## 3 Problem Statement  
Integration of mmWave, CRN and IoV is expected to be a fine solution to meet the growing requirements of future IoT applications. Consider an IoV network with \(n\) number of intelligent vehicles as \(V_{1},V_{2},..,V_{n}\). The main research problem in mmWave-assisted CR-IoV is the lack of focus on important aspects as follows,  
* **Security**[46; 47]**: In the \(n\) number of vehicles, there may be malicious or illegitimate vehicles presented. In the presence of malicious vehicles, data loss will be high. While attempting attack detection, the absence of an effective mechanism for a particular attack, such as an SSDF or PUE attack, decreases the detection accuracy. In addition, improper validation of SUs allows unauthorized SUs to access the spectrum.
* **Beamforming**[48]**: In mmWave-based communications, beamforming is performed based on the 2D position information. Inaccurate position information produces non-optimal beams, which affects data transmission.
* **Routing**[49; 50]**: In CR-IoV, routing metrics are more important for increasing the packet delivery rate. Furthermore, the computation of the optimal route without considering the transmission direction increases time consumption and complexity. Time increase in route selection results in higher transmission delay.  
The overall problem statement is depicted in Figure 1. As illustrated in the figure, these three problems affect the overall performance of the CR-IoV system, which is infeasible for achieving better transmission results in real-time.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Process** & **Existing Work** & **Demerits** \\ \hline SSDF Detection & Frequency Ordering Method [28] & \(\bullet\) The involvement of HMM demands large unstructured parameters which are ineffective \\  & & \(\bullet\) Increases false SSDF detection \\  & Collaborative Approach [29] & \(\bullet\) Sensing report collection is insecure \\  & AADS [30] & \(\bullet\) Unauthorized SU access increases vulnerability \\  & AADS [30] & \(\bullet\) Lower detection accuracy \\  & Channel variance localization [31] & \(\bullet\) Sensing report collection is insecure \\ PUE Detection & Channel variance localization [31] & \(\bullet\) SU validation is inefficient since the location information alone \\  & PUE Detection & \(\bullet\) High possibility for sensing reports to be falsified \\  & CSS based PUE detection [32] & \(\bullet\) Lack of localization approach increases the complexity \\  & Spectrum access based PUE detection [33] & \(\bullet\) Static rule-based method decreases the detection accuracy \\  & Chaotic tag-based detection [34] & \(\bullet\) BER is high when SNR decreases \\  & Two stage algorithm [35] & \(\bullet\) Inaccurate PUE detection \\  & Precoding method [36] & \(\bullet\) Position estimation in 2D is inaccurate \\ Beamforming & AN based beamforming [37] & \(\bullet\) Coverage of MIMO is limited due to the absence of 3D \\  & Hybrid beamforming [39] & \(\bullet\) Security is poor \\  & Angular based beamforming [40] & \(\bullet\) The coverage of beamforming is limited to a 120-degree \\  & MUBT scheme [41] & \(\bullet\) The efficiency of beamforming is low due to the presence of obstacles \\  & MUBT scheme [41] & \(\bullet\) Not suitable for large-scale users \\  & BPA based routing [42] & \(\bullet\) Limits the coverage of the MIMO system \\ Routing & ACO routing [43] & \(\bullet\) Needs a common idle channel all the time \\  & Trusted routing [44] & \(\bullet\) Increases transmission delay \\  & Connectivity-aware routing [45] & \(\bullet\) ACO increases the convergence time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis on existing works.