# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
(**a**) Packet delivery ratio variation for different optimization/data collection frequencies; (**b**) Communication cost per node per data-gathering cycle variation for different optimization/data collection frequencies; (**c**) Average channel utilization per data-gathering cycle variation for different optimization/data collection frequencies; (**d**) End to end latency per node per data-gathering cycle variation for different optimization/data collection frequencies. the proposed method tends to select more Ethernet channels as agents, which have lower communication costs, and tends to reduce cellular communication, which has a higher cost. Another important fact about channel utilization is that the channel utilization gap between the centralized and hybrid architectures for LTE and Ethernet communication channels increases with the increment of the nominal optimization frequency, as evident from Figure 8c. Thus, in terms of communication cost and channel utilization, 0.02 is the best frequency for the proposed method for data collection. On the other hand, the LTE and Ethernet channel utilization for the distributed SDVN are always zero for any frequency. Now, let us consider the effect of optimization/data collection frequency for end-to-end latency. As evident from Figure 8d, the latency per node per data-gathering cycle for the data collection in a centralized architecture gradually decreases with the increment of the data collection frequency. On the other hand, for the data collection using the proposed method, the latency tends to decrease for nominal optimization frequencies from 0.02 to 0.25 and then begins to slowly increase with the increment of the frequency. Furthermore, for all frequencies, the proposed method's average latency values are much lower than those of the centralized architecture, and the proposed method's latency 95% confidence interval upper limit lies well below the 95% confidence interval lower limit latency of the centralized architecture, thus we can confidently say that the proposed method's latency is lower than the centralized architecture for any frequency. However, the latency gap between the proposed method and the data collection for the centralized architecture decreases with the increment of the frequency. By contrast, for the distributed SDVN architecture, the average latency is very low (0.71 ms) and the value does not vary with frequency. In order to decide on an optimum value for the nominal optimization frequency, we should examine its effect on all communication costs, channel utilization, latency, and reliability. As analyzed, only frequencies greater than or equal to 4 Hz achieve the PDR benchmark (99.9%) for vehicular communication. As all communication costs, channel utilization, and end-to-end latency increase with increment of frequency for frequencies greater than or equal to 4 Hz for the proposed method, as evident from Figure 8b-d, we can conclude that 4 Hz is the optimum frequency which achieves a PDR of 99.9% and at the same time has a minimum cost, channel utilization, and latency. 3.3 Effect of Total Number of Nodes for Average Communication Cost, Average Channel Utilization, Average End-to-End Latency, and Packet Delivery Ratio  
In this experiment, we set both \(f\) and \(f^{\prime}\) to 4 Hz as it was found in Section 4.3.2 that 4 Hz is the minimum optimization frequency to achieve a target packet delivery ratio of 99.9%. We simulate an urban mobility scenario having a 60 kph maximum vehicle speed. RSU to vehicular node ratio is maintained at 1:3, and the optimization threshold for proposed data collection in hybrid SDVN architecture is set as 0.005, based on the result obtained in Section 4.3.1. The variable for each architecture's data collection is the total number of nodes in the vehicular network, which varies from 4 to 256.