Search Results (52)

Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~10¹³). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

With the advancement of artificial intelligence, the inference capabilities of Autonomous Underwater Vehicles (AUVs) have significantly improved, leading to growing interest in AUV applications. To ensure reliable operations, the field of underwater communications demands robust schemes that account for AUV mobility and enable the formation of underwater cellular networks. Conventional approaches using Linear Frequency Modulation (LFM) and Zadoff–Chu sequence (ZCS) sequences for frame detection and Cell ID (CID) assignment degrade substantially under severe Doppler conditions. In particular, AUVs experience pronounced Doppler shifts due to their mobility in underwater channels. In this study, we propose a methodology in which distinct Superimposed Adjusted-HFM (SA-HFM) signals are assigned to multiple buoys, allowing AUVs to jointly perform frame detection, CID assignment, and Doppler estimation in challenging underwater environments. To validate the proposed scheme, an ocean experiment was conducted in the East Sea of the Republic of Korea. The results demonstrate that the SA-HFM-based signals successfully achieved frame detection, CID assignment, and Doppler estimation at distances ranging from 500 m to approximately 2 km, even when the AUV moved at speeds of 1.02–1.54 m/s. The experimental results indicate that the proposed approach can offer robust underwater communication and facilitate the deployment of underwater cellular networks for mobile AUV operations. Full article

The energy supply of ocean monitoring buoys is a major challenge, especially for long-term, low-power applications. Data compression can reduce transmission energy and extend system lifespan. In particular, the algorithm cannot introduce delays to ensure real-time monitoring. In this scenario, we propose an efficient real-time compression scheme for lossless data compression (ERCS_Lossless) based on Golomb-Rice coding to efficiently compress each dimensional data independently. Additionally, we propose an efficient real-time compression scheme for lossy data compression with a flag mechanism (ERCS_Lossy_Flag), which incorporates a flag bit for each dimension, indicating if the prediction error exceeds a threshold, followed by further compression using Golomb-Rice coding. We conducted experiments on 24-dimensional weather and wave element data from a single buoy, and the results show that ERCS_Lossless achieves an average compression rate of 47.40%. In real communication scenarios, splicing and byte alignment operations are performed on multidimensional data, and the results show that the variance of the payload increases but the mean decreases after compression, realizing a 38.60% transmission energy saving, which is better than existing real-time lossless compression methods. In addition, ERCS_Lossy_Flag further reduces the amount of data and improves energy efficiency when lower data accuracy is acceptable. Full article

Common underwater target recognition systems suffer from low accuracy, high energy consumption, and low levels of automation. This paper introduces an underwater target recognition system based on the Jetson Xavier NX platform, which deploys an improved YOLOv11 recognition algorithm. During operation, the Jetson Xavier NX invokes an industrial camera to capture underwater target images, which are then processed by the improved YOLOv11 network for inference. The recognized information is transmitted via a serial port to an STM32 control board, which adaptively adjusts the lighting system to enhance image clarity based on the target information. Finally, the system controls an actuator to release a buoyant ball with positioning capabilities and communicates with the shore. On the ROUD dataset, the improved YOLOv11 algorithm achieves an accuracy of 87.5%, with a parameter size of 2.58M and a floating-point operation count of 6.3G, outperforming all current models. Compared to the original YOLOv11, the parameter size is reduced by 5% and the floating-point operation count by 0.3G. The improved DD-YOLOv11 also shows good performance on the URPC2020 dataset. After on-site experiments and hardware–software integration tests, all functions operate normally. The system is capable of identifying a specific underwater target with an accuracy rate of over 85%, simultaneously releasing communication buoys and successfully establishing communication with the shore base. This indicates that the underwater target recognition system meets the requirements of being lightweight, high-precision, and highly automated. Full article

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies. Full article

This study addresses the issues of the limited data storage capacity of Argo buoys and satellite communication charges on the basis of data volume by proposing a block lossless data compression method that combines bidirectional long short-term memory networks and multi-head self-attention with a multilayer perceptron (BiLSTM-MHSA-MLP). We constructed an Argo buoy data compression system using the main buoy control board, Jetson nano development board, and the BeiDou-3 satellite transparent transmission module. By processing input sequences bidirectionally, BiLSTM enhances the understanding of the temporal relationships within profile data, whereas the MHSA processes the outputs of the BiLSTM layer in parallel to obtain richer representations. Building on this preliminary probability prediction model, a multilayer perceptron (MLP) and a block length parameter (block_len) are introduced to achieve block compression during training, dynamically updating the model and optimizing symbol probability distributions for more accurate predictions. Experiments conducted on multiple 4000 m single-batch profile datasets from both the PC and Jetson nano platforms demonstrate that this method achieves a lower compression ratio, shorter compression time, and greater specificity. This approach significantly reduces the communication time between Argo buoys and satellites, laying a foundation for the future integration of Jetson Nano into Argo buoys for real-time data compression. Full article

In the context of climate change, there is a growing need for accurate, real-time data on water quality in river waterways. This results in the development of advanced monitoring systems. This article presents a remote water quality monitoring system designed specifically for use in inland waterways, the basic elements of which are placed in a buoy with an IoT unit. The proposed system uses a network of sensors strategically placed along the waterway to continuously measure critical parameters: temperature, pH, dissolved oxygen, and conductivity. Various compatibility, efficiency, and ease-of-use tests have been conducted to verify each aspect of the monitoring system. It has been shown that the sensors operate within the intended accuracy ranges. The central unit equipped with a GSM (Global System for Mobile Communications) module can wirelessly transmit data to a main server, enabling remote access and analysis via a user-friendly interface of the developed application. The paper details the technical architecture of the system, the integration of GSM technology to ensure reliable data transmission, and the results of the monitoring studies of the proposed parameters. The remote monitoring system offers significant benefits in terms of early detection of pollution events, ensuring the safety of aquatic life, and supporting sustainable navigation practices. The research results highlight the potential of GSM-based remote monitoring systems to revolutionize water quality management in waterways in various regions. Full article

Underwater cultural heritage (UCH) is a valuable resource that preserves humanity’s historical legacy, offering insights into traditions and civilisations. Despite its significance, UCH faces threats from inadequate regulatory frameworks, insufficient conservation technologies, and climate-induced environmental changes. This paper proposes an innovative platform combining the internet of underwater things and edge computing technologies to enhance UCH’s real-time monitoring, localisation, and management. The platform processes data through a central unit installed on a buoy near heritage sites, enabling efficient data analysis and decision making without relying on cloud connectivity. Integrating acoustic communication systems, LoRa technology, and nonterrestrial networks supports a robust multilayered communication infrastructure for continuous operation, even in remote maritime areas. The platform’s edge node deploys artificial intelligence models for real-time risk assessment, focusing on key environmental parameters to predict and mitigate corrosion rates and climate-related threats. A case study illustrates the system’s capabilities in underwater localisation, demonstrating how edge computing and acoustic triangulation techniques enable precise tracking. Full article

Semi-submersible platforms are used in the offshore oil and gas industry. They are specialised marine vessels that float on submersed drafts, which are composed of pontoons and columns and can serve as habitats for biofouling marine benthic communities. When these vessels sail from one place to another, either by using their own propellers or being towed, they can act as vectors for introducing non-native marine species. To establish themselves in new areas, these exotic species require suitable benthic habitats. Artificial substrates, such as harbour infrastructure where such vessels are moored, appear to be highly suitable for this purpose. In the present study, a mooring buoy and a harbour piling at Curaçao (southern Caribbean), frequently used by semi-submersible platforms, were found to be colonised by the sun corals Tubastraea coccinea and T. tagusensis at shallow depths. This report presents the first record of T. tagusensis as an introduced non-native species in the southern Caribbean, highlighting the potential role of harbour infrastructure in facilitating coral settlement at depths shallower than those typically observed. These findings underscore the ecological impact of artificial substrates in supporting invasive species and emphasise the need for monitoring programs and defouling facilities. Full article

Ocean buoys play a critical role in marine hydrological, water quality, and meteorological monitoring, with applications in navigation, environmental observation, and communication. However, accurately modeling and deploying a multi-buoy system in the complex marine environment presents significant challenges. To address these challenges, this study proposes an enhanced deployment strategy using the tuna swarm optimizer enhanced with the fractional-order calculus method for marine observation. The proposed method first introduces a detailed observation model that precisely captures the performance of buoys in terms of coverage and communication efficiency. By integrating the observation coverage ratio and communication energy consumption, we establish an optimal multi-buoy deployment model. The proposed method leverages tent chaotic mapping to improve the diversity of initial solution generation and incorporates fractional-order calculus to strengthen its search capabilities. Simulation experiments and statistical analysis verify the effectiveness of the proposed deployment model, with the proposed method achieving the best performance in deploying the multi-buoy system, reaching a final fitness value of 0.190052 at iteration 449, outperforming TSA, PSO, GWO, and WOA. These results highlight the potential of the proposed method in optimizing multi-buoy system deployment in marine observation. Full article

Real-time data transmission and reliable operation are essential for a tsunami monitoring system to provide effective data. In this study, a novel real-time tsunami monitoring system is designed based on a submersible mooring system. This system is equipped with a data acquisition and tsunami wave identification algorithm, which can collect the measured data of the pressure sensor and detect a tsunami wave in real time. It adopts the combination design of underwater inductive coupling transmission and a redundant BeiDou communication device on the water surface to ensure the reliability of real-time data transmission. Compared with traditional tsunami monitoring buoys, it has the advantages of reliable communication, good concealment, high security, and convenient deployment, recovery, and maintenance. The results of laboratory and sea tests show that the system has high reliability of data transmission, stable overall operation of the system, and good application prospects in the field of real-time tsunami monitoring and early warning. Full article

In base-station-based underwater wireless acoustic networks (B-UWANs), effective handover mechanisms are necessary to ensure seamless data services for mobile nodes such as autonomous underwater vehicles (AUVs). Unlike terrestrial base stations (BSs), moored buoy BSs in B-UWANs experience motion responses due to wave loads under environmental conditions, posing unique challenges to the handover process. This study examines how BS motion affects handover decision errors, which arise when AUVs incorrectly initiate handovers to unintended BSs due to BS motion. By utilizing the AUV–BS distance as a handover triggering parameter, our analysis reveals a significant increase in decision errors within the overlapping regions when both the current and target BSs are in motion, especially when moving in the same direction. In addition, these errors intensify with the magnitude of BS motion and are exacerbated by smaller BS network radii. Based on these simulation results, we present an analytical framework that not only measures the influence of BS motion on the AUV–BS distance but also provides strategic insights for refining underwater handover protocols, thereby enhancing operational reliability and service continuity in B-UWANs. Full article

In order to address the requirements of scientific multidisciplinary observation in diverse small-scale regions, we have introduced the Buoy-based Cable Seafloor Observatory System (BCSOS). This system offers a distinct advantage in contexts where the use of shorter cables is feasible, contrasting with the lengthy cables typically necessary for conventional observatories. The BCSOS consists of three primary components: the Real-Time Electric Communication (RTEC) Buoy, the Power Information Transmission System (PITS), and the Seafloor Observation Subsystem (SOS). The RTEC Buoy is equipped with instruments for measuring sea surface parameters and serves as a data and power hub. The PITS, comprising a robust EM cable, connects the buoy to the SOS, which houses instruments for seafloor observations. The system is designed for a maximum water depth of 100 m and has an expected lifespan of about 5 years. The BCSOS prototypes were deployed at the Huangqi Peninsula, Fujian Province, and successfully documented the process during Typhoon Doksuri (international code 2305) at the end of July 2023. The recorded data from the BCSOS revealed a significant increase in wave height and period as the storm approached the Huangqi Peninsula. Additionally, the RTEC buoy exhibited a notable response to the large waves. The data analysis revealed a distinct pattern between the buoy response and the direction of wave propagation across various sea conditions, that the buoy’s angular movement in pitch and roll directions follows a regular elliptical distribution corresponding to different wave propagation directions. Upon thorough evaluation, future enhancements to the system are slated to concentrate on refining its design, with a particular emphasis on bolstering stability and enhancing corrosion resistance. These improvements are aimed at cementing the system’s long-term viability and performance within the challenging marine environment. Full article