Utilizing the fuzzy IoT to reduce Green Harbor emissions

Abstract

The global trend toward “Green Harbors” has now become a vital topic. In the management of maritime pollution, government officials have paid increasing attention to air pollution from ship and port activities. Static and dynamic vessel data are provided by the Automatic Identification System (AIS). This is a telemetric system that automatically transmits information about a vessel to ports and ships in the vicinity; this is then combined with the Internet of Things (IoT). This study integrates the Marine Geography Information System (MGIS) and Fuzzy Division theory with the Spatial Data Mining (SDM) method to propose a system that calculates the optimal speed for any inbound container ship. This will not only reduce harbor air pollution, it will also ensure safe port entry and exit for all vessels. Seven algorithm blocks were used for the fuzzy logic control (FLC) inputs: Turning Capacity (TC), Crush Stop Capacity (CSC), Wind and Current Effect (WACE), Ship Operating Capacity Index (SOCI), Environment Influence (EI), Ship Maneuvering Capacity Index (SMCI), and Green Port Index (GPI); the fuzzy logic system produced one output, namely Optimum Speed (OS). This method provides the OS for the captain and thus the reference navigation speed for the Vessel Traffic Service (VTS). Hence, by limiting speed, the system can suppress air pollution, ensure navigation safety, and improve the efficiency of the port.