# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 5 5G and the Edge Cloud
### eXtended Reality (XR) and Industry 4.0 Applications  
Extended reality (XR) is one of the most emerging use cases nowadays. Virtual reality (VR) replicates a real-world environment or creates an imaginary world to involve users in an entirely virtual world. Augmented reality (AR) techniques fuse digital content with physical environment in real-time. Various fields can use these technologies, such as health care, entertainment, education, industry, etc. The future production systems, envisioned by Industry 4.0, has stringent requirements. They must be flexible, scalable, and easily reconfigurable systems supporting product-independency, universal manufacturing processes and capacity scaling, depending on the actual demand, is also a key enabler feature.  
The authors of [52] present Navantia's (one of the 10 largest shipbuilder company in the world) industrial AR (IAR) architecture, which relies on cloudlets [53] and on the fog computing paradigm. Note that edge computing is really close to fog computing and, in fact, some authors consider fog computing as one of the possible implementations of edge computing. In the article the proposed architecture is validated with real-world Microsoft HoloLens applications. The authors show that fog gateways are the fastest when transferring small payloads in terms of response delay, but a cloudlet is faster for medium and large IAR payloads.  
In [54], the components of general AR applications, the fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems are reviewed. The authors propose a three-layered hierarchical architecture, based on the MEC reference architecture. During the simulations, they considered a scenario, where multiple mobile devices, near the same base station, execute AR applications simultaneously. The simulation results show how they can significantly improve the latency and energy performance when compared against existing baseline schemes.  
The user equipment has limited resource capability, therefore it is difficult to meet the stringent service requirements, such as latency and reliability. One solution can be a scenario, when some part of the AR task is offloaded to the edge of the network. In [55], a scenario is considered, where multiple edge nodes cooperate to complete the AR tasks. The dependency between the components of each task is modeled with a directed acyclic graph through code partitioning. The objective of the proposed algorithm is to minimize the service failure probability (SFP) of the MEC-enabled AR service with the consideration of reliability and latency. The proposed heuristic algorithm in the article can significantly improve the probability to fulfill the targeted SFP in various network conditions.  
In [56], an intelligent resource scheduling strategy is proposed with an underlying hybrid computing framework. The computing system consists four layers for supporting artificial intelligent tasks: device computing layer, edge computing layer, cloud computing layer, and SDN layer. Then a two-phase algorithm is demonstrated for scheduling edge layer resources with the consideration of latency requirements. For the evaluation, the authors considered the following experiment; a mobile robot with an industrial camera moved along with a fixed trajectory to monitor the operation of equipment of the constructed Industry 4.0 platform. This robot periodically captured pictures and then calculated the images boundary for the next work process. Then, the running state of the equipment is judged by a neural network. Finally, the authors concluded that their resources scheduling technique can accelerate the implementation of Industry 4.0 and smart factory.