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Sensors 2019, 19(6), 1303;

Analysis of Mobile Edge Computing for Vehicular Networks

Center for Distributed and Mobile Computing, EECS Department, University of Cincinnati, P.O. Box 210030, Cincinnati, OH 45221-0030, USA
Author to whom correspondence should be addressed.
This paper is an extended version of “Context-Aware Mobile Edge Computing in Vehicular Ad-Hoc Networks” published in the Proceedings of the 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, Australia, 21–23 November 2018.
Received: 26 February 2019 / Revised: 6 March 2019 / Accepted: 12 March 2019 / Published: 15 March 2019
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Vehicular ad-hoc Networks (VANETs) are an integral part of intelligent transportation systems (ITS) that facilitate communications between vehicles and the internet. More recently, VANET communications research has strayed from the antiquated DSRC standard and favored more modern cellular technologies, such as fifth generation (5G). The ability of cellular networks to serve highly mobile devices combined with the drastically increased capacity of 5G, would enable VANETs to accommodate large numbers of vehicles and support range of applications. The addition of thousands of new connected devices not only stresses the cellular networks, but also the computational and storage requirements supporting the applications and software of these devices. Autonomous vehicles, with numerous on-board sensors, are expected to generate large amounts of data that must be transmitted and processed. Realistically, on-board computing and storage resources of the vehicle cannot be expected to handle all data that will be generated over the vehicles lifetime. Cloud computing will be an essential technology in VANETs and will support the majority of computation and long-term data storage. However, the networking overhead and latency associated with remote cloud resources could prove detrimental to overall network performance. Edge computing seeks to reduce the overhead by placing computational resources nearer to the end users of the network. The geographical diversity and varied hardware configurations of resource in a edge-enabled network would require careful management to ensure efficient resource utilization. In this paper, we introduce an architecture which evaluates available resources in real-time and makes allocations to the most logical and feasible resource. We evaluate our approach mathematically with the use of a multi-criteria decision analysis algorithm and validate our results with experiments using a test-bed of cloud resources. Results demonstrate that an algorithmic ranking of physical resources matches very closely with experimental results and provides a means of delegating tasks to the best available resource. View Full-Text
Keywords: cloud computing; distributed computing; mobile computing; VANET; wireless networks cloud computing; distributed computing; mobile computing; VANET; wireless networks

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Lamb, Z.W.; Agrawal, D.P. Analysis of Mobile Edge Computing for Vehicular Networks. Sensors 2019, 19, 1303.

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