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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Road Side Unit-Assisted Learning-Based Partial Task Offloading for Vehicular Edge Computing System
AU - Li, Song
AU - Sun, Weibin
AU - Ni, Qiang
AU - Sun, Yanjing
PY - 2024/4/1
Y1 - 2024/4/1
N2 - The rapid development of vehicular networks creates diverse ultra-low latency constrained and computation-intensive applications, which bring challenges to both communication and computation capabilities of the vehicles and their transmission. By offloading tasks to the edge servers or vehicles in the neighbourhood, vehicular edge computing (VEC) provides a cost-efficient solution to this problem. However, the channel state information and network structure in the vehicular network varies fast because of the inherent mobility of vehicle nodes, which brings an extra challenge to task offloading. To address this challenge, we formulate the task offloading in vehicular network as a multi-armed bandit (MAB) problem and propose a novel road side unit (RSU)-assisted learning-based partial task offloading (RALPTO) algorithm. The algorithm enables vehicle nodes to learn the delay performance of the service provider while offloading tasks. Specifically, the RSU could assist the learning process by sharing the learning information among vehicle nodes, which improves the adaptability of the algorithm to the time-varying networks. Simulation results demonstrate that the proposed algorithm achieves lower delay and better learning performance compared with the benchmark algorithms.
AB - The rapid development of vehicular networks creates diverse ultra-low latency constrained and computation-intensive applications, which bring challenges to both communication and computation capabilities of the vehicles and their transmission. By offloading tasks to the edge servers or vehicles in the neighbourhood, vehicular edge computing (VEC) provides a cost-efficient solution to this problem. However, the channel state information and network structure in the vehicular network varies fast because of the inherent mobility of vehicle nodes, which brings an extra challenge to task offloading. To address this challenge, we formulate the task offloading in vehicular network as a multi-armed bandit (MAB) problem and propose a novel road side unit (RSU)-assisted learning-based partial task offloading (RALPTO) algorithm. The algorithm enables vehicle nodes to learn the delay performance of the service provider while offloading tasks. Specifically, the RSU could assist the learning process by sharing the learning information among vehicle nodes, which improves the adaptability of the algorithm to the time-varying networks. Simulation results demonstrate that the proposed algorithm achieves lower delay and better learning performance compared with the benchmark algorithms.
KW - Electrical and Electronic Engineering
KW - Computer Networks and Communications
KW - Aerospace Engineering
KW - Automotive Engineering
U2 - 10.1109/tvt.2023.3312301
DO - 10.1109/tvt.2023.3312301
M3 - Journal article
VL - 73
SP - 5546
EP - 5555
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
IS - 4
ER -