Potential Game Based Distributed IoV Service Offloading With Graph Attention Networks in Mobile Edge Computing

Vehicular services aim to provide smart and timely services (e.g., collision warning) by taking the advantage of recent advances in artificial intelligence and employing task offloading techniques in mobile edge computing. In practice, the volume of vehicles in the Internet of Vehicles (IoV) often surges at a single location and renders the edge servers (ESs) severely overloaded, resulting in a very high delay in delivering the services. Therefore, it is of practical importance and urgency to coordinate the resources of ESs with bandwidth allocation for mitigating the occurrence of a spike traffic flow. For this challenge, existing work sought the periodicities of traffic flow by analyzing historical traffic data. However, the changes in traffic flow caused by sudden traffic conditions cannot be obtained from these periodicities. In this paper, we propose a distributed traffic flow forecasting and task offloading approach named TFFTO to optimize the execution time and power consumption in service processing. Specifically, graph attention networks (GATs) are leveraged to forecast future traffic flow in short-term and the traffic volume is utilized to estimate the number of services offloaded to the ESs in the subsequent period. With the estimate, the current load of the ESs is adjusted to ensure that the services can be handled in a timely manner. Potential game theory is adopted to determine the optimal service offloading strategy. Extensive experiments are conducted to evaluate our approach and the results validate our robust performance.

I. Introduction

In modern metropolis, due to the high density of population and the increase in car ownership, urban traffic problems (e.g., traffic block) are becoming increasingly prominent. With the increasing development of wireless communication as well as artificial intelligence, Internet of vehicles (IoV) are capable of providing innovative services such as automatic driving and collision warning, thus alleviating the current traffic pressure, which significantly improves users’ travel experience. Nevertheless, the limited computation resources make the vehicles fail to satisfy the high requirements for real-time service processing, posing a series of traffic security risks [1]. In addition, in the case of increasingly expensive fuel resources, the energy consumption generated by vehicular equipments will also increase the additional cost of users, resulting in the users’ economic burden.