Abstract
This paper aims to create an efficient container yard management system. For this purpose, a gantry crane management subsystem was designed and implemented for container information management based on the wireless sensor network (WSN). The system consists of a set of desktop management software and three different types sensor nodes, and locates the container via the two-way symmetrical time-of-arrival (TS-TOA) positioning approach. In this way, the subsystem enhances the accuracy of container positioning without elevating hardware cost. Through the implementation of the hardware and software systems, it is proved that the user can successfully locate the container, read and update cargo information, and complete similar operations through human-computer interaction in the desktop management software. Overall, this research greatly improves the efficiency of container management at the port, and helps to reduce the operational cost and port congestion.
Highlights
Recent years has seen a continued increase in international trade volume amidst economic globalization [1]
In light of the above, this paper introduces the structure of the wireless sensor network (WSN), discusses the application of WSN in the information management of containers [16], and designs and implements a subsystem based on gantry crane container positioning
Relying on the two-way symmetrical time-of-arrival (TS-time of arrival (TOA)) algorithm, the subsystem enhances the accuracy of container positioning without elevating hardware cost
Summary
Recent years has seen a continued increase in international trade volume amidst economic globalization [1]. Against this backdrop, container shipping becomes the dominant transport means of international trade, thanks to its large capacity and low freight rate [2,3]. There are numerous interconnected processes between the arrival and departure of a container at the port [5], such as cargo reporting, cargo unloading, and cargo placement These processes require the processing and exchange of a massive amount of data, which disqualifies manual identification, the error-prone, cost-intensive, and manpower-driven traditional method of data transfer and processing.
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