Vehicular fog computing (VFC) is a promising paradigm for reducing the computation burden of vehicles, thus supporting delay-sensitive services in next-generation transportation networks. However, traditional VFC schemes rely on radio frequency (RF) communications, which limits their adaptability for dense vehicular environments. In this paper, a heterogeneous visible light communication (VLC)-RF architecture is designed for VFC systems to facilitate efficient task offloading. Specifically, computing tasks are dynamically partitioned and offloaded to idle vehicles via both VLC and RF links, thereby fully exploiting the interference resilience of VLC and the coverage advantage of RF. To minimize the average task processing delay (TPD), an optimization problem of task offloading and computing resource allocation is formulated, and then solved by the developed residual-based majorization-minimization (RBMM) algorithm. Simulation results confirm that the heterogeneous VLC-RF architecture with the proposed algorithm achieves a 15% average TPD reduction compared to VFC systems relying solely on VLC or RF.
@article{arxiv.2601.10978,
title = {Delay-Aware Task Offloading for Heterogeneous VLC-RF-based Vehicular Fog Computing},
author = {Nan An and Hongyi He and Fang Yang and Chang Liu and Jian Song and Zhu Han and Binbin Zhu},
journal= {arXiv preprint arXiv:2601.10978},
year = {2026}
}