摘要：Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost. ? 2007-2012 IEEE.

ISSN号：1932-8184

卷、期、页：卷17 期2：2500-2511

发表日期：2023-06-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：IEEE Systems Journal

参与作者：叶强,陈莹

通讯作者：万江源,吕博枫

第一作者：黄霁崴

论文类型：期刊论文

论文概要：黄霁崴,万江源,吕博枫,叶强,陈莹，Joint Computation Offloading and Resource Allocation for Edge-Cloud Collaboration in Internet of Vehicles via Deep Reinforcement Learning，IEEE Systems Journal，2023，卷17 期2：2500-2511

论文题目：Joint Computation Offloading and Resource Allocation for Edge-Cloud Collaboration in Internet of Vehicles via Deep Reinforcement Learning