveness in complex underwater environments. Full article

This study presents the development and application of a continuous monitoring system to operate in hybrid mode for the atmospheric and oceanic environments. The developed system integrates a smart version of the underwater in situ sensor named KATERINA to a stationary platform in order to operate as a real-time communication tool. The data are transferred using a mobile telephony network and the power is generated for all modules by a solar panel. The system is applied for a period of around six months in different seasons to detect and identify gradients of environmental radioactivity in the atmosphere. The system observed gamma-ray emitters during dry and wet periods, exhibiting enhanced radon progenies during wet periods as identified in the acquired spectra. Moreover, the gross gamma-ray intensity depends on the radon progenies and is used as a tracer to interpret rainfall events in a qualitative manner. The background gamma-ray spectra during dry periods for different seasons are also discussed in terms of seasonality. The correlation of gamma-ray intensity rate with the rainfall rate is also studied for the wet periods, providing a R² value of around 75%. Full article

This paper addresses the problem of controlling autonomous underwater vehicles (AUVs) operating in a swarm under realistic underwater conditions characterised by inaccurate navigation, limited acoustic communication, and noisy sonar observations. A novel Trail Sonar-Based Algorithm (TSBA) is proposed for leader–follower swarm control. Unlike conventional reactive approaches, TSBA combines sparse acoustic communication with prior knowledge of the mission plan, enabling predictive estimation of the tracked vehicle’s state and reducing the dependence on continuous information exchange. To evaluate its effectiveness, TSBA was compared with a machine learning-based controller (NSCSUV) in a simulation environment incorporating navigation drift, sonar measurement errors, and a data-driven model of a real low-cost AUV. The proposed vehicle model achieved a mean speed error of 0.107 m/s and a mean heading error of 14.25°, providing a realistic basis for controller evaluation. Simulation results demonstrated that TSBA consistently outperformed the neural network-based approach in formation keeping while generating smoother control commands and requiring only minimal underwater communication. The algorithm maintained stable swarm behaviour despite sensor inaccuracies and communication constraints. Finally, experiments conducted with a real underwater vehicle confirmed the practical applicability and robustness of the proposed approach under real operating conditions. Full article

For underwater acoustic sensor networks (UWSNs), we propose a lattice-based aggregate signcryption scheme (UASC) to achieve many to one secure and efficient communication in UWSNs. Firstly, UASC constructs the pseudo identity of the node, performs lattice Gaussian sampling on the pseudo identity to generate a partial key, and then combines the underwater environmental noise collected by the node to generate a complete private key, to avoid the key generation center (KGC) leakage problem. Furthermore, UASC reuses the mathematical structures of encryption and signature on the lattice, designing a signcryption framework to achieve identity authentication and data confidentiality protection. By using the relay capability of the surface sink node, UASC combines with the ring learning with errors (RLWE) encryption and the rejection sampling to perform batch signature verification on the aggregate ciphertext set, which improves the overall response efficiency of the system. In addition, to resist tracking attacks, a dynamic update mechanism of pseudo identity and key is designed in UASC. Security analysis and experimental results show that UASC reduces overhead while satisfying relevant security requirements. Full article

As wearable sensors advance toward long-term motion monitoring and operation in humid environments, performance priorities are shifting from sensitivity to sustained reliability. Hydrogels are attractive sensing materials due to their tissue-like compliance, biocompatibility, and tunable conductivity; however, their hydrated networks readily absorb water under perspiration, high humidity, and underwater conditions, leading to structural relaxation, interfacial instability, conductive pathway disruption, and signal drift. Thus, anti-swelling design should move beyond reducing swelling ratios toward coordinated regulation of water transport, internal water environment, interfacial integrity, and signal stability. This review summarizes recent advances in anti-swelling hydrogel-based wearable sensors, focusing on structural engineering strategies, including network confinement, surface hydrophobicity, core–shell architectures, and gradient structures, as well as material regulation mechanisms, including ionic/coordination crosslinking, nanoconfinement, zwitterionic hydration, and solvation-mediated anti-water exchange, highlighting their synergistic roles in long-term anti-swelling performance and environmental adaptability. Representative applications in perspiration monitoring, underwater motion sensing, rehabilitation, and intelligent interaction demonstrate the importance of anti-swelling regulation for reliable sensing in wet environments. Finally, the remaining challenges are summarized, together with future perspectives on the synergistic design of structures, materials, and interfaces, standardized evaluation systems for realistic motion environments, and scalable manufacturing. Anti-swelling hydrogel sensors are expected to evolve from low-swelling materials into environmentally adaptive sensing platforms for aqueous environments, enabling advances in underwater sports monitoring, digital health, and underwater human–machine interaction. Full article

Underwater Wireless Sensor Networks (UWSNs) hold significant economic and military value; however, their routing protocols remain inherently vulnerable to external attacks. Unlike terrestrial networks, UWSNs cannot readily adopt complex cryptographic verification systems due to the high propagation delay, limited bandwidth, and low connectivity inherent in underwater acoustic channels. To address the wormhole attack—one of the most critical threats to UWSN routing—this paper proposes an intelligent routing scheme (UWSN-IRS) that not only detects wormhole attacks effectively but also identifies the source nodes and eliminates the threat. The proposed scheme comprises four integrated modules: a self-adjusting routing mechanism, a wormhole attack detection mechanism, a wormhole node localization mechanism, and an anti-cheating mechanism. The self-adjusting routing mechanism optimizes node distribution and intelligently searches for the optimal forwarding path. Upon the occurrence of a wormhole attack, the detection mechanism employs an artificial neural network to identify the compromised links and outputs a set of suspected wormhole nodes. Subsequently, the localization mechanism determines the exact positions of these malicious nodes through ranging and iterative positioning. Finally, the anti-cheating mechanism isolates the detected attacking nodes and deploys substitute nodes to fill the resulting monitoring voids. The experimental results demonstrate that the UWSN-IRS exhibits superior performance in attack scenarios, enabling reliable wormhole detection, precise attacker localization, and sustained normal network communication. Full article

Iterative position estimation algorithms based on Time Difference of Arrival (TDOA) are widely used in passive localization systems, including underwater acoustic networks and wireless sensor networks. A critical but often overlooked factor in their practical deployment is the selection of the initial estimate, which directly determines whether the iterative algorithm converges to the correct solution. This paper presents a case-specific approach to initial estimate selection in passive TDOA-based iterative position estimation algorithms. The study evaluates two proposed methods against a common baseline approach, where the initial guess is placed at the center of the sensor formation. Simulations were conducted in Python for both 2D and 3D scenarios, with sensors arranged in two different geometric configurations. A grid-based analysis over a 2 × 2 km area was used to assess performance under both noise-free and noisy TDOA conditions, with Gaussian-distributed error introduced at varying standard deviations. The results demonstrate that in regions where convergence is sensitive to initialization, the proposed Method 1 significantly improves reliability, especially for asymmetric sensor configurations. These findings highlight the importance of initial estimate selection to enhance position estimation accuracy and robustness, particularly in passive systems with limited prior information. Full article

Underwater object detection is a fundamental task in underwater sensing and is generally approached using either optical or sonar sensors. Although optical imaging provides rich semantic information, it is highly susceptible to water turbidity and illumination variations. By contrast, sonar imaging can effectively overcome visibility limitations, yet it suffers from severe speckle noise and blurred object contours. Moreover, resource-limited platforms impose strict demands on model lightweightness and real-time performance. To this end, this paper proposes a novel cross-modal heterogeneous distillation method (CMHD) to balance detection accuracy and computational complexity. CMHD performs cross-modal knowledge transfer by leveraging the rich semantics of RGB images to enhance sonar feature representation, compensating for the information deficiency of the sonar modality. Meanwhile, a heterogeneous distillation scheme compresses the detection capability of a high-capacity teacher YOLOX-M into a lightweight student YOLOX-S-Ghost, enabling strong feature extraction under a highly compact model. To mitigate the modality gap and geometric inconsistency between RGB and sonar modalities, we design a branch-aware heterogeneous distillation strategy. To improve detection accuracy and reduce model parameters, the student network incorporates Coordinate Attention (CA) in its backbone and adopts a lightweight neck design. Experiments on the UXO^† dataset demonstrate that CMHD achieves 79.6% mAP and 82.6% mAR, significantly outperforming the compared representative methods and serving as an accurate, efficient, and lightweight solution for underwater sonar object detection. Full article

Underwater optical sensor networks are essential for fish monitoring, yet imagery is often affected by illumination variability, low contrast, and complex backgrounds. Attention mechanisms are vital for feature representation in deep networks, yet existing approaches often struggle with spatial information loss and limited multi-scale interaction under such challenging conditions. This paper introduces Convolution to Interactive Capture and Recalibration Enhancement (C2ICARE), a lightweight attention module designed to overcome these challenges. The principal contribution of C2ICARE is the adaptation of memory interaction principles into an edge-oriented attention framework that enhances feature discrimination while maintaining computational efficiency. The architecture employs three core innovations: a 1:3 memory-feature split to preserve context while reducing cost, parallel multi-scale depthwise convolutions (3 × 3 and 7 × 7) for fine-grained and broad feature extraction, and a cross-branch interaction mechanism coupled with a ConvNeXt-style feed-forward network that avoids dimensionality reduction. Experimental results on an underwater fish dataset demonstrate that YOLO26n with C2ICARE achieves a mean average precision (mAP@0.5:0.95) of 0.7033, outperforming Coordinate Attention (+3.8%), FasterBlock (+1.7%), and CBAM (+0.4%) while adding only 0.05M parameters and 0.16 GFLOPs. Multi-objective Pareto Frontier analysis confirms that C2ICARE provides an effective balance between accuracy, efficiency, and generalization for resource-constrained deployment. EigenCAM visualizations further validate that the model focuses on biological morphology rather than background noise. Its lightweight design enables seamless integration with underwater sensor networks and fog platforms for real-time fish detection in aquaculture, commercial fisheries, and scientific research. Future work will investigate broader marine applications and cross-platform deployment scenarios. The code is available on GitHub. Full article

Underwater laser range-gated imaging (ULRGI) effectively suppresses water backscattering via time-slicing mechanisms, making it a primary modality for underwater vision. However, factors such as the inherent optical properties of water, intra-slice residual scattering, gating timing errors, and sensor noise make it difficult to separate target signals from the background. Consequently, the resulting images are generally affected by texture degradation and low contrast, severely limiting the accuracy of downstream tasks like object detection and environmental perception. To this end, we propose the use of a Wavelet-guided Multi-stage Gated Enhancement Network (WMGNet). Operating progressively across three stages, WMGNet’s first two stages employ an encoder–decoder architecture that leverages multi-scale frequency decomposition in the wavelet domain to pinpoint intra-slice scattering and decouple target signals from noise. To precisely extract fine details, we design a TextureBlock integrating feature gating (ConvGLU) and high-frequency attention (HFAttention). Additionally, a pixel-wise ground-truth guided attention module (GGAM) is introduced to optimize the precision and target-specificity of multi-stage feature fusion. Extensive comparative and ablation experiments demonstrate that the proposed WMGNet effectively eliminates scattering interference and restores texture details in underwater imaging. On our custom ULRGI dataset, it achieves state-of-the-art performance with a PSNR of 36.31 dB, an SSIM of 0.921, an MAE of 2.672, and an LPIPS of 0.060. Notably, it outperforms the second-best method by a margin of 3.06 dB in PSNR and reduces the MAE by 50.69%. Furthermore, evaluations on three public datasets confirm its robust cross-scenario generalization, yielding competitive PSNR values of 33.22 dB, 31.59 dB, and 32.06 dB, respectively. Overall, WMGNet provides a highly effective and robust solution for high-resolution underwater imaging. Full article

Autonomous Underwater Vehicles (AUVs) are increasingly used in deep-sea exploration, environmental monitoring, and marine engineering. Their operational safety and mission performance rely heavily on accurate and long-endurance underwater localization. However, both single-sensor localization methods and existing multi-sensor fusion approaches have inherent limitations, making it difficult to achieve high-precision localization during long-duration missions. To address this issue, this study develops a deep-learning-based multi-source sensor fusion framework for AUV localization. In the proposed framework, high-frequency data from the Inertial navigation system (INS) and Doppler velocity log (DVL) are used for continuous position propagation, while low-frequency absolute position observations from the Ultra-short baseline (USBL) system and Sonar are used to periodically correct the propagated results. Based on this framework, three instantiated models are developed using a Deep neural network (DNN), a Long short-term memory (LSTM) network, and a Bayesian semi-supervised mixed shallow-layer neural network (BSsMSLNN), respectively. Comparative experiments are conducted against the Extended Kalman filter (EKF) and Simultaneous localization and mapping system using Sonar, Visual, Inertial, and Depth sensor (SVIn2). The results show that the proposed framework effectively suppresses long-term error accumulation and significantly improves localization accuracy. Among the evaluated models, the BSsMSLNN-based method achieves the best performance in terms of trajectory fitting, root mean square error (RMSE), and coefficient of determination (R²). The proposed method provides a feasible solution for high-precision autonomous navigation of AUVs in GPS-denied environments. Full article

Recently, conductive hydrogels have gained extensive applications in flexible wearable electronics and have garnered considerable attention. However, their inherent swelling behaviour and limited mechanical strength have hindered their further development. In this study, a polyacrylamide/sodium alginate (PAM/SA, PS)-based hydrogel with high mechanical strength and anti-swelling properties was prepared by combining mechanical stretching–drying pretreatment with a bimetallic ion (Li⁺/multivalent metal ion) post-soaking strategy. Among multivalent metal ions (Ca²⁺, Al³⁺, and Zr⁴⁺), the Al³⁺-crosslinked hydrogel (PS-Al³⁺) demonstrated outstanding overall performance. It exhibited excellent mechanical properties, with tensile strength, elongation at break, and impact strength reaching 9.71 MPa, 993.53%, and 75 MJ/m³, respectively. Its dense network structure also gave it excellent anti-swelling properties (swelling ratio of 14%). As a strain sensor, the PS-Al³⁺ hydrogel displayed good conductivity (1.33 S/m), high sensitivity (GF = 2.25), fast response (response time of 403 ms), and negligible hysteresis (recovery time of 407 ms). Benefiting from its exceptional resistance to expansion, the material’s sensor response signals in underwater environments are highly consistent with those in air. Furthermore, this sensor has been successfully applied to swimming motion monitoring and data transmission in underwater environments. This study proposes a novel, low-cost, and simple approach for developing flexible sensors suitable for underwater environments. Full article