## 5 Simulation Setup and Experiments
### Baseline Graph-Based Resource Allocation  
The optimization of resources in high-mobile vehicles is a significant issue. We introduced a baseline low-complexity algorithm. To achieve this milestone, we first assigned an arbitrary V2V link to all clusters specified in Figure 2 and then simulated our baseline graph-based resource allocation approach using MATLAB. Algorithm 1 presents our proposed resource optimization and allocation method.  
The baseline graph-based resource allocation algorithm aims to optimize resource allocation in high-mobile vehicle environments, specifically for vehicular-to-vehicular (V2V) communication. The algorithm utilizes a graph-based approach to effectively distribute resources among clusters, ensuring efficient utilization and improved network performance. The algorithm follows the following steps:  
Figure 5: (**a**) Delivery Ratio and Cluster Range Comparison, (**b**) Authentication Delay and Cluster Range Comparison, (**c**) Keying Overhead and Cluster Range Comparison, (**d**) Detection Accuracy and Cluster Range Comparison.  
* Initialization: Assign an arbitrary V2V link to each cluster based on predefined criteria or randomly.
* Graph Construction: Create a graph representation where each cluster is represented as a node, and the V2V links between clusters are represented as edges. This graph captures the connectivity and relationships between clusters.
* Resource Evaluation: Evaluate the resources available within each cluster and the requirements of the V2V communication links. This assessment may include available bandwidth, signal strength, channel conditions, and quality-of-service (QoS) metrics.
* Resource Allocation: Utilize graph-based algorithms to allocate resources based on connectivity and performance requirements. Various graph algorithms, such as minimum spanning tree, shortest path, or maximum flow algorithms, can be employed to optimize resource allocation.
* Optimization: Continuously evaluate and optimize the resource allocation based on dynamic changes in the network, such as variations in vehicle density, traffic conditions, or communication demands. This ensures adaptability and efficient resource utilization in real-time scenarios.  
The baseline graph-based resource allocation algorithm serves as a starting point and provides a low-complexity solution for resource optimization in high-mobile vehicle environments. It aims to balance resource utilization, network performance, and scalability. By leveraging connectivity information and graph-based techniques, the algorithm optimizes the allocation of resources, enhancing the overall performance and efficiency of V2V communication in vehicular networks.  
Figure 6 shows the Cumulative distribution function (CDF) of the quick Signal-to-Interference-Plus-Noise Ratio (SINR) of V2V and V2I communications using our proposed graph-based resource allocation algorithm.  
In Figure 7, we have merely discussed the Sum of V2I capacity with varying vehicle speeds by adopting the algorithm provided by [66] in our algorithm. V2V transmit power in dBm is taken as 33. Large-scale fading parameters are V2V = 3 and V2I = 8, which are shadowing standard deviations. Cell parameter setup is done so that the carrier frequency is 3.5 GHz, 200 cell radius in meters, base station height 25 m, 35 m BS-highway distance in meters, and base station noise is 5 dB. The vehicle antenna height is 1.5 m, with the vehicle antenna gain of 3 dBi and the vehicle noise being 9 dB. The number of lanes is reduced from six to four, and the lane width is reduced from 4 to 3 m. The velocity range is from 20 to 160. According to TR 36.885, the average inter-vehicle distance is 2.5 *v/3.6. The maximum number of links for V2I is reduced from 10 to 5 [32].