## 4 Proposed mmWave Assisted CR-IoV
### Simulation Setup  
The proposed work combines mmWave technology with CR-IoV, i.e., the considered simulation tool needs to support all these technologies. Thus, we have selected a network simulator-3.26 (ns-3.26) tool for performing simulations. Ns-3 has a wide range of modules to support various network technologies and algorithms. We have installed the ns-3.26 tool on the PC, which has Ubuntu-14.04 LTS (32-bit) as the Operating System (OS). All algorithms are written in C++ language and imported with Python language. The other simulation parameters considered are listed in Table 3.  
After setting up all the simulation parameters, we start the simulation. Experiments are performed with varying numbers of vehicles and the number of attackers in the network.  
**Application Scenario (Self Driving Car)**--The proposed mmWave-assisted CR-IoV is applicable for all IoV and Intelligent Transportation System (ITS) based applications. Here, we have demonstrated one important application of self-driving cars. In self-driving car applications, there will be a huge need for a spectrum for continuous data transmission and reception. The conventional DSRC is insufficient to meet the huge requirements of self-driving cars. Thus, the proposed mmWave-assisted CR-IoV model is most suitable for self-driving car applications. The self-driving cars connected scenario is shown in Figure 7. As per the scenario, each car demands any service (can be infotainment or entertainment) or information from any nearby vehicles. Mainly, multimedia file sharing, parking slot requests, traffic information dissemination, safety conditions sharing, etc., are shared among self-driving cars. All these data are delay sensitive and must reach the vehicles without a huge delay. For that, seamless connectivity among self-driving cars is required. For all these requirements, our proposed approach provides a better solution.  
First, the spectrum requirement is resolved by modeling the CR-IoV system. The cars are allowed to sense the environment and use the available spectrum in an optimum way. Next, the problem of delay is handled by producing optimal beams and selecting optimal multi-objective routes. Furthermore, security is also a major issue need to be addressed in self-driving applications. In particular, the use of the CR-IoV model attracts the attackers to use the available spectrum intentionally. As per our work, we have tackled this issue with SSDF and PUE attack detection and lightweight certificate validation procedure. Overall, the proposed work is apt for self-driving car applications that meet each requirement.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area & 1000 \(\times\) 1000 \\ Number of CBS & 1 \\ Number of SUs & 20 \\ Number of PUs & 10 \\ Number of PBS & 1 \\ Number of TA & 1 \\ Number of Attackers & 5 \\ Spectrum range & 5 MHz to 100 MHz \\ Number of channels & 20 \\ Channel bandwidth & 20 MHz \\ Data rate & 150 Mbps (Maximum) \\ Number of packets generated & 2048 \\ Packet size & 1024 \\ Packet Interval & 0.1 s \\ Simulation time & 100 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.