# Analysis of Mobile Edge Computing for Vehicular Networks +
## 3 Comparison of Task Delegation Scheme with Experimental Results
### Experimental Results
#### 3.3.2 Comparison of TOPSIS Ranking with Experimental Results  
To validate our approach, we implemented two test cases discussed previously and conducted a series of trials on the alternative infrastructure defined in the TOPSIS example. Recall that servers are located in different geographical locations to represent the communication delay associated with remote servers. Testing is accomplished by sending processing requests to each remote resource from a local lab machine connected to a WiFi network. We chose a wireless network to better replicate vehicular networks. However, a stationary lab machine does not accurately emulate the environment of a vehicular network. To remedy this shortcoming, we conducted another series of trials in which the requests were sent to the remote resources over an LTE network, as Cellular-V2X communication has become more popular. However, experiments were conducted with an off-the-shelf smart phone that operates in the LTE frequency band ranging 700 MHz to 2500 MHz. In the realm of vehicular networks, LTE is being considered as an enabling technology for vehicular operating in the 5.9 GHz frequency band as LTE-V2X [21]. Given that these technologies are still in development and an LTE connected vehicle was not available for experimentation, the best configuration for our experiments was an LTE smart phone. Our first test case is JSON parsing, as previously described in this section. Figure 6 plots the run times of the JSON parsing process on each remote resource for a range of data set sizes. The horizontal axis specifies the amount of data by the number of weeks over which it was collected. For example, the points at \(x\)-axis value 1 represents the run-time of JSON parsing for 1 week of GPS trace data. In both Figures 6 and 7, dotted lines represent the cpu time taken for the process, while the solid lines represent the total run time in milliseconds (ms) for the request including cpu time and network overhead. Our second test case is an intersection encounter discovery, as defined previously. This task is much more cpu intensive, as it requires a large number of Haversine distance calculations. Figure 7a,b plot the cpu and total time for each alternative with an intersection search radius values \(r\) of 250 and 500 m respectively. Figure 6: Run-times of JSON parsing for each resource. With the experimental results plotted for both processes, we can now compare the ranking provided in our TOPSIS model. If our ranking is accurate, we should see similarities between the performance of the remote resources and the rankings we obtained with the TOPSIS method. Comparisons between TOPSIS rankings and the actual performance are shown in Table 3. Our alternatives or server resources are compared by observing the total time for each processing task to complete. In the case of the JSON parsing task we can see, in Table 4, that ranking of our experiments vary from the TOPSIS ranking in two of five cases. However, on closer inspection, we find that ranking of the rankings is better than the ranking of the rankings. Figure 7: Intersection encounter analysis runtime with \(r=250\) m (**a**) and \(r=500\) m (**b**). that the two incorrectly ranked cases are reversed. Upon inspecting the average run time across all test cases for these two alternatives, shown in Table 4, we find that \(A_{east}\) with an average run time of 1010.125 ms and \(A_{west}\) with an average run time of 948.125 ms, only differ by 62 ms overall. Similarly, the rankings for the intersection encounter discover task exhibits a similar property in that two of five ranks are incorrect and are reversed from what TOPSIS provides. In this case, the difference between the two cases is even smaller than that of the JSON rankings. The average run times for the two incorrectly ranked alternatives \(A_{central}\) and \(A_{east}\) are 4923.75 ms and 4947.875 ms respectively and differ, on average, 24.125 ms across all test cases.