# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 4 5G Functions and Features to Support the Information Flow within Industry 4.0
### Cloud-Based Big Data Analysis
Besides the improvement related to the direct transmission speed and latency, there are numerous 5G features that can enhance performance and overall quality of service. In order to perform energy-efficient collaborative tasks, a D2D architecture is suggested for a large number of mobile equipment in [110], while [111] implements a predictive and constructive caching approach to decrease peak traffic demands. An application-aware traffic redirection mechanism for MEC is proposed in [99] to minimize response time and use of bandwidth, meanwhile, the nearest edge server autonomously develops MEC services to provide mobile equipment with seamless QoS for video streaming [98]. Paper [97] provides an energy-aware discharge architecture, whereby each mobile equipment determines whether to perform or discharge computational tasks to the Multi-Access Edge Computing (MEC) server to reduce energy consumption. High level features, such as SDN, is used to dynamically manage a community of neighboring vehicular nodes in order to improve control over networks and their resources in vehicular networks [102]. In [103], in order to achieve the delay requirement of 5G, a non \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**5G Functionality or Feature** & **Hybrid Computing** & **Edge Computing** & **Fog Computing** & **Mobile-Edge Computing** \\ \hline Decision making & - & [97; 98; 99; 100; 101; 102; 103; 104; 105] & - & [97; 98; 101; 102; 103; 104; 105] \\ \hline Data analysis & [104; 106] & - & [106] & - \\ \hline Local computation & [104; 105; 106; 107; 108; 109; 110; 111; 104; 106; 107] & [97; 98; 100; 103; 108; 109; 101; 106; 108; 109] & [97; 98; 100; 103; 105; 106; 101; 110; 111] \\ \hline Local storage & [106] & [98; 109; 111] & [106; 109] & [98; 106; 111] \\ \hline Energy efficiency & - & [97; 101; 108; 110] & [108] & [97; 101; 111; 110] \\ \hline Quality of Service & [104; 105; 106; 107; 108; 109; 101; 106; 109] & [106; 107; 109; 107; 109] & [98; 100; 103; 105; 106] \\ \hline Network function virtualization & - & [100; 103; 109] & [109] & [100, 103] \\ \hline Software-defined networking & [107] & [102; 103; 109] & [107; 109] & [102; 103] \\ \hline Low latency & [104; 105; 106; 107; 108; 109; 107] & [97; 98; 100; 103; 108; 109; 101; 106; 107; 108; 109] & [97; 98; 100; 103; 105; 106; 110; 111] \\ \hline Resource management & [105; 107] & [101; 102; 109; 110] & [107; 109] & [101; 102; 105; 110] \\ \hline Data management & [105; 106] & [97; 99; 100; 102; 111] & [106] & [97; 99; 100; 102; 106; 111] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence 5G technology in cloud-based big data analysis. standalone (i.e., disconnected from the Internet) MEC-based architecture for mission-critical public safety services is presented. In [106], a mobile edge and fog computing architecture based on D2D is implemented to allow collaborative computing that performs tasks on more than a single platform or paradigm to enhance MEC. A real-time, context-aware, service-composition, and collaborative architecture is proposed in [104,105] to provide a fast composite service that consolidates several services enabled by the collaboration of various hardware and software with different capabilities.