Development of a Maritime Transport Emulator to Mitigate Data Loss from Shipborne IoT Sensors

Recently, the maritime logistics industry has been transitioning to smart logistics by leveraging such technologies as AI and IoT. In particular, maritime big data plays a significant role in providing various services, including ship operation monitoring and greenhouse gas emissions assessment, and is considered essential for delivering maritime logistics services. Marine big data comprise real-world data collected during ship operations, but it is susceptible to loss due to temporal and environmental constraints. To address this issue, an Emulator is proposed to generate supplemental data, including location data, data count, and average distance, using accumulated maritime transport data. This study proposes an Emulator that repetitively generates new data such as location data, data count, and average distance using maritime transport data accumulated up to now. The location data is generated using the cumulative distance and trigonometric ratios based on the location information of standard routes. The data count and average distance are calculated based on user-input parameters such as voyage time and data interval. The generated data is inserted into a database and monitored on a map in real time. Experiments were conducted using maritime transport route data, and the results validated the effectiveness of the Emulator.