## 6 Performance Evaluation
### Simulation Results  
In order to show the influence of the storage size, let the number of storage cells in a roadside unit (\(w(j)\), for \(\forall j\in U\)) range from 1 to 10. The simulation results of the four data acquisition models, i.e., Greedy, DAS, vDAS and DAS-RU, are illustrated in Figure 5. Since the contents are distributed according to the present network information in each storage cycle, the replication solutions in different cycles are independent. For a simple presentation of reliable results, the simulation results in all the cycles are averaged.  
From Figure 4(a), when the storage space is small, the access ratios of DAS and DAS-RU are almost the same, while that of vDAS is a little lower. The main reasons lie in two aspects: (1) in the Gale-Shapley algorithm, the preferences of left vertices (contents) play a more important role than the priorities of right vertices (storage cells); (2) since vDAS uses the encountering latency factor to compute the contents' preferences, the benefits of the response factor (indicating the delivery ratio) wears off. In addition, when there are sufficient storage spaces, the access ratio of DAS increases to approximately 95%, while that of Greedy drops. This is because the stable matching algorithm improves the resource utilization. In addition, as the storage size increases, the ratio difference between DAS and DAS-RU becomes bigger because of the severe bandwidth competition around roadside units, which results in an obvious advantage of the supplementary storage at vehicular nodes. Overall, compared with others, DAS has a high and stable access ratio.  
As Figure 4(b) illustrates, with an increasing number of storage cells, the average access delays of all the models keep stable after some drops. Since they all consider the delivery delay when distributing contents, enough storage helps to disseminate the contents to nearby roadside units and hence shorten the latency. While Greedy keeps the shortest latency, DAS has the longest one. Nevertheless, the delay of DAS is around 37% of the lifetime of contents (600 s), which is acceptable in most of the vehicular applications.  
We observe that, in Figure 4(c), all the methods have similar numbers of replicas, when each roadside unit has less than three storage cells. When the storage size rises, Greedy occupies more storage spaces than DAS and vDAS by nearly 10%. Since Greedy replicates contents from a local view, the storage spaces in the whole network are not efficiently utilized. Additionally, DAS has a few more replicas than DAS-RU because vehicles also carry content replicas for supplement storage, especially when the bandwidth competition at roadside units is severe. In one word, compared with Greedy and vDAS, DAS keeps a small storage consumption.  
In Figure 5d, the transmission overheads of DAS, vDAS and Greedy are similar, while DAS-RU has a small overhead due to no V2V communications. Comparing Figure 5c,d, we see that the number of transmissions is larger than the number of replicas. Besides the transmission failures, the main reason is that one replica at a roadside unit or a vehicular node may serve for multiple requests during different V2V or V2U contacts.  
In conclusion, when \(w(j)=10\), compared with Greedy, DAS has a higher access ratio and less storage cost by approximately 30% and 10%, respectively, with a longer access delay (the delay is around 37% of the content lifetime, which is acceptable in most vehicular services). In DAS, the supplementary storage at vehicular nodes increases the access ratio by about 6%. In addition, DAS has a high access ratio, while its variant vDAS has a short access delay. This implies that our data acquisition framework demonstrates different properties via adjusting the models of the preference and the priority. This flexibility may enlarge the application scope of our proposal.