# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection
As for this experiment, all nodes of the network exist in a fixed map of areas 1500 m \(\times\) 1500 m, when the number of nodes increases, the node density increases, and thus, the average entropy of the network should also increase. Therefore, as the network gets more complex (bigger with higher average entropy), the latency for the centralized architecture increases as the number of links between the nodes increases. On the other hand, for the proposed method, when the network gets bigger and the average entropy of the network increases, the latency is reduced by the objective function of the proposed method, which is aware of the links between nodes and effectively forwards traffic through agents that have more neighbors, and by utilizing more Ethernet links as agents which have lower cost and delay. Therefore, for large networks, the proposed method will be very effective in reducing latency per node per data-gathering cycle. However, the latency per node per data-gathering cycle for the distributed architecture, which barely varies with the network size, is much lower than both the proposed method and the centralized architecture for any network size. As evident from Figure 9d, a clear relationship between the network size and the PDR for the proposed method cannot be observed. For the data collection in centralized architecture, the PDR is always 100% for any network size due to redundant data transmission by the nodes. However, for network sizes of four and eight, the average PDR for the proposed method has reached 100%, and a minimum average value of 99.9% was observed for the network size of 16. For all other network sizes except 16, the average PDR of the proposed method had been more than 99.9% value, as evident from Figure 9d, which is a remarkable achievement, as it had attained the ultra-reliability benchmark required for vehicular communication. On the other hand, for the distributed SDVN architecture, the packet delivery ratio is extremely poor for lower network sizes, and it grows with the increment of network size, and the average PDR reaches 91% for network size of 256. 3.4 Effect of Mobility for Average Communication Cost, Average Channel Utilization, Average End-to-End Latency, and Average Packet Delivery Ratio  
In this experiment, 120 vehicles and 60 RSUs exist in the vehicular network where the data collection and nominal optimization frequencies are set to 4 Hz, based on the result in Section 4.3.2. The optimization threshold for the proposed data collection method for the hybrid SDVN architecture is set as 0.005, based on the results obtained in Section 4.3.1. The variable in this experiment is the allowable maximum speed of vehicles for three mobility scenarios: urban, non-urban, and autobahn. The proposed data collection method for the hybrid SDVN architecture, data collection in centralized SDVN architecture, and data collection in distributed SDVN architecture were evaluated for each of the mobility scenarios by varying the maximum allowable speed of vehicles to obtain the results in Figure 10.  
It is very evident from Figure 10a that for all mobility scenarios, the average cost for data collection in both the proposed hybrid architecture and centralized architecture increases with speed. However, the costs for data collection for a given architecture for a given speed vary between different mobility scenarios for the hybrid and centralized SDVN architectures. The increasing order of cost for both architectures is urban, rural, and autobahn. The preceding order of cost can be explained due to the coverage of 139 m, 278 m, and 347 m for urban, rural, and autobahn mobility scenarios, respectively. Due to the preceding coverage specified, the average number of neighbors for a given node can be expected to vary in the same order. Thus, the average cost of the three mobility scenarios varies in the same order. In contrast, the cost for the distributed SDVN architecture does not vary with mobility and resides at a low fixed value of 2.734 irrespective of mobility.