Journal of System Simulation
Abstract
Abstract: As intelligent vehicles are equipped with more and more sensors, the explosive growth of sensor data is generated, which brings severe challenges to vehicular communication and computing. In addition, the modern road presents a three-dimensional structure, and the system architecture of traditional vehicular networks cannot guarantee full coverage and seamless computing. A task offloading strategy for UAV-assisted and 6G-enabled (Sixth Generation) vehicular edge computing networks is proposed. Furthermore, a flexible and intelligent vehicular edge computing mode is composed by vehicles and UAVs, which provide three-dimensional edge computing services for delay-sensitive and computation-intensive vehicular tasks, and ensure timely processing and fusion of massive sensor data. Finally, the optimal task offloading strategy in the network is obtained by an algorithm based on reinforcement learning.
Recommended Citation
Hu, Feng; Gu, Haiyang; and Lin, Jun
(2023)
"UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks,"
Journal of System Simulation: Vol. 35:
Iss.
11, Article 7.
DOI: 10.16182/j.issn1004731x.joss.23-0307
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss11/7
First Page
2373
Last Page
2384
CLC
TP273; TP391
Recommended Citation
Hu Feng, Gu Haiyang, Lin Jun. UAV-enabled Task Offloading Strategy for Vehicular Edge Computing Networks[J]. Journal of System Simulation, 2023, 35(11): 2373-2384.
DOI
10.16182/j.issn1004731x.joss.23-0307
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