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A Data Download Method from RSUs Using Fog Computing in Connected Vehicles

Dae-Young Kim1, Seokhoon Kim2,*

School of Information Technology, Daegu Catholic University, Gyeongsan-si 38430, Republic of Korea.
Department of Computer Software Engineering, Soonchunhyang University, Asan-si 31538, Republic of Korea.

* Corresponding Author: Seokhoon Kim. Email: email.

Computers, Materials & Continua 2019, 59(2), 375-387.


Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and an RSU has poor connection quality, the efficiency and speed of the data download from the RSU is decreased. This problem affects the quality of the user experience. Therefore, it is important for a connected vehicle to connect to an RSU with consideration of the network conditions in order to try to maximize download speed. The proposed method maximizes download speed from an RSU using a machine learning algorithm. To collect and learn from network data, fog computing is used. A fog server is integrated with the RSU to perform computing. If the algorithm recognizes that conditions are not good for mass data download, it will not attempt to download at high speed. Thus, the proposed method can improve the efficiency of high speed downloads. This conclusion was validated using extensive computer simulations.


Cite This Article

D. Kim and S. Kim, "A data download method from rsus using fog computing in connected vehicles," Computers, Materials & Continua, vol. 59, no.2, pp. 375–387, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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