# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
Another important fact to note in Figure 10a is that the gradients of the cost variation of the centralized architecture for all mobility scenarios are higher than the gradients of the cost variation for the proposed method. In other words, the increment of cost with the speed of the proposed method is lower compared to the centralized architecture. For all mobility scenarios, the average cost of the centralized architecture has always been higher than the average cost of the proposed method for all speeds. However, the performance gap for cost per node per data-gathering cycle between the two data collection methods varies in the increasing order as urban, rural, and autobahn. Figure 10: Performance evaluation of the data collection methods under different mobility scenarios. (**a**) Communication cost per node per data-gathering cycle variation for different mobility scenarios. (**b**) Average channel utilization per data-gathering cycle variation for different mobility scenarios. (**c**) End-to-end latency per node per data-gathering cycle variation for different mobility scenarios. (**d**) Packet delivery ratio per data-gathering cycle variation for different mobility scenarios. Now, let us analyze the channel utilization variation shown in Figure 10b. For all mobility scenarios, the proposed method's channel utilization for LTE has always been lower than that of the centralized architecture. In contrast, for all mobility scenarios, the proposed method's channel utilization for Ethernet has always been higher than that of the centralized architecture. However, for all mobility scenarios, the DSRC utilizations for all three architectures overlap one another and have essentially remained the same with changes in the maximum allowable speed of vehicles. For a given speed, the channel utilization for DSRC and Ethernet between different mobility scenarios are almost the same, while significant differences can be observed in the LTE channel utilization between different mobility scenarios for the hybrid and centralized architectures. For the centralized architecture, the LTE channel utilization for each mobility scenario in the increasing order is urban, rural, and autobahn. This variation explains the reason for the communication cost variation for the centralized architecture with mobility in the exact same order. However, for the proposed method, the LTE channel utilization for the urban mobility scenario decreases from 0-20 kph, and then begins to increase from 30-60 kph. For the proposed method, the LTE channel utilization gradually drops with an increment of speed for the rural mobility scenario while it essentially remains the same for 0-50 kph, and then increases at 90 kph and again essentially remains the same from 90-250 kph in the autobahn mobility scenario. For the autobahn mobility scenario, the LTE channel utilization gap between the proposed method and centralized architecture gets narrower with the increment of the speed of vehicles. However, note that for the proposed method, LTE channel utilization values for the urban and rural mobility scenarios are similar, and relatively higher values can be observed for the autobahn scenario. The preceding pattern is the reason for observing similar communication cost values for urban and rural mobility scenarios, and relatively higher communication cost values for autobahn mobility scenarios for data collection using the proposed method. One of the main reasons for the increment of LTE utilization and cost for the autobahn scenario is that vehicles tend to exist in clusters in the autobahn mobility scenario, having a higher number of neighbors per node than in other mobility scenarios. On the other hand, LTE and Ethernet channel utilization for the distributed architecture is always zero for any speed under any mobility scenario, as those channels are not utilized in distributed architecture.