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Supervisor of Master's Candidates

School/Department:School of computer science and information egineering

Administrative Position:党支部书记

Education Level:With Certificate of Graduation for Doctorate Study


Degree:Doctoral degree


Alma Mater:Hefei University of Technology

Discipline:Computer Applications Technology




Education Level:With Certificate of Graduation for Doctorate Study

Alma Mater:Hefei University of Technology

Paper Publications

An RSU-crossed dependent task offloading scheme for vehicular edge computing based on deep reinforcement learning

DOI number:10.1504/IJSNET.2023.130711
Key Words:task oloading; vehicular edge computing; dependent task; deep reinforcement learning
Abstract: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.
Co-author:Jianing Shi,Benhong Zhang,Zengwei Lyu,Lingjie Huang
First Author:Xiang Bi
Indexed by:Journal paper
Document Type:J
Page Number:244-256
ISSN No.:1748-1279
Translation or Not:no
Date of Publication:2023-05-01
Included Journals:SCI