Delay-Aware Task Offloading for Heterogeneous VLC-RF-based Vehicular Fog Computing

TL;DR

This paper proposes a delay-aware task offloading scheme in a heterogeneous VLC-RF vehicular fog computing system, optimizing resource allocation to reduce processing delay in dense vehicular environments.

Contribution

It introduces a novel VLC-RF architecture with a residual-based optimization algorithm for efficient task offloading in vehicular fog computing.

Findings

Achieves 15% reduction in average task processing delay.

Effectively exploits VLC interference resilience and RF coverage.

Demonstrates improved performance over single-technology VFC systems.

Abstract

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.