## 6 Evaluation
### Performance Evaluation  
#### 6.2.1 RSU Clustering Performance Evaluation  
To verify the effectiveness of the proposed RAPSCA algorithm, this paper evaluates the vehicle access cost and the algorithm's impact on the overall block allocation running time under different numbers of RSU nodes, varying blockchain lengths, and different numbers of clusters.  
* Impact of the number  
block data from more distant nodes, increasing access latency. Additionally, as the number of blocks grows, the range of data involved in vehicle queries for historical blocks expands, further elevating access costs. At the same time, the block access cost of RAPSCA is significantly lower than that of the random clustering strategy and comparable to the single-cluster scheme. When the block count is high, the access cost of the single-cluster scheme is slightly lower than RAPSCA. This is because, with fewer block copies available, the number of locations where a single RSU in the RAPSCA scheme can directly access a required block is lower than in the single-cluster mode, resulting in additional data transmission overhead. As shown in Figure 3(a,b), as the block count increases, RAPSCA maintains relatively low access costs while also improving the overall efficiency of the algorithm. Therefore, this algorithm is well-suited for RSU devices with limited computational and storage resources, demonstrating strong adaptability in large-scale block storage scenarios.
* Impact of the number of clusters on the RSU Clustering Process: As shown in Figure 4(a,b), the system is configured with 120 RSUs, 1200 vehicles, and 200 blocks, while the number of clusters varies from 3 to 7. As shown in Figure 4(a), the RAPSCA algorithm achieves access costs comparable to the single-cluster scheme, and its access cost remains stable as the number of clusters changes. This indicates that the RAPSCA clustering process does not introduce additional access overhead. When sufficient storage resources are available in the IoV system, a well-designed clustering strategy ensures that the total access cost remains at an optimal level, without imposing extra burdens due to cluster partitioning. However, since strong trust relationships among RSUs are only guaranteed within individual clusters, improper cluster partitioning may disrupt critical communication paths, making it difficult for some RSUs to directly access required blocks. This would increase access latency and significantly raise the overall system access cost. Therefore, a well-structured cluster partitioning strategy not only optimizes storage resource utilization efficiency but also reduces system access overhead, enhancing the overall performance of the SIoV system. As shown in Figure 4(b), as the number of clusters increases, the number of RSUs per cluster decreases, leading to a reduction in the runtime of the block allocation algorithm. Although increasing the number of clusters improves overall algorithm efficiency, it also reduces the total storage resources within each cluster. Therefore, in practical applications, parameters such as \(\eta\) and the number of clusters should be carefully chosen to ensure that clusters are reasonably partitioned while maintaining sufficient storage resources within each cluster.  
Figure 4: Results of clustering algorithms with the number of blocks.