# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 5 Discussion
The second one is by selecting agents that have lower communication costs and delays for the uplink. In a heterogeneous network, the optimizer tends to select RSUs as agents since they have lower communication costs and delays compared to vehicular nodes equipped with LTE links, which have comparatively higher communication costs and delays. Third, the proposed method adaptively sends metadata to the centralized planes, only if the metadata of an agent changes (only if the neighborhood of an agent changes). Fourth, it reduces downlink data transmission cost by performing optimization if and only if the normalized network link entropy change is greater than a specified entropy threshold. Thus, for large networks consisting considerable percentage of vehicular nodes, the cost for the proposed method is much lower than that for the data collection in the centralized architecture for any nominal optimization frequency or any mobility scenario. The proposed method also reduces the average end-to-end latency using several methods. First, as agents, the optimizer selects nodes that have a lower delay. Thus, the optimizer tends to select RSU nodes as agents as they have a lower delay compared to vehicular nodes. For the proposed method, the data is sent to the centralized planes only by the agents. However, for the centralized architecture, as all nodes are agents, all nodes send data to the centralized planes, thus having a higher cost and delay than the proposed method even in a no RSU scenario. In a distributed architecture, both the cost and latency per node per data-gathering cycle is much lower than in both hybrid and centralized architectures. However, this low cost and latency are entirely because of the distributed SDVN architecture, or in other words, the VANET does not send data to the centralized planes where LTE and Ethernet communication channels are not utilized. Therefore, VANETs do not have knowledge regarding the entire vehicular network as there is no centralized controller collecting status or other data from nodes in order to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Size** & \(P_{\text{HI}}\) & \(P_{\text{H2}}\) \\ \hline
4 & 0.0004 & 0.0559 \\ \hline
8 & 0.0004 & 0.1358 \\ \hline
16 & 0.0000 & 0.1249 \\ \hline
32 & 0.0000 & 0.0021 \\ \hline
64 & 0.0000 & 0.0004 \\ \hline
[96, 128, 160, 192, 224, 256] & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table of \(p\) values of null hypotheses for different network sizes. make network decisions. Thus, the achievement of low delay and cost in VANETs is at the expense of not having centralized control, which gives programmability and flexibility to the network. On the other hand, the proposed data collection for the hybrid architecture has moderate latency and cost (higher than distributed architecture and lower than centralized architecture) values, at the same time ensuring centralized control in the SDN paradigm. Therefore, we can conclude that the proposed data collection is the method that achieves the lowest latency and cost out of the methods which have centralized control that get the advantages of programmability and flexibility. In the process of achieving a lower average communication cost and latency by the proposed method, it is clear, according to the results, that the average LTE channel utilization of the proposed method has been utilized less, and the average Ethernet channel utilization has been utilized higher than that of the centralized architecture except for smaller networks (network sizes less than or equal to 16). The DSRC channel utilizations for all three methods overlap with one another for any mobility scenario, network size, or nominal optimization frequency. The reason for that is data broadcasting step in all methods behaves similarly such that DSRC channel utilization is the same. In other words, for the centralized and distributed architectures, all nodes broadcast data at a given time step.