# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 5 Discussion
As the proposed data collection for hybrid architecture optimizes data collection by minimizing total communication cost and delay, as proven by the results, the communication cost and latency of the proposed method are confidently much lower than those of the data collection in the centralized architecture except for smaller networks (network sizes less than or equal to 16), as evident from Figure 9. The reason for the smaller performance gap between the two data collection methods in a smaller network is due to the tendency for nodes in the network to be located far apart from each other such that the normalized heterogeneous link-state entropy value of the network is close to zero. In such a case, all isolated nodes will be appointed as agents by the optimizer, and such a case is similar to the data collection in a centralized architecture. In other words, there is no difference in data collection by isolated nodes between the data collection for the hybrid and centralized architectures. Let us now analyze the significance of the performance gap between the proposed method and the data collection in centralized SDVN architecture statistically. Let us consider the two null hypotheses listed below. **H1. **_The communication cost of the proposed method is higher than that of the centralized architecture;_
**H2. **_The latency of the proposed method is higher than that of the centralized architecture;_  
We consider a significance level of 0.05 in order to test the above-given hypotheses. If the probability \(p\) (how likely it is that data could have occurred under the null hypothesis) value is less than 0.05, the null hypothesis can be rejected, and the alternative hypothesis will be significant. As in all other tests (entropy test in Section 4.3.1, frequency test in Section 4.3.2, and mobility test in Section 4.3.4 except rural mobility scenario), it was proven that the communication cost and latency of the proposed method were confidently lower (as 95% upper CI of cost or latency of the proposed method lies below the 95% lower CI of the cost or latency of the data collection in centralized architecture) than that of the centralized architecture, we can conclude that both null Hypotheses H1 and H2 have a \(p\)-value of 0.00 such that alternative hypotheses are true and significant. However, we should examine the \(p\)-value for different network sizes, as evident from Section 4.3.3, and for different speed values for rural mobility scenarios, as evident from Section 4.3.4, where the performance gap between the data collection using the proposed method and that of the centralized architecture had been narrow, and 95% of the CIs overlap for some data points. Let us denote the \(p\)-value for Hypothesis H1 as \(P_{\text{HI}}\) and the \(p\)-value for Hypothesis H2 as \(P_{\text{H2}}\). We confirm that \(P_{\text{HI}}=P_{\text{H2}}=0\) for all speeds in the rural mobility scenarios, too. Therefore, alternative hypotheses are significant for all experimental results in Sections 4.3.1, 4.3.2 and 4.3.4. Now, let us compute \(p\) values for the hypotheses for different network sizes, which are given in Table 3.  
Thus, as evident from Table 3, the alternative hypothesis of H1, which is the cost of the proposed method is lower than that of the centralized architecture, is statistically significant for all network sizes, since the \(P_{\text{H1}}\) value is less than 0.05. However, the alternative hypothesis of H2, which is the latency of the proposed method is lower than that of the centralized architecture, is significant, since \(P_{\text{H2}}\) is less than 0.05, only for network sizes greater than or equal to 32. The proposed method reduces the cost by using several strategies. The first one is that it reduces transmission overhead by collecting data using agents and transmitting the collected data by encapsulating it with a single header. Thus, when selecting agents, the proposed method selects agents that have more neighbors.