## 4 Proposed mmWave Assisted CR-IoV
### Comparative Analysis
#### 5.3.1 Analysis of Throughput  
Throughput is an important metric that measures the overall network performance. For better analysis, we have compared throughput with respect to the number of vehicles and the number of attackers. In Figure 8, throughput is compared with respect to the number of vehicles in the network and Figure 9 compares throughput with respect to the number of attackers. In both aspects, the proposed mmWave-assisted CR-IoV shows better performance, up to 98 Mbps. In all works, a gradual increase is addressed with an increase in the number of vehicles and vice versa with the number of attackers. The main reason is that the presence of a large number of vehicles assures the possibility of selecting optimal forwarders. On the other hand, the presence of attackers increases data loss and interrupts data transmission, which leads to a decrease in throughput. In particular, existing works can only achieve throughput up to 80 Mbps, which is much lower than the proposed work. In SVM and ML-NN-based methods, there is no particular procedure for routing, i.e., data loss is high due to data transmission through non-optimal vehicles. In 2D-beamforming, the produced beams are non-optimal, which leads to degradation in throughput. Specifically, OLSR, which is the conventional routing protocol only has a throughput of 70 Mbps since the OLSR selects routes only based on a distance measure. Due to these reasons, existing works are not able to achieve better throughput. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Existing Work** & **Network Model** & **Security** & **Beamforming** & **Routing** & **Demerits** \\ \hline SVM [46] & Cognitive & ✓ & \(\times\) & \(\times\) & \(\bullet\) Conventional kernel function decreases accuracy \(\bullet\) Lack of attack specific feature degrades the performance \(\bullet\) Complexity The complexity of the approach is high \(\bullet\) Not suitable for detecting various types of CR attacks \(\bullet\) Forms inaccurate beams \(\bullet\) Lack of position information decreases beampattern efficiency \(\bullet\) Route selection needs a large amount of hello packets \(\bullet\) Increases packet overhead \(\bullet\) Complexity is high since all vehicles are considered as solution \(\bullet\) An increase in route selection time results in higher transmission delay & \(\bullet\) Conventional kernel function decreases accuracy \(\bullet\) Lack of attack specific feature degrades the performance \(\bullet\) Complexity The complexity of the approach is high \(\bullet\) Not suitable for detecting various types of CR attacks \(\bullet\) Forms inaccurate beams \(\bullet\) Lack of position information decreases beampattern efficiency \(\bullet\) Route selection needs a large amount of hello packets \(\bullet\) Increases packet overhead \(\bullet\) Selects non-optimal route \(\bullet\) Complexity is high since all vehicles are considered as solution \(\bullet\) An increase in route selection time results in higher transmission delay & \(\bullet\) Conventional kernel function decreases accuracy \(\bullet\) Lack of attack specific feature degrades the performance \(\bullet\) Complexity The complexity of the approach is high \(\bullet\) Not suitable for detecting various types of CR attacks \(\bullet\) Forms inaccurate beams \(\bullet\) Lack of position information decreases beampattern efficiency \(\bullet\) Route selection needs a large amount of hello packets \(\bullet\) Receases packet overhead \(\bullet\) Selects non-optimal route \(\bullet\) Complexity is high since all vehicles are considered as solution \  
In the presence of five attackers, we have achieved throughput up to 94%, which is relatively higher than previous works. For the same number of attackers, SVM has 60 Mbps of throughput, and the ML-NN method has 54 Mbps of throughput, which is 34 Mbps and 40 Mbps lower than the proposed work. The analysis shows that the existing SVM and ML-NN methods are unable to handle a large number of attackers in the network.