Analysis of Security Attacks and Taxonomy in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSN) have gained more attention from researchers in recent years due to their advancement in marine monitoring, deployment of various applications, and ocean surveillance. The UWSN is an attractive field for both researchers and the industrial side. Due to the harsh underwater environment, own capabilities, and open acoustic channel, it is also vulnerable to malicious attacks and threats. Attackers can easily take advantage of these characteristics to steal the data between the source and destination. Many review articles are addressed some of the security attacks and taxonomy of the Underwater Wireless Sensor Networks. In this study, we have briefly addressed the taxonomy of the UWSNs from the most recent research articles related to the well-known research databases. This paper also discussed the security threats on each layer of the Underwater Wireless sensor networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.

Research on Internet of Things Technology for Intelligent Three-Dimensional Online Monitoring System of Marine Ranch

The online monitoring network of marine ranching ecological environment is one of the important development directions of marine ranching construction. Traditional marine monitoring systems are large in size, high in power consumption, and expensive. The investment in pasture construction cannot achieve a large amount of investment to form a monitoring network. There is an urgent need for a low-cost marine monitoring method that can afford a large area and high spatial density. This problem cannot be realized in traditional technology. Based on MEMS technology, this paper develops low-cost monitoring small floats that can be deployed in a large number of “swarm bees”, with high spatial and temporal resolution, and take into account environmental and biological population characteristics monitoring. Each small float is connected to a multi-node sensor chain and video monitoring node. The low-cost surface and underwater wireless transmission and wireless communication means are used for networking observation, which proposes a brand-new solution for the construction of the intelligent three-dimensional monitoring of the marine pasture environment.

Offshore Wind and Wave Energy Complementarity in the Greek Seas Based on ERA5 Data

In this work, 20 years (2000–2019) of ERA5 wave and wind data are analyzed and evaluated for the Greek Seas by means of in-situ measurements derived from the POSEIDON marine monitoring system. Four different statistical measures were used at six locations, where in-situ wind and wave measurements are available from oceanographic buoys. Furthermore, the ERA5 wind and wave datasets were utilized for the estimation of the available wind and wave energy potential for the Greek Seas, as well as for the assessment of complementarity and synergy between the two resources. In this respect, an event-based approach was adopted. The spatial distribution of the available wind and wave energy potential resembles qualitatively and quantitatively the distributions derived from other reanalysis datasets. Locations with high synergy and complementarity indices were identified taking into account water depth. Finally, taking into consideration a particular offshore wind turbine power curve and the power matrix of the PELAMIS wave energy converter, the estimation of the combined energy potential on a mean annual basis is performed.

Marine Monitoring for Offshore Geological Carbon Storage—A Review of Strategies, Technologies and Trends

Carbon capture and storage (CCS) could significantly contribute to reducing greenhouse gas emissions and reaching international climate goals. In this process, CO2 is captured and injected into geological formations for permanent storage. The injected plume and its migration within the reservoir is carefully monitored, using geophysical methods. While it is considered unlikely that the injected CO2 should escape the reservoir and reach the marine environment, marine monitoring is required to verify that there are no indications of leakage, and to detect and quantify leakage if it should occur. Marine monitoring is challenging because of the considerable area to be covered, the limited spatial and temporal extent of a potential leakage event, and the considerable natural variability in the marine environment. In this review, we summarize marine monitoring strategies developed to ensure adequate monitoring of the marine environment without introducing prohibitive costs. We also provide an overview of the many different technologies applicable to different aspects of marine monitoring of geologically stored carbon. Finally, we identify remaining knowledge gaps and indicate expected directions for future research.

On Using Lagrangian Drift Simulations to Aid Interpretation of in situ Monitoring Data

One key challenge of marine monitoring programs is to reasonably combine information from different in situ observations spread in space and time. In that context, we suggest the use of Lagrangian transport simulations extending both forward and backward in time to identify the movements of water bodies from the time they were observed to the time of their synopsis. We present examples of how synoptic maps of salinity generated by this method support the identification and tracing of river plumes in coastal regions. We also demonstrate how we can use synoptic maps to delineate different water masses in coastal margins. These examples involve quasi-continuous observations of salinity taken along ferry routes. A third application is the synchronization of measurements between fixed stations and nearby moving platforms. Both observational platforms often see the same water body, but at different times. We demonstrate how the measurements from a fixed platform can be synchronized to measurements from a moving platform by taking into account simulation-based time shifts.

Detection of Macrobenthos Species With Metabarcoding Is Consistent in Bulk DNA but Dependent on Body Size and Sclerotization in eDNA From the Ethanol Preservative

DNA metabarcoding is a promising method to increase cost and time efficiency of marine monitoring. While substantial evidence exists that bulk DNA samples adequately reflect diversity patterns of marine macrobenthos, the potential of eDNA in the ethanol preservative of benthic samples for biodiversity monitoring remains largely unexplored. We investigated species detection in bulk DNA and eDNA from the ethanol preservative in samples from four distinct macrobenthic communities in the North Sea. Bulk DNA and eDNA were extracted with different extraction kits and five COI primer sets were tested. Despite the availability of a nearly complete reference database, at most 22% of the amplicon sequence variants (ASVs) were assigned taxonomy at the phylum level. However, the unassigned ASVs represented only a small fraction of the total reads (13%). The Leray primer set outperformed the four other primer sets in the number of non-chimeric reads and species detected, and in the recovery of beta diversity patterns. Community composition differed significantly between bulk DNA and eDNA samples, but both sample types were able to differentiate the four communities. The probability of detecting a species in the eDNA from the ethanol preservative was significantly lower than for bulk DNA for macrobenthos species having small to medium body size and for species having chitine or CaCO3 in their cuticula. Detection in the bulk DNA samples was not affected by the investigated morphological traits, indicating that monitoring of macrobenthos species will be most robust when using bulk DNA as template for metabarcoding.

Cost-efficiency assessments of marine monitoring methods lack rigor—a systematic mapping of literature and an end-user view on optimal cost-efficiency analysis

Global deterioration of marine ecosystems, together with increasing pressure to use them, has created a demand for new, more efficient and cost-efficient monitoring tools that enable assessing changes in the status of marine ecosystems. However, demonstrating the cost-efficiency of a monitoring method is not straightforward as there are no generally applicable guidelines. Our study provides a systematic literature mapping of methods and criteria that have been proposed or used since the year 2000 to evaluate the cost-efficiency of marine monitoring methods. We aimed to investigate these methods but discovered that examples of actual cost-efficiency assessments in literature were rare, contradicting the prevalent use of the term “cost-efficiency.” We identified five different ways to compare the cost-efficiency of a marine monitoring method: (1) the cost–benefit ratio, (2) comparative studies based on an experiment, (3) comparative studies based on a literature review, (4) comparisons with other methods based on literature, and (5) subjective comparisons with other methods based on experience or intuition. Because of the observed high frequency of insufficient cost–benefit assessments, we strongly advise that more attention is paid to the coverage of both cost and efficiency parameters when evaluating the actual cost-efficiency of novel methods. Our results emphasize the need to improve the reliability and comparability of cost-efficiency assessments. We provide guidelines for future initiatives to develop a cost-efficiency assessment framework and suggestions for more unified cost-efficiency criteria.

High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.