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The Automatic Identification System (AIS) is the emerging system for automatic traffic control and collision avoidance services in the maritime transportation sector. It is one of the cornerstone systems for improved marine domain awareness and is embedded in e-navigation, e-bridging, and autonomous ships proposals. However, AIS has some security vulnerabilities that can be exploited to invade privacy of passengers, to launch intentional collision attacks by pirates and terrorists, etc. In this work, we explore how Identity-Based Public Cryptography and Symmetric Cryptography may enhance the security properties of the AIS.

Boating tourism in coastal-maritime areas often overlaps spatially and temporally with other economic activities, such as fishing, leading to complex interactions. These interactions can create opportunities for positive cooperation or generate conflicts that pressure natural resources and stakeholders. The aim of this study is to show whether or not there is evidence of interactions between fishing (nf = 43) and tourism/recreation (nt = 65) vessels. This study focuses on the interaction between maritime tourism activities and fishing in southern Portugal, using a social network analysis (SNA) approach based on automatic identification system (AIS) data to evaluate spatial and temporal patterns. The findings reveal that tourism activities dominate zones closer to the coast, with intermediate areas serving as shared spaces where interactions between vessel activities are more likely to occur. There was evidence of occasional interactions between a few recreational and fishing vessels (two passengers and three seiners), but the inferences from the results are insufficient to demonstrate how beneficial they are for both activities.

This paper presents the results of research on the fusion of tracking radar and an Automatic Identification System (AIS) in an Electronic Chart Display and Information System (ECDIS). First, the concept of these systems according to the International Maritime Organization (IMO) is described, then a set of theoretical information on radar tracking and the fusion method itself is given and finally numerical results with real data are presented. Two methods of fusion, together with their parameters, are examined. A proposal for calculating the covariance matrix for radar and AIS data is also given, and the paper ends with conclusions.

Inland waterway transport is an essential element of integrated transport systems, and the inland waterway freight volume accounts for about 50% of the total waterway freight volume in China. During the navigation, anchoring, and operation of ships, various water pollutants are generated, and the pollution generated by sewage is receiving more and more attraction. To prevent and control pollution from ships, it is important to estimate the amount of sewage and pollutants involved. In this study, the data preparation process is established to generate the Degree of Ship Activity (DSA) data pool after cleaning and thinning the massive original Automatic identification System (AIS) data, and then the data fusion method of a fine GIS grid is established to integrate the DSA data into each grid. The total DSA in the lower reaches of the Yangtze River is 37.14 million h/a. The sewage and pollutant generation inventories for the lower reaches of Yangtze River are estimated and analyzed spatiotemporally. It is estimated that the generations of sewage are 1,768,600 t/a in total. After spatial analysis, it is revealed that the water areas with a relatively large amount of pollutant generation are mainly related to ports distributed along the channel and the DSA density. Finally, based on the spatial distribution characteristics of the estimated inventories, the countermeasures of “zero discharge” for inland ships, the receiving facility system improving, and prevention and control at the river basin level are proposed.

The Philippines has acquired access to the NovaSAR-1 satellite developed by Surrey Satellite Technology, Ltd. (SSTL) for the implementation of its project Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for Innovative Terrestrial Monitoring and Maritime Surveillance, which will provide simultaneous S-band SAR imaging with AIS data for applications targeted on improving maritime domain awareness. The country has inherent challenges in this field due to its archipelagic nature, with earth observation seen as a potential solution as it provides an immediate and wide coverage over designated priority areas. This contributes toward achieving Sustainable Development Goal 14: Life Below Water by providing objective information in support of data-driven decision and policymaking, closing knowledge gaps in monitoring Philippine waters.

To solve the configuration issue when using International Electrotechnical Commission (IEC) 61850 for distributed intelligence in Distribution Automation Systems (DAS), this paper proposes the configuration solution in terms of semantic models and processing methods. Firstly, the special requirements of the DAS configuration are analyzed, consisting of the system boundary of a configuration project, the topology configuration for distributed applications, and the automatic identification of the Intelligent Electronic Devices (IED). The new models of Process, Line, and other elements are then presented based on the System Configuration Language (SCL) to describe the distribution network topology. The planned contents are allocated into a new format of the Configured IED Description (CID) file to realize the distributed applications. A register service is designed, which fulfills the automatic identification of IEDs when they are remotely placed into a DAS. The service checks the configuration status in real-time and automates the whole configuration engineering process. The case study shows that the proposed solution allows an IED to detect the real-time topology and re-establish the data flow configuration with peer IEDs independently from the master station; thus the distributed applications can be performed more autonomously and efficiently.

Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship’s trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.

This study proposes an Automatic Identification System (AIS)-based spatiotemporal allocation methodology to estimate catch distribution and fishing effort for large purse seine fisheries in Korean waters. AIS trajectory data from July 2019 to June 2022 were analyzed to identify fishing grounds, while carrier vessel port-entry records were used to estimate daily landings. These were allocated to specific fishing segments to derive spatially explicit catch quantities. Compared with periodic surveys or voluntary reports, the AIS-based approach significantly enhanced the accuracy of fishing ground identification and the reliability of catch estimation. The results showed that fishing activity peaked between November and February, with the highest catch densities observed south of Jeju Island and in adjacent East China Sea waters. Catch declined markedly from April to June due to the mackerel closed season. These findings demonstrate the method’s potential for evaluating the effectiveness of Total Allowable Catch (TAC) regulations, supporting dynamic and adaptive management frameworks, and strengthening IUU fishing monitoring. Although the current analysis is limited to TAC-regulated species, AIS-equipped vessels, and a three-year dataset, future studies could expand the timeframe, integrate environmental data, and apply this methodology to other fisheries to improve sustainable resource management.

This paper investigates the Additional Secondary Phase Factor (ASF) characteristics of Automatic Identification System (AIS) signals spreading over a rough sea surface. According to the change of the ASFs for AIS signals in different signal form, the influences of the different propagation conditions on the ASFs are analyzed. The expression, numerical calculation, and simulation analysis of the ASFs of AIS signal are performed in the rough sea surface. The results contribute to the high-accuracy propagation delay measurement of AIS signals spreading over the rough sea surface as, well as providing a reference for reliable communication link design in marine engineering for Very High Frequency (VHF) signals.

Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.