# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Results on Optimization of Data Collection  
Distributed architecture has only the broadcasting step in data collection and does not have a data unicasting step to the control server. However, in order for future researchers to investigate more in this area, we perform a comparative analysis of the proposed optimization framework for the hybrid architecture against the distributed SDVN architecture as well. Furthermore, we compare it against the centralized architecture, which indeed has the unicasting communication with the centralized control plane in addition to the broadcasting step, which is a fair comparison against the proposed method, as both methods have unicasting and broadcasting steps. 3.1 Effect of Network Entropy Change Threshold for Average Communication Cost, Average Channel Utilization, Average End-to-End Latency, Packet Delivery Ratio, and Optimization Percentage  
In this experiment, the urban mobility scenario given in Figure 4(a) with the maximum speed for vehicles set to 60 kph is employed where both the data collection frequency (\(f\)) and nominal optimization frequency (\(f^{\prime}\)) are set to 2 Hz. In this scenario, 120 vehicles, and 60 RSUs exist in the network where the variable for the proposed data collection in a hybrid SDVN architecture is the network entropy threshold (T). There exists no variable for the centralized SDVN architecture and distributed SDVN architecture in this experiment. Performance evaluation metrics are assessed by varying the T value and the results for the data collection in three architectures are as shown in Figure 7. Note that colored areas in the graphs indicate the 95% Confidence Intervals (CIs). The motivation to check an entropy threshold value to decide on optimization is to reduce the communication utilization and costs that occur in the optimization step, such as downlink broadcasting of the solution. Thus, in this approach, for minor changes in the network, finding a new solution for optimization will not be carried out. However, for significant topology changes, a new solution to the optimization is found out. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline RSU backbone delay & 10 \(\mathrm{\SIUnitSymbolMicro s}\) \\ \hline Broadcasting data payload size & 84 bytes (centralized and distributed architectures), (\(84+4\mathcal{N}_{i}\)) in hybrid architecture \\ \hline Broadcasting metadata payload size & 12 bytes (Exists in all nodes in the first data-gathering cycle and agent nodes in subsequent cycles in hybrid architecture) \\ \hline Unicasting uplink data payload size & \(4\sum_{m\in\mathcal{S}}(\mathcal{N}_{m}Z_{m}+1)+84(\mathcal{N}_{i}+1)\) is the maximum probable payload size for hybrid architecture, \(84(\mathcal{N}_{i}+1)\) for centralized architecture, \(0\) for distributed architecture \\ \hline Unicasting uplink metadata payload size & \(12+4\mathcal{N}_{i}\) (Exists only in the first data-gathering cycle of hybrid architecture) \\ \hline Broadcasting downlink metadata payload size & 2N bytes \\ \hline Communication cost per byte & 1—wired, 2—DSRC, 40—Cellular \\ \hline Optimization coefficients & \(C_{CN}=2,\ C_{CA}=1,\ C_{O}=1\) \\ \hline Data collection frequency & Variable (\(f\)) in the range [0.02, 10] \\ \hline Nominal optimization frequency & Variable (\(f^{\prime}\)) in the range [0.02, 10] \\ \hline Maximum velocity of vehicles & Variable (\(V_{max}\)) in the range [0, 250] \\ \hline Number of Nodes & Variable (\(\mathcal{N}\)) in range [4, 256] \\ \hline Network link entropy change threshold & Variable (T) in range [0, 1] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._It is clear from the PDR result in Figure 7a that for entropy thresholds greater than or equal to 0.005, the average packet delivery ratio is less than 99.7%. Thus, at a 2 Hz nominal optimization frequency, only entropy thresholds less than or equal to 0.005 are reliable enough to deliver packets at the destination. A benchmark PDR of 99.9% is required for high-end V2X services [48; 58].