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Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.

The world’s oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosystem is threatened by human activities. Marine protected areas (MPAs) are distinctive spaces protected by law due to their unique characteristics, such as being the habitat of endangered marine species. Even with this protection, there are still illegal activities such as poaching or anchoring that threaten the survival of different marine species. In this context, we propose an autonomous surface vehicle (ASV) model system for the surveillance of marine areas by detecting and recognizing vessels through artificial intelligence (AI)-based image recognition services, in search of those carrying out illegal activities. Cloud and edge AI computing technologies were used for computer vision. These technologies have proven to be accurate and reliable in detecting shapes and objects for which they have been trained. Azure edge and cloud vision services offer the best option in terms of accuracy for this task. Due to the lack of 4G and 5G coverage in offshore marine environments, it is necessary to use radio links with a coastal base station to ensure communications, which may result in a high response time due to the high latency involved. The analysis of on-board images may not be sufficiently accurate; therefore, we proposed a smart algorithm for autonomy optimization by selecting the proper AI technology according to the current scenario (SAAO) capable of selecting the best AI source for the current scenario in real time, according to the required recognition accuracy or low latency. The SAAO optimizes the execution, efficiency, risk reduction, and results of each stage of the surveillance mission, taking appropriate decisions by selecting either cloud or edge vision models without human intervention.

IntroductionUnderwater target detection plays a crucial role in marine environmental monitoring and ocean exploration. However, accurate detection remains challenging due to low illumination, blurred small objects, and complex background interference. Although convolutional neural network-based detectors have improved detection performance, many existing approaches are computationally expensive, limiting their deployment on resource-constrained underwater platforms.MethodsTo address these challenges, we propose YOLOv8n-PFA, a lightweight and high-precision underwater object detection framework. The proposed method introduces a novel Parallel Fusion Attention (PFA) module that models channel and spatial attention in parallel using residual connections to enhance discriminative features while suppressing background noise. The Wise Intersection over Union (WIoUv3) loss is incorporated to stabilize training and improve localization accuracy. Additionally, depth-wise convolutions (DWConv) are strategically applied to reduce model parameters and computational complexity. To further validate generalization capability, the PFA module is also integrated into YOLOv11n.ResultsExperimental results show that YOLOv8n-PFA achieves 84.2% mean Average Precision (mAP) on the URPC2020 dataset with 2.68 M parameters and 7.7 GFLOPs, and 84.8% mAP on the RUOD dataset with 2.98 M parameters and 7.9 GFLOPs. When integrated into YOLOv11n, the model achieves 84.7% mAP on URPC2020 and 85.3% on RUOD with only 2.76 M parameters and 6.5 GFLOPs. Across both datasets, the proposed approach improves mAP by 2.8-4.1% over baseline models while maintaining a lightweight architecture.DiscussionThe results demonstrate that the proposed framework provides an effective and computationally efficient solution for real-time underwater target detection in challenging marine environments. The consistent performance gains across different YOLO generations further confirm the scalability and robustness of the proposed PFA module.

Monitoring marine litter in the Arctic is crucial for environmental assessment, yet automated methods are needed to process large volumes of visual data. This study develops and compares two distinct machine learning approaches to automatically detect floating marine litter, birds, and other anomalies from ship-based optical imagery captured in the Barents and Kara seas. We evaluated a supervised Visual Object Detection (VOD) model (YOLOv11) against a self-supervised classification approach that combines a Momentum Contrast (MoCo) framework with a ResNet50 backbone and a CatBoost classifier. Both methods were trained and tested on a dataset of approximately 10,000 manually annotated sea surface images. Our findings reveal a significant performance trade-off between the two techniques. The YOLOv11 model excelled in detecting clearly visible objects like birds with an F1-score of 73%, compared to 67% for the classification method. However, for the primary and more challenging task of identifying marine litter, which demonstrates less clear visual representation in optical imagery, the self-supervised approach was substantially more effective, achieving a 40% F1-score, versus the 10% obtained for the VOD model. This study demonstrates that, while standard object detectors are effective for distinct objects, self-supervised learning strategies can offer a more robust solution for detecting less-defined targets like marine litter in complex sea-surface imagery.

Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements.

Cheng, R.-H.; Wang, S.-S.; Sun, L., and Gao, Y., 2020. A study of the marine environment monitoring technology. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 189-192. Coconut Creek (Florida), ISSN 0749-0208. With the increasing development and utilization of marine resources, the marine environment and ecology are greatly affected. In order to understand the status of marine environment and ecology, it is necessary to continuously carry out marine environmental monitoring, so as to carry out marine environmental protection measures in a timely and effective manner. Based on this, this paper first introduces the marine environment monitoring system based on wireless sensor, then analyzes the wireless sensor technology and its network differential compression coding algorithm, and finally gives the construction method of marine environment monitoring platform based on wireless network.

Chi, H.T.; Du, Y., and Brett, P.M., 2020. Design of a marine environment monitoring system based on the Internet of Things. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 256–260. Coconut Creek (Florida), ISSN 0749-0208. A good marine environment is the basis for the development and utilization of marine resources. Marine environment monitoring is an important part of marine environment protection. Therefore, the research on the marine environment monitoring technologies is of great significance. In order to realize intelligent monitoring of the marine environment, combining with the sensor technology, 4G communication technology and Zigbee communication technology, this study designed a marine environment monitoring system based on the Internet of Things (IoT). The system is composed of three parts: the information management subsystem, the data collection subsystem and the monitoring terminal subsystem; the structural composition and main implementation technologies of each subsystem are elaborated and the functions of each module in the system are tested by experiments. The test results show that, the IoT-based marine environment monitoring system can realize the functions of collecting, receiving, sending, analyzing, processing, querying and releasing marine environment data. This study provides a technical support for China's marine environment monitoring, and has important theoretical reference value for the research of intelligent marine environment monitoring in China.

IntroductionMarine litter is an issue affecting all regions of the World Ocean. To address the problem of World Ocean pollution, it is essential first and foremost to develop observation methodologies capable of providing objective assessments of marine litter density and its sources.MethodsOne of the most accessible yet still objective observation methods is visual imaging of the ocean surface followed by the analysis of the imagery acquired. The goal of our study is to develop a method for analyzing marine surface imagery capable of detecting anomalies, given that some of the anomalies would represent floating marine litter.ResultsFor this purpose, we apply our algorithm based on artificial neural networks trained within the contrastive learning framework, along with a classifier based on supervised machine learning method for analyzing optical imagery of sea surface.DiscussionThe approach we present in this study is capable of detecting anomalies such as floating marine litter, birds, unusual glare, and other atypical visual phenomena. We explored capabilities of the artificial neural networks we use in this study within two training approaches with subsequent comparison of the results. Within our sampling approach, we propose to utilize the ergodic property of sea wave fields, leading to significant spatial autocorrelation of image elements with a substantial correlation radius.

In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance.

BACKGROUND AND OBJECTIVES: This study aimed to investigate the long-term relationship between chlorophyll-a, sea surface temperature, and sea surface salinity monthly from January 2015 to December 2021. It was carried out in the Northern Bay of Bengal, which experiences extreme monsoons, in the southwest monsoon and northeast monsoon from June to September and November to February, respectively. Monsoon is the main cause of changes in chlorophyll-a, sea surface temperature and sea surface salinity.METHODS: The seasonal model was used to examine the relationship between these three parameters, which were obtained using the Copernicus Marine Environment Monitoring Service data. The seasonal model was used to observe periodic patterns and predict parameters based on their regularity. Meanwhile, Pearson’s correlation analysis was conducted to determine the relationship between chlorophyll-a, sea surface temperature and sea surface salinity.FINDINGS: This study found that the three parameters, namely chlorophyll-a, sea surface temperature, and sea surface salinity, follow the monsoon pattern, as shown in the seasonal model. The minimum value of chlorophyll-a occurred in February, March and April, while the maximum value of approximately 2 milligram per cubic meter occured at stations 1, 2, 3, 4, 5 and 7, but at 9 and 10, it increased to 12 - 14 mg/m3. This indicates that station positions are very sensitive to changes in chlorohophyll-a values. When the southwest monsoon occurred, it reached the maximum. Furthermore, the minimum sea surface temperature values occurred in January and at almost every station in the year. It was shown to be associated with the northeast monsoon, which causes winter. On the sea surface temperature graph, several peaks were observed in positive local extremes yearly at almost all stations. The maximum sea surface temperature occurred in May, June, and July, according to the shape of the graph, which peaked in the middle of the year. The sea surface salinity graph formed a peak and valley which occurred yearly in May or April, as well as September and October, respectively.CONCLUSION: Chlorophyll-a had 1 trough and 1 peak, with the sea surface temperature graph possessing only 1 peak, while the sea surface salinity graph had 1 peak and 1 trough, respectively. These graph patterns implied that chlorophyll-a first achieved a minimum value before reaching the máximum. The sea surface temperature graph had a maximum value in the middle of the year, while the minimum occurred at the beginning or end. Moreover, the sea surface salinity graph first reached the maximum value and then declined to the minimum. KEYWORDS: Coefficient of correlation; Copernicus Marine Environment Monitoring Service (CMEMS Data); Northern Bay of Bengal; Northeast monsoon; Seasonal model; Southwest monsoon.