Open Access

ARTICLE

A Task Offloading Method for Vehicular Edge Computing Based on Reputation Assessment

Jun Li^1,*, Yawei Dong¹, Liang Ni¹, Guopeng Feng¹, Fangfang Shan^1,2

1 School of Computer Science, Zhongyuan University of Technology, Zhengzhou, 450007, China
2 Henan Key Laboratory of Cyberspace Situation Awareness, Zhengzhou, 450007, China

* Corresponding Author: Jun Li. Email:

Computers, Materials & Continua 2025, 83(2), 3537-3552. https://doi.org/10.32604/cmc.2025.059325

Received 04 October 2024; Accepted 12 February 2025; Issue published 16 April 2025

Abstract

With the development of vehicle networks and the construction of roadside units, Vehicular Ad Hoc Networks (VANETs) are increasingly promoting cooperative computing patterns among vehicles. Vehicular edge computing (VEC) offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure, thereby reducing the computational burden on connected vehicles. However, this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes. Existing vehicular edge computing platforms have not adequately considered the misbehavior of vehicles. We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment. This approach integrates deep reinforcement learning and reputation management to address task offloading challenges. Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method.

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Keywords

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