计算机应用研究2024，Vol.41Issue(7)：1971-1976,6.DOI:10.19734/j.issn.1001-3695.2023.11.0546
基于多智能体深度强化学习的车联网可信任务卸载策略
Strategy on trusted task offloading for Internet of Vehicles based on multi-agent deep reinforcement learning
摘要
Abstract
Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed,this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles,and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility,so as to help the terminal devices select reliable edge nodes for task offloading.At the same time,this paper modeled the offloading strategy as the time delay and energy con-sumption minimization problem under the reputation constraint,and used the multi-agent deep deterministic policy gradient al-gorithm to solve the approximate optimal solution of the NP-hard problem.The edge server received rewards based on the com-pletion of task offloading,and then updated the reputation recorded on the blockchain.Simulation experiments show that the proposed algorithm reduces in terms of time delay and energy consumption by 25.58％to 27.44％compared with the bench-mark testing schemes.关键词
车联网/边缘计算/区块链/深度强化学习/任务卸载Key words
Internet of Vehicles/edge computing/blockchain/deep reinforcement learning/task offloading分类
信息技术与安全科学引用本文复制引用
王亚丽,娄世豪..基于多智能体深度强化学习的车联网可信任务卸载策略[J].计算机应用研究,2024,41(7):1971-1976,6.基金项目
国家自然科学基金资助项目(62072159) （62072159）
河南省科技攻关资助项目(222102210011,232102211061) （222102210011,232102211061）
