@Article{cmc.2025.059325,
AUTHOR = {Jun Li, Yawei Dong, Liang Ni, Guopeng Feng, Fangfang Shan},
TITLE = {A Task Offloading Method for Vehicular Edge Computing Based on Reputation Assessment},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {2},
PAGES = {3537--3552},
URL = {http://www.techscience.com/cmc/v83n2/60519},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.059325}
}