# A 5G-Based VR Application for Efficient Port Management
## 3 Application of 5G + VR in Smart Ports
### System Architecture  
The overall architecture of the \"5G + VR Smart Port\" system consists of four layers, including the terminal device layer, network layer, cloud layer, and platform service layer. The terminal layer includes high-definition cameras, VR helmets, smartwatches, and smart devices, to support services such as remote access, video capture, scene capture, action recognition, and operation presentation. The network layer includes 5G networks, intelligent IoT components, providing data connections for various business scenarios [22; 23; 24; 25]. The cloud platform provides extended network and computation capabilities for complex tasks using cloud computing technology. The platform layer includes various components such as broadcast control, content delivery, and mobile edge computing to enable port business control, cargo distribution, video analysis, and processing. Through VR, the scene in the port can be integrated virtualization, and the virtual port environment can be very intuitive. It can not only simulate various weather conditions well but also understand drainage systems, power supply systems, road traffic, and so on, at a glance. In the virtual environment, a variety of business drills can be comprehensively simulated, which can increase the fault tolerance rate of the smart port when completing various tasks. By controlling the VR handle or a mouse connected to a PC, the staff can control trucks and cranes to move goods to any warehouse efficiently. According to the business characteristics of smart ports and the requirements for transmission rate and delay, the major applications of 5G + VR in smart ports include autonomous driving, remote control of mechanical equipment, smart security, and emergency command, which are discussed elaborately in the following context. Figure 5 shows the smart port architecture. The system architecture first utilizes various types of sensors to sense and pre-process environmental information, which provide basic data support for positioning and complex environment awareness in the port, These sensors include intelligent beacons, as well as video surveillance, radar, satellite positioning, VDES, and other means equipped on autonomous navigation vessels, trucks loaded and allocated around smart port park. When facing some severe visual impaired conditions such as rain and fog in the port, the system may suffer temporary loss of visual information. The cumulative error of the Inertial Measurement Unit (IMU) is corrected by fusing millimeter wave radar, vision and BeiDou satellites. The multi-sensor based localization of multiple targets (e.g., obstacles, ships, bridges, etc.) can be used to solve ambiguous localization and generate target localization information in complex waters. This information is then integrated with systems based on VDES, radar, and AIS to generate information of fairway traffic and other vessels. In order to meet the needs of smart ports for comprehensive and accurate perception of complex environment, multi-sensors are combined with the hydrological and meteorological information released by air, sky, land, and water communication systems based on wireless mesh network, 5G, Beidou, and VDES to carry out cross-validation. Finally, it can provide a sensory transmission network with full coverage of the complex environment of the smart port. This paper focuses on the verification and analysis of these applications' feasibility. We have realized the model of autonomous driving, remote control, intelligent security, emergency management, and intelligent cargo management system in VR. Particularly, this paper implemented and performed an in-depth analysis on the remote control of mechanical equipment, controlling remote PLC to grasp cargo through VR under 5G network. The applications are described in detail as follows. * Autonomous Driving in Smart Port  
Autonomous trucks in smart ports can maintain depth perception, accuracy, and effectiveness even under terrible working conditions such as rain, snow, and night.