Most recently, vehicular fog computing (VFC) has been regarded as a novel and promising architecture to effectively reduce the computation time of various vehicular application tasks in Internet of vehicles (IoV). However, the high mobility of vehicles makes the topology of vehicular networks change fast, and thus it is a big challenge to coordinate vehicles for VFC in such a highly mobile scenario. In this paper, we investigate the joint task assignment and resource allocation optimization problem by taking the mobility effect into consideration in vehicular fog computing. Specifically, we formulate the joint optimization problem from a Min-Max perspective in order to reduce the overall task latency. Then we decompose the nonconvex problem into two sub-problems, i.e., one to one matching and bandwidth resource allocation, respectively. In addition, considering the relatively stable moving patterns of a vehicle in a short period, we further introduce the mobility prediction to design a mobility prediction-based scheme to obtain a better solution. Simulation results verify the efficiency of our proposed mobility prediction-based scheme in reducing the overall task completion latency in VFC.
|Original language||English (US)|
|Title of host publication||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - May 2020|
|Event||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of|
Duration: May 25 2020 → May 28 2020
|Name||IEEE Wireless Communications and Networking Conference, WCNC|
|Conference||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020|
|Country||Korea, Republic of|
|Period||5/25/20 → 5/28/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under Grants 61936014 and 61901302, and in part by the National Key Research and Development Project under Grant 2019YFB2102300, 2019YF-B2102301, and 2017YFE0119300. This work was also supported in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2020D01), in part by the Scientific Research Project of Shanghai Science and Technology Committee under Grant 19511103302, and in part by the National Science Foundation under Grant CPS-1932413 and ECCS-1935915.
- mobility prediction
- resource allocation
- task assignment