# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
However, at a 2 Hz optimization frequency, the proposed method fails to achieve the 99.9% PDR benchmark required for vehicular communication by 0.02%. However, as evident in Section 4.3.2, the proposed method will achieve the reliability benchmark of 99.9% at higher frequencies (frequencies greater than or equal to 4 Hz). The aim of this experiment is to study the effect of entropy threshold on reliability, cost, latency, etc., at a fixed frequency of 2 Hz. On the other hand, at a 2 Hz data collection frequency, the PDR of the centralized architecture has always been 100%. This high reliability in the centralized architecture is due to the fact that a given packet originating at a node is transmitted to the centralized controller by the node itself and all neighbors of the nodes that receive the packet. Thus, redundant data is transmitted by multiple nodes, however, it ensures that a given packet is always received at the centralized controller from one or more nodes. On the other hand, the packet delivery ratio for data collection in distributed SDVN architecture is much poorer than both other architectures, as it has an average value of 85.4%. This poor packet delivery ratio in VANETs occurs due to the presence of isolated  
Figure 7: Performance evaluation of the data collection methods under different entropy threshold values. (**a**) Packet delivery ratio and optimization percentage variation for different entropy thresholds. (**b**) Communication cost per node per data-gathering cycle variation for different entropy thresholds; (**c**) Average channel utilization per data-gathering cycle variation for different entropy thresholds. (**d**) End-to-end latency per node per data-gathering cycle variation for different entropy thresholds. nodes in the vehicular network where a data packet broadcast is not delivered to any other node. When considering the transmission cost, as evident from Figure 7b, the proposed optimization for SDVN architecture has a much lower cost at all entropy threshold values compared to that of the centralized architecture. The average cost of the centralized architecture is more than three times that of the proposed method. The cost for the proposed method slowly reduces with an increment of entropy threshold up to entropy threshold 0.005, then gradually increases with the threshold value, and then suddenly drops to a lower value for thresholds greater than 0.10. At 0 entropy threshold, the cost is high due to the optimization percentage being 100% (always optimized), and excess resources are consumed to broadcast the optimization solution to the nodes. So, up to a threshold of 0.005, the cost reduces gradually as a higher threshold reduces the optimization percentage, as evident from Figure 7a. After 0.005, the cost begins to gradually increase as an optimization solution that minimizes the cost is not found due to the higher threshold values even when the topology changes. However, the smallest communication cost exists for distributed SDVN architecture, as evident from Figure 7b as the VANETs only have DSRC communication cost and do not involve communication with the centralized planes. When considering the channel utilization results in Figure 7c, all proposed optimization for hybrid architecture, distributed, and centralized SDVN architectures have exactly the same DSRC utilization for any entropy threshold value. However, LTE utilization and Ethernet utilization differ among the three architectures. The proposed method has a higher Ethernet utilization for entropy threshold values less than 0.050 and Ethernet utilization for the proposed method approaches those of the centralized architecture for entropy threshold values greater than 0.050. Furthermore, the proposed method always has a lower LTE channel utilization for any entropy threshold value than that of the centralized architecture. The highest LTE utilization for the proposed method can be observed for the entropy threshold of zero and a decreasing trend can be observed with the increment of the entropy threshold.