# Analysis of Mobile Edge Computing for Vehicular Networks +
## 4 Further Analysis of Delay  
In a challenging environment such as a vehicular network serving autonomous vehicles, delay can have a significant impact on real-time applications. Thus, an architecture that has some a priori knowledge of network delay, and the ability to make informed decisions on which resource to send a request, is desirable. To evaluate the variations in communication delay, we conduct a serious of experiments and record the time spent processing the request along with the time taken for the requesting node to receive the response. In Figure 8, we target the three geographically dispersed server locations and the local server for delay analysis. Each server is sent a series of requests with increasing data size, where data is split into chunks by week. The number of data chunks for each test are shown on the plots as the numbers 1 though 8 on the \(x\)-axis. The total time of the requests is shown on the \(y\)-axis in milliseconds. The overall time taken for each request is the sum of the two bar segments. CPU time is shown in blue and delay in orange. Recall that the \"cin\" server was a resource allocated in our lab at the University of Cincinnati and had the lowest delay since it was very close to the requesting node. The request made to the servers is the intersection discovery technique detailed in a previous section of this paper. Here we have passed two large values for \(r\) of 0.25 km and 0.5 km. Here the intersection coordinates could be derived by taking the centroid of the points that are found to be in the circle with radius \(r\). The run time increased in a linear fashion and was of little interest for our purposes. However, it can \begin{table}
\begin{tabular}{c c c} \hline \hline
**Alternatives** & **JSON (ms)** & **Int. Enc. Discovery (ms)** \\ \hline \(A_{central}\) & 1040.5 & 4923.75 \\ \(A_{east}\) & 1010.125 & 4947.875 \\ \(A_{west}\) & 948.125 & 4513.375 \\ \(A_{cin}\) & 960.25 & 4532 \\ \(A_{local}\) & 587 & 4160 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average run-times in milliseconds (ms). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alternatives** & **TOPSIS Rank** & **JSON Rank** & **Int. Enc. Discovery Rank** \\ \hline \(A_{central}\) & 5 & 5 & 4 \\ \(A_{east}\) & 2 & 4 & 5 \\ \(A_{west}\) & 4 & 2 & 2 \\ \(A_{cin}\) & 3 & 3 & 3 \\ \(A_{local}\) & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of technique for order of preference by similarity to ideal solution (TOPSIS) ranking with test cases. easily be seen that the delay varies greatly between requests. The spread of this delay can be used to provide a better representation of what delay can be expected for a specific resource. While the round trip communication delay may have improved with a wired connection, we purposefully chose a wireless standard since we are seeking to emulate a wireless environment. Each of the experiments in Figure 8 has been conducted on a lab machine and requests were sent with a WiFi connection to the Internet. Since our target environment involves mobile nodes and wireless communication, we conducted additional experiments where requests were sent over wireless LTE network in various locations and while the requesting node is moving. The LTE network provider for the experiments was Google Fi (known as Project Fi at the time of the experiments) and the experiments are conducted with a Google Pixel 2 XL device. Experiments with the mobile device were conducted while the device was moving at a walking pace in a congested area. The path covered in the mobile experiments traversed the campus of the University of Cincinnati, Cincinnati, OH, USA, which has many trees, tall buildings, and other obstructions. Figure 8: _Cont._  
Figure 9 shows experiments in which requests were sent to the central server from two locations and while the device is moving at a walking pace between the two locations. The computations requested were the JSON parsing task that has been detailed in an earlier section.