# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 4 5G Support for Industrial IoT Applications Utilizing Robotics
### Miscellaneous Usage of 5G Technologies for Industrial Purposes  
**Network Function Virtualization (NFV)**--described in Section 3.4--can be used to support smart manufacturing in various ways. In papers [36; 37] the authors present a detailed use case that reflects the needs of real-world manufacturers using the experience from a large-scale manufacturing company. They propose an architecture with specific network services and virtual network functions that realize the use case in practice. Their experimental results indicate that a fully virtualized smart manufacturing use case is not only feasible, but also reduces machine interconnection and configuration time and thus improves productivity by orders of magnitude. Then, they show a real-world smart manufacturing application entirely implemented using NFV concepts, and a lightweight prototyping framework that simplifies the realization of vertical NFV proof-of-concepts, they demonstrate how NFV services can simplify machine data collection, aggregation, and analysis.  
**Network slicing**, introduced in Section 3.3, can be of significant help for industrial applications. The authors of [38] present a platformized the 5G system aiming at developing reconfigurable IndustrialInternet environments for smart factories in order to support dynamic production processes, while optimizing incurred cost and enabling a high level of remote monitoring and control. It proposes a framework based on network slicing for Industry 4.0. The proposed framework is validated in three different realistic use cases, which clearly point out the advantages of the envisioned solution: Remote Industrial Production Monitoring, Remote Equipment Maintenance, and Dynamic Industrial Manufacturing.  
Network slicing can also be used in autonomous driving. As detailed in [39], SDN, NFV, and network slicing can enhance the QoS of autonomous driving applications. It presents a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that the framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.  
**Multi-access Edge Computing (MEC)**, as described in Section 5, formerly known as Mobile Edge Cloud, allows virtualization of services in factory production achieving higher service reliability and smaller cost of industrial equipment. The MEC approach allows efficient resource planning through a dynamic extension or reduction of allocated resources [40]; and this approach can improve the reliability through dynamic management of the allocated computational resources between different cloud services. The paper shows the concept of MEC for two industrial use cases. The use case \"remote control of the robot arm\" can be acquired to perform the work in dangerous environments. The use case \"cooperation of two robot arms\" extrapolates to the cooperation between multiple robot arms on mutual work on the production line. The use-cases were realized in a real-world demonstration.