The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Lee, EunSu; Mokashi, Amit J.; Moon, Sung–Hoon; Kim, GeunSub

doi:10.3390/jmse7090287

Cited by 49 publications

(22 citation statements)

References 22 publications

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“…The AIS provides static, dynamic and voyagerelated data according to the Safety Of Life At Sea (SOLAS) convention, refs. ( [107][108][109]). AIS data are detailed in Figure 5; thus, the mappings of the architecture of the vessel and the transmitted signals are both essential to the identification of its vulnerabilities.…”

Section: H Automatic Identification System (Ais)mentioning

confidence: 99%

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

et al. 2022

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The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted.

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Section: H Automatic Identification System (Ais)mentioning

confidence: 99%

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

et al. 2022

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“…In addition, Ducruet et al [37] conducted some tests on the accuracy of Lloyd data, and found that it collected most of the marine transportation information and that the extracted data were enough to represent the global marine network flow. Lee et al [38] indicated that the use of AIS data was beneficial to the public, enterprises, institutions, etc., and that the accessibility and availability of AIS data should be greatly enhanced. Therefore, the AIS data used in this paper are conducive to a more comprehensive discussion of the maritime network, and thus can provide more accurate optimization suggestions for the sustainable development of the port.…”

Section: Relevant Research On Evaluation Datamentioning

confidence: 99%

Evaluation of Development Potential of Ports in the Yangtze River Delta Using FAHP-Entropy Model

et al. 2020

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Port development potential refers to the potential but unrealized status and capacity of ports, which can become a reality when external conditions permit. A correct analysis of port development potential helps to better formulate investment response plans and national development strategies, and finally achieve the sustainable development of the ports. Based on the Automatic Identification System (AIS) data, basic port data, hinterland city data, traffic network data, and relevant economic and policy data, we constructed an evaluation index system of port development potential, and evaluated the development potential of eight representative ports in the Yangtze River Delta port group of China with the methods of FAHP-entropy (FAHP—Fuzzy Analytical Hierarchy Process). The results show that: (1) The development potential of the port group in the Yangtze River Delta is positioned in the upper middle level; its development prospects are considerable, and other countries or ports could give priority of cooperation with it to maximize its benefits. (2) Port economy and policy are the primary core indicators affecting the development potential of ports, while per capita GDP (gross domestic product), number of berths, and port network status are the secondary core indicators affecting the development potential of ports. (3) Ports with larger development potential usually have one or more outstanding indicators, while the potential of ports with balanced development among all indicators is relatively weak.

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“…The AIS data represent the static data and the dynamic data of the ships. The dynamic data have information such as Maritime Mobile Communication Service Identification (MMSI) number, latitude and longitude, speed/course over ground, steering rate, and headings [36]. The static data include inherent information about a ship, such as the length, the breadth, the maximum draft, and the type.…”

Section: Datamentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

Cited by 49 publications

References 22 publications

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

Evaluation of Development Potential of Ports in the Yangtze River Delta Using FAHP-Entropy Model

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

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