Open Access

ARTICLE

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

Jianhua Liu^*, Jincheng Wei, Rongxin Luo, Guilin Yuan, Jiajia Liu, Xiaoguang Tu

Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan, 618307, China

* Corresponding Author: Jianhua Liu. Email:

(This article belongs to the Special Issue: Multi-Service and Resource Management in Intelligent Edge-Cloud Platform)

Computers, Materials & Continua 2024, 81(1), 1337-1361. https://doi.org/10.32604/cmc.2024.056286

Received 19 July 2024; Accepted 14 September 2024; Issue published 15 October 2024

Abstract

With the rapid advancement of Internet of Vehicles (IoV) technology, the demands for real-time navigation, advanced driver-assistance systems (ADAS), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and multimedia entertainment systems have made in-vehicle applications increasingly computing-intensive and delay-sensitive. These applications require significant computing resources, which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks, energy consumption, and cost constraints. To address this issue in IoV-based edge computing, particularly in scenarios where available computing resources in vehicles are scarce, a multi-master and multi-slave double-layer game model is proposed, which is based on task offloading and pricing strategies. The establishment of Nash equilibrium of the game is proven, and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium. Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing (MEC)-enabled IoV environments. Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility. Specifically, the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

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Keywords

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