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Open AccessArticle

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, LT-08412 Vilnius, Lithuania
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(17), 3782; https://doi.org/10.3390/s19173782
Received: 12 July 2019 / Revised: 26 August 2019 / Accepted: 28 August 2019 / Published: 31 August 2019
(This article belongs to the Collection Positioning and Navigation)
The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly. View Full-Text
Keywords: streaming sensors data; neural network retrain time; model sensitivity and precision; marine traffic anomaly detection; SOM data batching streaming sensors data; neural network retrain time; model sensitivity and precision; marine traffic anomaly detection; SOM data batching
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Venskus, J.; Treigys, P.; Bernatavičienė, J.; Tamulevičius, G.; Medvedev, V. Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding. Sensors 2019, 19, 3782.

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