Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

TL;DR

This paper introduces a game-theoretic approach to optimize task offloading and migration in MEC-assisted vehicular networks, reducing overhead and improving task completion success rates.

Contribution

It proposes a novel game model for task offloading and migration considering interference, with algorithms that guarantee convergence and enhance performance.

Findings

Reduces computation overhead significantly.

Increases task processing success rate.

Ensures convergence to Nash equilibrium.

Abstract

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.