# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
At low entropy threshold values, optimization is done frequently so that the LTE channel for downlink broadcasting of the solution is frequently utilized. On the other hand, for the distributed SDVN architecture, both LTE communication channel and Ethernet communication channel utilization is always zero as those channels are not utilized by VANETs. Now, let us consider the result on end-to-end latency in Figure 7d. As evident from Figure 7d, the proposed optimization for hybrid architecture has a much lower latency than the centralized architecture for any entropy threshold value, as the upper limit of the 95% confidence interval of the proposed method's latency lies much lower than the lower limit of the 95% confidence interval of the centralized architecture's latency values. An increasing trend for the latency can be observed with the increment of the entropy threshold values for the proposed method, except after a threshold of 0.10, where there is a sudden drop in latency. On the other hand, the average latency for VANETs is much lower than both the proposed method and data collection for centralized architecture, as VANETs do not utilize LTE and Ethernet communication channels. When analyzing the overall result, there are sudden drops in PDR, communication cost, LTE channel utilization, and latency, as evident from Figures 7a-c and 7d, respectively, in the proposed data collection method for hybrid SDVN architecture for entropy thresholds greater than 0.10. This sudden decrease in cost, latency, LTE utilization, and PDR is due to the fact that for entropy thresholds greater than 0.10, optimization has occurred only in the very first data-gathering cycle, and a new solution has not been found after that, as proven by the optimization percentage values (0.8%) in Figure 7a. Therefore, PDR drops as broadcasting nodes become isolated and a new solution for optimization is not found due to the large entropy threshold. Thus, due to poor PDR, the decrement in cost, LTE utilization, and latency for higher entropy thresholds greater than 0.10 cannot be interpreted as performance gains. Now, let us determine an optimum value for the threshold. As entropy thresholds less than or equal to 0.005 resulted in a PDR of 99.7%, the optimum value should be selected from that set. In terms of cost and channel utilization, the optimum threshold value is 0.005, as evident from Figure 7b,c, and in terms of end-to-end latency, the optimum threshold is 0.000 as evident from Figure 7d. At a threshold of 0.000, optimization always takes place, and at a threshold of 0.005, optimization takes place 80% of the time, as evident from Figure 7a. We expect the reduction in cost for a threshold of 0.005 than that of 0.000 is due to this 20% reduction in optimization, which requires extra resources for the downlink broadcasting step. Thus, considering the advantage in channel utilization, and cost, and as it has the highest average PDR of 99.7% for the considered nominal optimization frequency of 2 Hz, we set the optimization threshold to 0.005 for the rest of this paper unless otherwise specified. 3.2 Effect of Nominal Optimization Frequency (\(f^{\prime}\)) for Average Communication Cost, Average Channel Utilization, Average End-to-End Latency, and Average Packet Delivery Ratio  
In this experiment also, an urban mobility scenario with the maximum speed of vehicles set to 60 kph is selected where there are 40 vehicles and 20 RSUs in the simulation. The network entropy threshold value for optimization is set as zero as needed to study the effect of nominal optimization frequency in this experiment. Otherwise, if an entropy threshold is set, it will have an effect on the results for nominal optimization frequency. Thus, a new solution to the optimization is found at a rate of nominal optimization frequency. As \(f=kf^{\prime}\), we set the data collection frequency (\(f\)) equal to the nominal optimization frequency (\(f^{\prime}\)) by setting \(k\) value to 1.