# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 7 Conclusions  
In this work, a 5G-enabled architecture is presented based on Smart Networks for Industry (SN4I), an experimental facility for Industry 4.0 based on NFV and SDN that spans between the Aeronautics Advanced Manufacturing Center (CFAA) and the Faculty of Engineering in Bilbao (EIB) at the University of the Basque Country. To complete the proposed architecture, the integration of a WSN is shown, in order to enable the deployment of IIoT applications with the implementation of appropriate security mechanisms. The substrate provided by the proposed IT infrastructure, in which isolated slices composed of machine tools, sensors, and interconnected Virtual Network Functions can be flexibly deployed and configured, has enabled the development of the use case presented in this work, consisting in a 5G-enabled smart broaching process. In this use case, the monitoring of the broaching process is enabled through ML VNFs and through the measurement of environmental parameters from the WSN. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Benefit** & **Overall Impact** & **Impact on Use Case** \\ \hline More agile and automatized deployment cycle of services. & Reduction in deployment time and costs. Faster transition between local deployment to in-plant deployment. & Faster deployment and production start-up of data recollection and ML services. \\ \hline More flexible lifecycle management of services. & Easier upgrade, adaptation, and configuration of services, and service downtime reduction. & Ability to automate specific service tasks. Ability to quickly scale resources on demand and integrate new service components if required by the process. \\ \hline Service isolation at data and performance level. & Data leak avoidance between business processes and avoidance of performance issues due to resource contention. & Guarantees offered in the isolation of the broaching process traffic as well as in the performance and bandwidth of the ML services. \\ \hline Intelligent service placement, efficient resource allocation, and highly granular network traffic classification. & Lower costs and better performance due to the optimization of computing and networking resources and intelligent matching of hardware capabilities. & Computing resources efficiently assignable based on ML processing requirements. Bandwidth adaptable for different traffic flows of the broaching tool and between the service VMs. \\ \hline Service catalog availability. & Faster service delivery and faster introduction of new services. Ability to export and import designed services. & Ability to export services or components designed for this process to other processes for immediate deployment. Ability to quickly import and deploy already designed external services to this process. \\ \hline Interoperability between NFV solutions. & Ability to integrate with other data centers or other NFV technologies, including public clouds and containerized environments. & Ability to introduce additional hardware capabilities if beneficial to the process. \\ \hline Dynamically assignable and shareable sensors and sensor services. & Lower costs due to usage optimization and fast service provisioning due to dynamicity. & Obtainable environmental data from sensors dynamically adaptable to varying process requirements. \\ \hline Strong but lightweight and expressive support of security policies. & Customizable and fine-grained security policies and high reconfigurability with a minimal performance impact. & Usage of sensor services customizable to process requirements. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of benefits. Furthermore, to guarantee secure access and use of the information that these sensors gather, the WSN has been protected with a strong and expressive access control mechanism named Hidra.