Search Results (197)

Smart fisheries are an important way to promote the sustainable development of fisheries, and efficient and reliable marine communication systems are the key to realizing smart fisheries. As an emerging marine communication technology, the VHF Data Exchange System (VDES) has the advantages of a high data transmission rate, large communication capacity, and wide coverage, providing new opportunities for the transformation and upgrading of smart fisheries. This paper introduces the technical architecture and functions of the VDES, compares it with existing marine communication technologies, analyzes the key requirements of a smart fishery, and assesses how the VDES meets these requirements. The potential application scenarios of the VDES in smart fishery fields such as fishing vessel monitoring, fishery resource management, and maritime security are discussed. The challenges faced by the VDES in the application of smart fisheries, such as technology, policies and regulations, and construction cost layout, are analyzed, and its future development trend is prospected. Suggestions such as its integration with emerging technologies, the realization of global seamless coverage, and the strengthening of international cooperation and data sharing are proposed. This paper aims to provide theoretical guidance and scientific reference for the promotion and application of the VDES in smart fisheries. Full article

The narrow-leaf ash (Fraxinus angustifolia) is distributed across southern Europe and northern Africa. Descriptions from 1751 report the presence of a large number of these trees in the Maritime Department of Cartagena; however, their numbers are actually greatly reduced in the Region of Murcia. This species is protected and finds refuge in riverbeds within the semi-arid environment of southeastern Spain. The desertification affecting this area of continental Europe has a significant impact on natural systems, with the ash being particularly vulnerable due to its water requirements. The objective of this study was to identify the locations of individual ash trees and assess the conditions of their surrounding environment. A literature review was conducted, and based on the environmental conditions necessary for their survival, the entire region was surveyed using GPS. A total of 670 trees were geolocated. The majority are situated in riverbeds. They are absent from the southern coastal zone and at higher altitudes above 1000 m above sea level in the Region of Murcia. Monitoring its reduction, expansion, and development could provide insights into how climate change is evolving and the potential extinction of this species in arid and semi-arid regions. Full article

The accurate prediction of marine shaft centerline trajectories is essential for ensuring the operational performance and safety of ships. In this study, we propose a novel Transformer-based model to forecast the lateral and longitudinal displacements of ship main shafts. A key challenge in this prediction task is capturing both short-term fluctuations and long-term dependencies in shaft displacement data, which traditional models struggle to address. Our Transformer-based model integrates Bidirectional Splitting–Agg Attention and Sequence Progressive Split–Aggregation mechanisms to efficiently process bidirectional temporal dependencies, decompose seasonal and trend components, and handle the inherent symmetry of the shafting system. The symmetrical nature of the shafting system, with left and right shafts experiencing similar dynamic conditions, aligns with the bidirectional attention mechanism, enabling the model to better capture the symmetric relationships in displacement data. Experimental results demonstrate that the proposed model significantly outperforms traditional methods, such as Autoformer and Informer, in terms of prediction accuracy. Specifically, for 96 steps ahead, the mean absolute error (MAE) of our model is 0.232, compared to 0.235 for Autoformer and 0.264 for Informer, while the mean squared error (MSE) of our model is 0.209, compared to 0.242 for Autoformer and 0.286 for Informer. These results underscore the effectiveness of Transformer-based models in accurately predicting long-term marine shaft centerline trajectories, leveraging both temporal dependencies and structural symmetry, thus contributing to maritime monitoring and performance optimization. Full article

The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system. The performance of the YOLOv8n-cls model was assessed for the ice classification modules due to its fast inference speed, making it suitable for resource-constrained onboard systems. The training and evaluation were conducted across multiple platforms, including Roboflow, Google Colab, and Compute Canada, allowing for a detailed comparison of their capabilities in image preprocessing, model training, and real-time inference generation. The results demonstrate that Image Classification Module I achieved a validation accuracy of 99.4%, while Module II attained 98.6%. Inference times were found to be less than 1 s in Colab and under 3 s on a stand-alone system, confirming the architecture’s efficiency in real-time ice condition monitoring. Full article

The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions. Full article

With the rapid advancement of underwater communication and unmanned aerial vehicle (UAV) technologies, the potential applications of cross-medium communication in environmental monitoring, maritime Internet of Things (IoTs), and rescue operations, in particular, have attracted great attention. This study explores the feasibility of achieving cross-medium direct acoustic communication through the air–water interface. Specifically, it investigates challenges such as acoustic impedance mismatches and signal attenuation caused by energy loss during interface transmission, aiming to understand their impact on communication performance. Experimental tests employed underwater acoustic transducers as signal transmitters to propagate sound waves directly into the air, attempting to establish communication links with aerial UAV nodes. Preliminary experimental results indicate that even conventional underwater acoustic transducers can achieve information exchange between underwater nodes and UAVs, laying a foundation for further research and application of cross-medium communication systems. Full article

The maritime industry is undergoing a paradigm shift driven by rapid advancements in wireless communication and an increase in maritime traffic data. However, the existing automatic identification system (AIS) struggles to accommodate the increasing maritime traffic data, leading to the introduction of the very-high-frequency (VHF) data exchange system (VDES). While the VDES increases bandwidth and data rates, ensuring the stable transmission of maritime IoT (MIoT) application data in congested coastal areas remains a challenge due to frequent collisions of AIS messages. This paper presents a slot occupancy-based collision avoidance algorithm (SOCA) for a VDES network in the MIoT. SOCA is designed to mitigate the impact of interference caused by transmissions of AIS messages on transmissions of VDE-Terrestrial (VDE-TER) data in coastal areas. To this end, SOCA provides four steps: (1) construction of the neighbor information table (NIT) and VDES frame maps, (2) construction of the candidate slot list, (3) TDMA channel selection, and (4) slot selection for collision avoidance. SOCA operates by constructing the NIT based on AIS messages to estimate the transmission intervals of AIS messages and updating VDES frame maps upon receiving VDES messages to monitor slot usage dynamically. After that, it generates a candidate slot list for VDE-TER channels, classifying the slots into interference and non-interference categories. SOCA then selects a TDMA channel that minimizes AIS interference and allocates slots with low expected occupancy probabilities to avoid collisions. To evaluate the performance of SOCA, we conducted experimental simulations under static and dynamic ship scenarios. In the static ship scenario, SOCA outperforms the existing VDES, achieving improvements of 13.58% in aggregate throughput, 11.50% in average latency, 33.60% in collision ratio, and 22.64% in packet delivery ratio. Similarly, in the dynamic ship scenario, SOCA demonstrates improvements of 7.30%, 11.99%, 39.27%, and 11.82% in the same metrics, respectively. Full article

Background: In recent decades, the size and passenger capacity of cruise/passenger ships has been associated with noticeable growth; in turn, this has created significant concerns regarding the adequacy of existing evacuation protocols during an “abandon the ship” situation (life threatening emergency). This study provides a systematic overview of related weaknesses and challenges, identifying critical factors that influence evacuation efficiency, and also proposes innovative/interdisciplinary solutions to address those challenges. It further emphasizes the growing complexity of cruise/passenger ship evacuations due to increased vessel size/heavy density of human population, as well as identifying the necessity of addressing both technical and human-centered elements to enhance safety and efficiency of those specific operations. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, a comprehensive systematic literature search was conducted across academic databases, including Scopus, Science Direct, Google Scholar, and a limited number of academic journals that are heavily maritime-focused in their mission. Emphasis was placed on peer-reviewed articles and certain gray studies exploring the impacts of ship design, human behavior, group dynamics, and environmental conditions on evacuation outcomes. This review prioritized research incorporating advanced simulation models, crowd management solutions (applied in various disciplines, such as stadiums, airports, malls, and ships), real-world case studies, and established practices aligned with contemporary maritime safety standards. Results: The key findings identify several critical factors influencing the overall evacuation efficiency, including ship heeling angles, staircase configurations, and passenger (physical) characteristics (with their mobility capabilities and related demographics clearly standing out, among others). This effort underscores the pivotal role of group dynamics, including the influence of group size, familiarity among the group, and leader-following behaviors, in shaping evacuation outcomes. Advanced technological solutions, such as dynamic wayfinding systems, real-time monitoring, and behavior-based simulation models, emerged as essential tools for optimizing an evacuation process. Innovative strategies to mitigate identified challenges, such as phased evacuations, optimized muster station placements, and tailor made/strategic passenger cabin allocations to reduce congestion during an evacuation and enhance the overall evacuation flow, are also highlighted. Conclusions: Protecting people facing a life-threatening situation requires timely preparations. The need for a holistic evacuation strategy that effectively integrates specific ship design considerations and human factors management, along with inputs related to advanced information technology-related solutions, is the best way forward. At the same time, the importance of real-time adaptive management systems and interdisciplinary approaches to address the challenges of modern cruise/passenger ship evacuations clearly stands out. These findings provide a robust foundation for future research and practical applications, contributing to advancements in maritime safety and the development of efficient evacuation protocols for large-in-size cruise/passenger vessels. Full article

In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sensitive Cross Attention module and the Self-Attention module, with a particular focus on enhancing small object detection performance in low-resolution IR imagery. By leveraging a multi-level attention mechanism, the model effectively improves detection performance for both small and large objects, outperforming the baseline YOLOv8 model. To further optimize the performance of IR cameras, we introduced a color palette preprocessing technique and identified the optimal palette through a comparative analysis. Experimental results demonstrated that the Average Precision increased from 85.3 to 88.2 in EO camera images and from 68.2 to 73 in IR camera images when the Black Hot palette was applied. The Black Hot palette, in particular, provided high luminance contrast, effectively addressing the single-channel and low-resolution limitations of IR imagery, and significantly improved small object detection performance. The proposed technique shows strong potential for enhancing vessel detection performance under diverse environmental conditions and is anticipated to make a practical contribution to real-time maritime monitoring systems. Furthermore, by delivering high reliability and efficiency in data-constrained environments, this method demonstrates promising scalability for applications in various object detection domains. Full article

A novel tracking and monitoring system for ISO 668 dry containers was realized by the ESA-funded “5G SENSOR@SEA” project, integrating 5G cellular technologies for massive Internet of Things with a GEO satellite-optimized backhauling link. The scope is the development of monitoring and tracking new services for multimodal container shipping. With the cooperation of four industrial partners and a telecommunication research center, the so-called “5GT System” was designed, developed, tested and validated up to field trials. Several modules of the system were designed, built and finally installed on the ship and in the teleport: the container tracking devices placed on the containers, the NB-IoT cellular network with optimized satellite backhauling, the Ku-band satellite terminals and the maritime service platform based on the OneM2M standard. The field trial conducted during the intercontinental liner voyage of a container ship showed primary technical achievements, including fair switching between terrestrial and satellite networks, reduction in packet loss in the open sea scenario and seamless integration of the BLE mesh network over the container tracking devices as NB-IoT/BLE LE Mesh gateways. 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

This study addresses the challenges associated with single-system long-baseline real-time kinematic (RTK) navigation, including limited positioning accuracy, inconsistent signal reception, and significant residual atmospheric errors following double-difference corrections. This study explores the effectiveness of long-baseline RTK navigation using an integrated system of the BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), and Galileo Satellite Navigation System (Galileo). A long-baseline RTK approach that incorporates Kalman filtering and partial ambiguity resolution is applied. Initially, error models are used to correct ionospheric and tropospheric delays. The zenith tropospheric and inclined ionospheric delays and additional atmospheric error components are then regarded as unknown parameters. These parameters are estimated together with the position and ambiguity parameters via Kalman filtering. A two-step method based on a success rate threshold is employed to resolve partial ambiguity. Data from five long-baseline IGS monitoring stations and real-time measurements from a ship were employed for the dual-frequency RTK positioning experiments. The findings indicate that integrating additional GNSSs beyond the BDS considerably enhances both the navigation precision and the rate of ambiguity resolution. At the IGS stations, the integration of the BDS, GPS, and Galileo achieved navigation precisions of 2.0 cm in the North, 5.1 cm in the East, and 5.3 cm in the Up direction while maintaining a fixed resolution exceeding 94.34%. With a fixed resolution of Up to 99.93%, the integration of BDS and GPS provides horizontal and vertical precision within centimeters in maritime contexts. Therefore, the proposed approach achieves precise positioning capabilities for the rover while significantly increasing the rate of successful ambiguity resolution in long-range scenarios, thereby enhancing its practical use and exhibiting substantial application potential. Full article

In this study, we propose an advanced object detection model for fire and smoke detection in maritime environments, leveraging the DETR (Detection with Transformers) framework. To address the specific challenges of shipboard fire and smoke detection, such as varying lighting conditions, occlusions, and the complex structure of ships, we enhance the baseline DETR model by integrating EfficientNet-B0 as the backbone. This modification aims to improve detection accuracy while maintaining computational efficiency. We utilize a custom dataset of fire and smoke images captured from diverse shipboard environments, incorporating a range of data augmentation techniques to increase model robustness. The proposed model is evaluated against the baseline DETR and YOLOv5 variants, showing significant improvements in Average Precision (AP), especially in detecting small and medium-sized objects. Our model achieves a superior AP score of 38.7 and outperforms alternative models across multiple IoU thresholds (AP50, AP75), particularly in scenarios requiring high precision for small and occluded objects. The experimental results highlight the model’s efficacy in early fire and smoke detection, demonstrating its potential for deployment in real-time maritime safety monitoring systems. These findings provide a foundation for future research aimed at enhancing object detection in challenging maritime environments. Full article

The evolution of maritime surveillance is significantly marked by the incorporation of Artificial Intelligence and machine learning into Unmanned Surface Vehicles (USVs). This paper presents an AI approach for detecting and tracking unmanned surface vehicles, specifically leveraging an enhanced version of YOLOv8, fine-tuned for maritime surveillance needs. Deployed on the NVIDIA Jetson TX2 platform, the system features an innovative architecture and perception module optimized for real-time operations and energy efficiency. Demonstrating superior detection accuracy with a mean Average Precision (mAP) of 0.99 and achieving an operational speed of 17.99 FPS, all while maintaining energy consumption at just 5.61 joules. The remarkable balance between accuracy, processing speed, and energy efficiency underscores the potential of this system to significantly advance maritime safety, security, and environmental monitoring. Full article

Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience. Full article