摘要
Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures,the Intelligent Transportation System(ITS)has evolved as a promising paradigm for improving safety,efficiency of the transportation system.However,the strict delay requirement of the safety-related applications is still a great challenge for the ITS,especially in dense traffic environment.In this paper,we introduce the metric called Perception-Reaction Time(PRT),which reflects the time consumption of safety-related applications and is closely related to road efficiency and security.With the integration of the incorporating information-centric networking technology and the fog virtualization approach,we propose a novel fog resource scheduling mechanism to minimize the PRT.Furthermore,we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme.Numerical results demonstrate that our proposed schemes is able to reduce about 70%of the RPT compared with the traditional approach.
Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures, the Intelligent Transportation System(ITS) has evolved as a promising paradigm for improving safety, efficiency of the transportation system. However, the strict delay requirement of the safety-related applications is still a great challenge for the ITS, especially in dense traffic environment. In this paper, we introduce the metric called Perception-Reaction Time(PRT), which reflects the time consumption of safety-related applications and is closely related to road efficiency and security. With the integration of the incorporating information-centric networking technology and the fog virtualization approach, we propose a novel fog resource scheduling mechanism to minimize the PRT. Furthermore, we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme. Numerical results demonstrate that our proposed schemes is able to reduce about 70% of the RPT compared with the traditional approach.
基金
supported by National Key R&D Program of China(No.2018YFE010267)
the Science and Technology Program of Sichuan Province,China(No.2019YFH0007)
the National Natural Science Foundation of China(No.61601083)
the Xi’an Key Laboratory of Mobile Edge Computing and Security(No.201805052-ZD-3CG36)
the EU H2020 Project COSAFE(MSCA-RISE-2018-824019)
作者简介
Xiaosha Chen,received the B.Sc.degree in information and communication engineering from University of Electronic Science and Technology of China,Chengdu,China,in 2016.He is currently working toward the Ph.D.degree in University of Electronic Science and Technology of China.His research interests include handling routing protocols,performance analysis and congestion control in vehicular network;corresponding author:Supeng Leng,email:spleng@uestc.edu.cn,is a Full Professor and a Vice Dean in the School of Information&Communication Engineering,University of Electronic Science and Technology of China(UESTC).He is also the leader of the research group of Ubiquitous Wireless Networks.He received his Ph.D.degree from Nanyang Technological University(NTU),Singapore.He has been working as a Research Fellow in the Network Technology Research Center,NTU.His research focuses on resource,spectrum,energy,routing and networking in Internet of Things,vehicular networks,broadband wireless access networks,smart grid,and the next generation mobile networks.He published over 180 research papers in recent years.He serves as an organizing committee chair and TPC member for many international conferences,as well as a reviewer for over 10 international research journals;Ke Zhang,received the Ph.D.degree from University of Electronic Science and Technology of China in 2017.He is currently a Lecturer with the School of Information and Communication Engineering,University of Electronic Science and Technology of China.His research interests include scheduling of mobile edge computing,design and optimization of next-generation wireless networks,smart grid,and the Internet of Things;Kai Xiong,is a Ph.D.degree candidate in the School of Communication and Information Engineering,University of Electronic Science and Technology of China,Chengdu.His research interests include design and optimization of next-generation wireless networks,Internet of Vehicles,Machine Learning,Mobile Edge Computing,and Communication Theory.
