# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
The performance of data collection methods was evaluated by varying the total number of nodes, and the results are shown in Figure 9.  
It is very clear from Figure 9a that for both the proposed method in the hybrid architecture and for the data collection in the centralized architecture, the average cost per node per data-gathering cycle increases with the increment of the network size. The reason for the increment of average communication cost per node per  
compared to data collection in a centralized architecture. For low network sizes, the cost reduction of the proposed method compared to the centralized architecture is lower, as evidenced by the lower difference between the cost average values, and the overlapping upper 95% confidence interval limit for cost in the proposed method with lower 95% confidence interval limit for the centralized architecture. However, with large networks, the proposed method significantly reduces the average cost per node per data-gathering cycle, as evidenced by the non-overlapping 95% confidence intervals of the cost for the proposed method and centralized architecture with a large difference between average values, as shown in Figure 9a. Thus, the proposed method will be very effective in reducing communication costs in large networks for data collection. Now let us consider the channel utilization variation with respect to the total number of nodes in the network. As evident from Figure 9b, the channel utilization of all LTE, DSRC, and Ethernet channels for both the proposed method and data collection for centralized architecture increase with the network size. However, for the distributed architecture, the LTE and Ethernet communication channel utilization is always zero, irrespective of the network size. The DSRC channel utilization for all three architectures overlaps for all network sizes proving the equivalence of the data collection methods in DSRC communication channels. However, the utilization of LTE and Ethernet channels do not overlap  
Figure 9: Performance evaluation of the data collection methods under different vehicular network sizes. (**a**) Communication cost per node per data-gathering cycle variation under different network sizes (**b**) Average channel utilization per data-gathering cycle variation for a different number of nodes. (**c**) End-to-end latency per node per data-gathering cycle variation for different network sizes. (**d**) Packet delivery ratio per data-gathering cycle variation for different network sizes. for centralized and hybrid architectures. The rate of increment of LTE utilization with network size is lower, while the rate of increment of Ethernet utilization with network size is higher for the proposed data collection in hybrid SDVN architecture compared to data collection in centralized SDVN architecture. For small network sizes (less than or equal to 32), the utilization of LTE and Ethernet channels for the proposed method and centralized architecture is almost the same, as evident from Figure 9b. However, as the network gets bigger, the channel utilization gap between the proposed method and centralized architecture gets wider for both Ethernet and LTE communication channels. As the network gets bigger, the average LTE utilization of the proposed method varies less than that of the centralized architecture, and the average Ethernet utilization of the proposed method varies larger than that of the centralized architecture. The result on latency given in Figure 9c clearly shows how the proposed method is effective in large networks to reduce the latency per node per data-gathering cycle. For small-size networks (size less than or equal to eight), the average latency and the 95% confidence intervals for the latency for both the proposed method and centralized architecture overlap, indicating that for very small networks, the latency of both methods is almost the same.