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 comprises real-world data collected during ship operations, but it is susceptible to loss due to temporal and environmental constraints. Together with identifying and addressing the root causes of data loss, it is mandatory to supplement the loss by analyzing and utilizing the collected data. This study proposes an emulator that repetitively generates new data such as location data, data count, and average distance using maritime transport data cumulated 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. To evaluate the emulator's performance, experiments were conducted using the maritime transport route data, and the results demonstrated its effectiveness.