Next Article in Journal
Multi-Scale DenseNets-Based Aircraft Detection from Remote Sensing Images
Previous Article in Journal
Laboratory Calibration and Field Validation of Soil Water Content and Salinity Measurements Using the 5TE Sensor
Open AccessArticle

Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning

1
Department of Marine Industry and Maritime Police, Jeju National University, Jeju 64343, Korea
2
Dept. of Computer Science, Chungbuk National University, Cheongju 28644, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5273; https://doi.org/10.3390/s19235273
Received: 8 October 2019 / Revised: 20 November 2019 / Accepted: 28 November 2019 / Published: 29 November 2019
(This article belongs to the Section Sensor Networks)
Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency. View Full-Text
Keywords: big data; stream data; context-aware service; distributed and parallel processing; vessel traffic service; maritime traffic stream sensor data big data; stream data; context-aware service; distributed and parallel processing; vessel traffic service; maritime traffic stream sensor data
Show Figures

Figure 1

MDPI and ACS Style

Kim, K.-I.; Lee, K.M. Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors 2019, 19, 5273.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop