个人信息Personal information
- 硕士生导师
- 教师拼音名称：bx
- 所在单位：计算机科学与技术系
- 职务：党支部书记
- 学历：博士研究生毕业
- 性别：男
- 学位：博士学位
- 职称：副教授
- 在职信息：在职
- 毕业院校：合肥工业大学
- 所属院系：计算机与信息学院（人工智能学院）
- 学科：计算机应用技术
论文成果
An RSU-crossed dependent task offloading scheme for vehicular edge computing based on deep reinforcement learning
发布时间：2023-12-05 点击次数：
DOI码：10.1504/IJSNET.2023.130711
发表刊物：INTERNATIONAL JOURNAL OF SENSOR NETWORKS
关键字：task oloading; vehicular edge computing; dependent task; deep reinforcement learning
摘要：Various interdependent and computationally intensive on-vehicle tasks have posed great pressure on the computing power of vehicles. Vehicular edge computing (VEC) is considered to be a promising paradigm to solve this problem. However, due to the high mobility, vehicles will pass through multiple road-side units (RSUs) during task computing. How to coordinate the oloading decision of RSUs is a challenge. In this study, we ropose a dependent task oloading scheme by considering vehicle mobility, service availability, and task priority. Meanwhile, to coordinate the oloading decisions among the RSUs, a Markov decision process (MDP) is carefully designed, in which the action of each RSU is divided into three steps to decide whether, where, and how each task is oloaded separately. Then, an advanced DDPG-based deep reinforcement learning (DRL) algorithm is adopted to solve this problem. Simulation results show that the proposed scheme has better performance in reducing task processing latency and consumption.
合写作者：Jianing Shi,Benhong Zhang,Zengwei Lyu,Lingjie Huang
第一作者：Xiang Bi
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：41
期号：4
页面范围：244-256
ISSN号：1748-1279
是否译文：否
发表时间：2023-05-01
收录刊物：SCI