# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 4 Traffic Measurement with SDN
### Generic Measurements of the Framework  
Although F5 TS consists of SDN and other modules, finding traffic that passes through the switches was considered. Here, SDN plays a major role in improving the transportation system's data traffic monitoring and measurement. Generally, network traffic depends on User Datagram Protocol (UDP) traffic and Transmission Control Protocol (TCP) traffic. Within the SDN layers, the number of traffic measurements needs to be considered for the configuration of network traffic management. In the control layer, the processing time of the End-to-End (E2E) delay is the duration of the total delay between the starting and endpoints. Measuring E2E delays influence the traffic flows of TCP and UDP, which are two different types of traffic measured carefully for managing traffic. In this measurement, the functions of the SDN-based MA support the optimization of the processing time of E2E delay. In the data layer of SDN, the transmission time and waiting time dominate in the E2E delay calculation that measures the characteristics of the traffic. To improve the measurement, optimizing the transmission and waiting time depended on the design of the algorithm and protocol should be considered in the F5 TS. The measurements used in an intelligent approach and security solutions allow transport operators to enhance QoS. In the future, novel QoS prediction techniques will be useful in driverless and drone transport use. Applying this technique to F5 TS, users will have efficient and secure services through highly reliable communication links with appropriate QoS characteristics as demanded by ITS services. In this measurement, AI can also play a meaningful role in determining when to perform a certain autonomous task in the vehicle in an efficient manner. The QoS prediction can also be used to predict the most economical time for a software update as a proactive service. Interestingly, in many cases, including certain C-ITS and telematics applications, these performance fluctuations are not a problem if they can be predicted in advance. The real-time QoS predictions enhance the proactive security solution for protecting the services intelligently. This would allow service providers, mobile network users, and automotive applications to dynamically adapt their behaviors to the prevailing or imminent QoS level in the automotive industry. This paragraph presents the mathematical formulation of finding intelligent value to clarify the conditions in which it works well. Since data traffic has many characteristics, we present the mathematical formulation for selected or stationary traffic. This formulation is also applicable to real-time or nonstationary users (\(\mathbf{P_{u}}\) or \(\mathbf{L_{u}}\) or \(\mathbf{E_{u}}\) or \(\mathbf{S_{u}}\)) traffic. In the framework, all delivered data of the user packets in the network are compared with predicted data of the packets. Thus, we consider an error approximation of user traffic; that is, user packets are approximated as an error. Suppose that a traffic path in the network is shared by \(\boldsymbol{K}\) (\(>1\)) users. Let \(\boldsymbol{D_{k}}(\boldsymbol{t}),\ \boldsymbol{t}=1,\ 2,\ \ldots\ \boldsymbol{K}\), denote the cumulative amount of data transmitted by user \(k\) observed during the selected time. In this mathematical formulation, \(\boldsymbol{D_{k}}(\boldsymbol{t})\), can be considered as delivered or predicted data. In the mathematical formulation, the calculation of QoS allows us to improve the service, including ITS and other intelligent features of transportation services depended on the accuracy means minimum or no error (\(e=0\)) calculated from (1) [18]. Intelligent value is inversely proportional to error as in (1) because when errors increase during the calculations of QoS in any of the data of ITS, intelligence decreases over the period (\(\Delta\boldsymbol{t}\)). Here, all delivered and predicted data are time-dependent.