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

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E-pub ahead of print
  • Qinting Jiang
  • Xiaolong Xu
  • Muhammad Bilal
  • Jon Crowcroft
  • Qi Liu
  • Wanchun Dou
  • Jingyan Jiang
<mark>Journal publication date</mark>12/03/2024
<mark>Journal</mark>IEEE Transactions on Intelligent Transportation Systems
Number of pages14
Publication StatusE-pub ahead of print
Early online date12/03/24
<mark>Original language</mark>English


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.