Distributed Resource Allocation and Task Offloading for Vehicular Edge of Things Computing
Ghada Afifi, Bassem Mahmoud Mokhtar · 2025
Vehicular Edge of Things Computing (VEoTC) schemes have emerged to enhance the Quality of Experience (QoE) of users requesting computational tasks. Such schemes exploit various computing resources embedded in Service Vehicles (SVs) to provide on demand computation. Typically, Vehicular Cloud Computing (VCC) and Vehicular Edge Computing (VEC) facilitate Internet of Vehicles (IoV) applications. However, the stringent latency guarantees required by such applications are challenging to satisfy given the high mobility and fluctuating densities of users in vehicular environments. Alternatively, VEoTC schemes have the potential to extend the computational coverage to areas with no or limited Roadside Unit (RSU) infrastructure and minimize the task latency as services are provided closer to the user. The SVs serve as mobile edge computing units providing computational services to vehicular users. In this paper, we propose a distributed task scheduling scheme to maximize the QoE of users in vehicular environments. The proposed technique facilitates task offloading to the SVs and/or RSUs through joint computing resource and channel allocation. Comparative experiments demonstrate that the proposed distributed approach enhances the QoE of users under different operational conditions.