# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
\(f\) and \(f^{\prime}\) which are synced together are the variables in this experiment for the proposed data collection in hybrid SDVN architecture, and \(f\) is the variable for the data collection in centralized and distributed SDVN architectures. The results obtained accordingly are as shown in Figure 8.  
Let us consider the result concerning the packet delivery ratio in Figure 8a. For any data collection frequency (\(f\)), the average PDR of the centralized architecture always achieves a value of 100% due to redundant data transmission from multiple nodes in this architecture. On the other hand, for the proposed optimization for data collection, the average PDR is less than 99% when the optimization frequency is less than or equal to 0.5 Hz. For the proposed optimization, the PDR always tends to increase with the increment of the frequency, as evident from Figure 8a. The reason for an increment in PDR with frequency is due to the fact that with an increment of optimization frequency, the probability of occurrence of data loss (packets not being delivered) due to broadcasting nodes being isolated (problem shown in Figure 3a) is low. The proposed method achieves an average PDR of 99.7% at 2 Hz and achieves 99.9% for frequencies greater than or equal to 4 Hz. Thus, in order to achieve a vehicular communication reliability benchmark of 99.9%, the optimization frequency must be greater than or equal to 4 Hz. As suggested by the lower average PDR values and low confidence interval lower limits for PDR in Figure 8a, nominal optimization frequencies less than or equal to 0.5 Hz are not acceptable for vehicular communication using the proposed method. On the other hand, the data collection using distributed SDVN architecture has the lowest average packet delivery ratio at any data collection frequency. The PDR for the distributed SDVN architecture tends to increase for data collection frequencies in the range of 0.02 to 0.25, and then has been saturated at a fixed value for higher frequencies indicating that data collection frequency has a lower impact on PDR in VANETs. As evident from Figure 8b, the centralized architecture's cost per node per data-gathering cycle is always higher than that of the proposed optimization for the hybrid SDVN architecture for any given optimization/ data collection frequency. While the centralized architecture's cost gradually decreases with the increment of the data collection frequency (\(f\)), the cost of the proposed data collection for hybrid architecture gradually increases with the increment of the optimization frequency (\(f^{\prime}\)). Furthermore, the gap (cost) between the proposed method and data collection for centralized architecture reduces with the increment of the optimization frequency as evident from Figure 8b. On the other hand, the average cost per node per data-gathering cycle for the distributed SDVN architecture remains at a constant value of 2.734 and does not vary with the data collection frequency. Now, let us analyze the result for channel utilization given in Figure 8c. It is clear from Figure 8c that for all data collection methods, the channel utilization for any given channel increases with the increment of optimization frequency (data collection frequency for centralized and distributed architectures). The DSRC utilization curves for all 3 methods overlap, suggesting that all methods have the same DSRC utilization for a given frequency. At all the frequencies, the proposed method's LTE utilization is lower than that of the centralized architecture, while the Ethernet channel utilization of the proposed method is always higher than that for the data collection in a centralized architecture. That is because  
Figure 8: Performance evaluation of the data collection methods under different optimization/data collection frequencies.