# A 5G V2X Ecosystem Providing Internet of Vehicles +
## 5 Materials and Methods
### The 5G Ecosystem Simulation Methodology  
In order to evaluate the 5G ecosystem as the IoV infrastructure support, we have conducted the evaluation of a set of data transfer tests in enhanced Mobile Broadband (eMBB) scenarios with Internet-based video services [57]. The 5G mmWave communications were used in the simulations. Simulations were conducted through ns-3 simulator. Scenarios were modeled and codes were developed in ns-3. Some modules were modified in order to support the integration of the proposed ecosystem. V2X scenarios were simulated with different vehicle densities. The simulations were executed in a high-performance computing environment provided through cluster F37 of the CEFET-MG, a brazilian public education institution. The cluster is open to students and professors of the institution who wish to develop research and teaching projects. The F37 cluster has 32 machines of shared use, in 3 different configurations which are grouped into the small, medium and large rows. The simulations were executed in the large row (4 Supermicro machines, 64 physical threads without hyperthread, and 128 GB RAM) under QoS requirements called part2d and part10d, which have a maximum time of 2 and 10 days, respectively. This is one of the first works that compares MATLAB's results [1; 16] with evaluations of VANETs integrated to 5G. These simulations allow executing all the protocols and modeling real scenarios with an intense mobility of the expected 5G networks. Generally, simulations performed through the MATLAB present a mathematical efficiency. In contrast, they are not able to capture the complexity of the network on a much higher level of details, because it presents limitations on the network important aspects, for example, the mobility deal. For mmWave communications, the mmWave ns-3 module was used [58; 59]. On LENA module, it divides the available resources between active flows. On mmWave module, the resources separation is based on time division multiple access with the allocation of time domain symbols inside a periodic sub-frame for different users in Downlink (DL) or Uplink (UL) directions. The flow rate calculation on bits/s (bits by second) for each vehicle is given by T (throughput), where S (M, B) represents the size of the transport block as defined by 3 GPP TS 36.213, M is the modulation and coding scheme, B is the transmission bandwidth configuration in number of resource blocks, and \(\tau\) represents the duration of the Transmission Time Interval (TTI), formulated by the Equation (1):  
\[T=\frac{S(M,B)}{8\tau} \tag{1}\]  
For video traffic, the Dynamic Adaptive Streaming over HTTP (DASH) was adopted. Three adaptive algorithms were tested: FESTIVE, PANDA, and TOBASCO2 [60]. We suppose that there is one video server, for each video required, across the network. To support the SDN controller, the OpenFlow 1.3 (OFSwitch13) module was adopted [61]. Based on the SDN concept, the OF protocol was simulated. The structure was composed by the controller and the network OF elements [62; 63]. With this module, it was possible to extend the controller application interface to implement the desired control logic to orchestrate the network, in our case, the delivery of multimedia services. The simulations were executed assuming a complete traffic model which means that the base stations are always transmitting and receiving multimedia data, with download of data through links and mmWave devices, CN based on SDN, and 5G scenarios. The parameters adopted to model each scenario considered the node numbers for each gNodeB (numgNB), gNodeB height (hgNB) in meters, User Equipment height (hUE) in meters, maximum and minimum distance (maxdist, mindist), eMBBactive vehicles (numVehicles), and vehicles speed (speed), as Table 3 shown. All users are active and communicate all the runtime. Three deployment scenarios based on Technical Recommendation (TR) 38.913 were simulated: rural macro, urban macro and urban micro [64]. First of all, the RMa (Rural Macro) scenario supporting high-speed vehicles.