# Analysis of Mobile Edge Computing for Vehicular Networks +
## 5 Conclusions and Future Work  
In this paper, we introduced the CAMEVAN architecture for vehicular networks that supports edge-computing and employs a multiple-criteria decision analysis method to choose an optimal resource for delegation of computational tasks. We present an example using the TOPSIS decision-making method, and validate our results with experiments on geographically diverse test bed of computational resources. Our experimental results support the results of the decision-making process and is accurate for the majority of test cases. Our testing considered multiple criteria in the decision-making process, including real-time cpu load, real time memory availability, and real time network path delay. To produce a more robust decision-making model, more criteria could be considered or criteria weights could be adjusted to better suite individual situations.  
In our decision-making process, we consider criteria that pertain to the computational resources and their geographical location. In future work, we would like to incorporate criteria that relate to an individual job, such as the amount of data that must be transmitted to the computational resource, computational complexity of the task, and priority of the task. Incorporating these criteria that vary for each job, would allow decisions to be made with respect to both the computational resources available and characteristics on the computational job. This type of decision-making process, would allow the model to bias jobs that require more or less of a specific resource to be the best alternative. For example, if a job is computationally intensive, but requires very little memory, alternatives with small amounts of available memory wouldn't be considered less favorable then alternatives with large amounts of available memory.  
For future work, we would like to continue the experiments with a mobile node and incorporate more parameters into the allocation process. Mobile nodes present a unique challenge in that they have significantly varying network delay. Accommodating this uncertainty would require a robust and intelligent system. One potential approach would be to maintain historical measures of average delay for each resource. With each request the delay could be measured and used to update the information about a specific resource. Our experiments with delay have shown that geographically diverse resource can have differing delays and could be subjected to unexpected spikes in delay. We have also shown that current wireless communication technologies exhibit consistent delay while travelling at low speeds. While the average delay is higher than an ideal level for geographically distant resources, mobile edge computing would move resources closer to the edge, thus reducing the delay while maintaining consistency. Finally, it would be beneficial to repeat the experiments for a mobile device in a moving vehicle or, if possible, from an Internet connected vehicle.  
Figure 11: Analysis of delay spread with high fliers shown. (**a**) Analysis of delay spread for intersection encounter requests sent to the “east”, “central”, and “west” servers. (**b**) Analysis of delay spread from a mobile device with requests being made while stationary at locations one and two and while walking between the two locations.  
**Author Contributions:** Conceptualization, Z.W.L. and D.P.A.; formal analysis, Z.W.L.; software, Z.W.L.; supervision, D.P.A.; writing--original draft, Z.W.L.; writing--review and editing, D.P.A.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.