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Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN

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Abstract

The Fog Computing was proposed to extend the computing task to the network edge in lots of Internet of Things (IoT) scenario, such as Internet of Vehicle (IoV). However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability of IoV. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture which composed by fog computing radio access network (F-RAN) and Vehicular Fog Computing (VFC), which is called VF-based F-RAN. In addition, we propose a heuristic algorithm enhanced by deep learning to optimize the computation offloading in this hybrid architecture. The simulation result reveals that the proposed hybrid fog architecture with the heuristic algorithm can effectively improve the data processing efficiency and balance the Quality of Experience (QoE).

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Acknowledgments

This work was supported in part by the Foundation of China under Grant 2017YFC0821305, 2016QY03D0604 and partially supported by the SCST Project Number 18511105902 Foundation.

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Tianpeng Ye, Xiang Lin, Jun Wu, Gaolei Li & Jianhua Li

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  1. Tianpeng Ye
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  2. Xiang Lin
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  3. Jun Wu
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  4. Gaolei Li
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  5. Jianhua Li
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Correspondence to Xiang Lin.

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This article belongs to the Topical Collection: Special Issue on Data Science in Cyberspace 2019

Guest Editors: Bin Zhou, Feifei Li and Jinjun Chen

Appendix

Appendix

To simplify the representation, we use {p0,p1,...,pk} to instead of all the elements in (3). First, we order that p0 = p1 = ... = pk. Therefore, Qi = p0. When the offloading data size is changed, the function will be as follows:

$$ \begin{array}{@{}rcl@{}} {Q_{i}} &=& \max \{ {p_{0}} \pm \varepsilon ,{p_{1}},...,{p_{w}} \mp o(\varepsilon ),{p_{k}}\} ,\forall \varepsilon > 0 \\ {Q_{i}} &=& \left\{ \begin{array}{l} {p_{0}} + \varepsilon \\ {p_{w}} + o(\varepsilon ) \end{array} \right. > {p_{0}} \end{array} $$
(10)

Where o() is a function denoting the change of the vehicle computing time consumption which leaded by the change of the offloading data size.

Therefore, we can prove that Qi can be minimized when p0 = p1 = ... = pk.

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Ye, T., Lin, X., Wu, J. et al. Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN. World Wide Web 23, 2547–2565 (2020). https://doi.org/10.1007/s11280-020-00808-9

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