Name
Abstract | ||
|---|---|---|
With the growing trend of making vehicles smarter, the idea of utilizing vehicles as the infrastructures for communication and computation has triggered great interest. There have been increasingly efforts integrating the connected vehicles into a cloud computing system, but the performance of such system is restricted by its high latency. To solve this problem, a new computing paradigm, fog computing has been proposed to better exploit potential computing resources of connected vehicles with a collaborative multitude of end-user clients or near-user edge devices [8]. The fog computing differs from cloud computing by its proximity to end users, dense geographical distribution and support for mobility. However, current studies on fog computing based vehicular system mainly focus on its reliability and security issues instead of investigating realistic scenarios. To the best of our knowledge, this paper is the first to propose vehicular fog computing by studying its capabilities using realistic data acquired from tens of thousands of taxis in Beijing, China. A mathematical model is developed for vehicular fog computing, based on which we can make prediction of potential computing capacity of a vehicular fog and analyze the impact of communication range on the capacity. Then we present temporal and spatial distribution of potential computation capacity of vehicular fog computing in a city-wide scale. Our study quantitatively reveals the capabilities of vehicular fog computing at different scales, which offers insightful guidelines for the related system and protocol designs in the future. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.03.065 | Information Sciences |
Keywords | Field | DocType |
Vehicular fog computing,Traffic jam,Cloud computing,Vehicular network | End user,Taxis,Fog computing,Exploit,Edge device,Artificial intelligence,Machine learning,Beijing,Mathematics,Cloud computing,Distributed computing,Computation | Journal |
Volume | ISSN | Citations |
501 | 0020-0255 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuefeng Xiao | 1 | 0 | 0.34 |
| Xueshi Hou | 2 | 144 | 7.32 |
| Xinlei Chen | 3 | 111 | 23.89 |
| Chenhao Liu | 4 | 4 | 1.74 |
| Yong Li | 5 | 2972 | 218.82 |