Invited Speakers

Prof. Xingwei Wang, Northeastern University, China

王兴伟,教授,博士生导师,东北大学研究生院常务副院长,国家杰出青年科学基金获得者,国务院政府特殊津贴获得者,辽宁省智能互联网理论与应用重点实验室主任,辽宁杰出科技工作者,“兴辽英才计划”科技创新领军人才,教育部新世纪优秀人才,辽宁省优秀教师,辽宁省百千万人才工程“百人层次”,辽宁省优秀博士论文指导教师,沈阳市五一劳动奖章获得者。长期致力于互联网、云计算、网络空间安全等领域的科学研究与产品开发工作。主持国家重点研发计划、国家自然科学基金、工信部、教育部等科技项目30余项。先后获国家科技进步二等奖2项、教育部科技进步一等奖2项、中国通信学会科学技术一等奖、教育部技术发明二等奖、辽宁省技术发明二等奖、辽宁省教学成果一等奖等。在国内外著名学术期刊和会议上发表论文400余篇,SCI收录300余篇。出版学术著作10余部。取得国家发明专利授权50余项。领导的团队入选辽宁省高等学校创新团队。连续入选爱思唯尔中国高被引学者(2014以来)。

Xingwei Wang, Professor, PHD supervisor. He is the Executive Dean of Graduate School at Northeastern University, the director of Liaoning Provincial Key Laboratory of Intelligent Internet Theory and Application, winner of the National Science Fund for Distinguished Young Scholars, expert of the State Council Special Allowance, outstanding scientific and technological worker of Liaoning Province, the leading talent of scientific and technological innovation of Xing Liao Talent Program, outstanding talent of the Ministry of Education in the new century, outstanding teacher of Liaoning Province, the hundred people level of Hundred Million Talent Project in Liaoning Province, the excellent doctoral dissertation instructor of Liaoning Province, and the winner of Shenyang May Day Labor Medal. His research fields include Internet, cloud computing and cyberspace security. He has undertaken more than 30 major projects, such as the National Key Technology R&D Program, NSFC projects. He has won two Second Prizes of National Scientific and Technological Progress, two First Prizes of Scientific and Technological Progress of the Ministry of Education, the First Prize of Science and Technology of China Communication Society, the Second Prize of Technological Invention of the Ministry of Education, the Second Prize of Technological Invention of Liaoning Province, the First Prize of Teaching Achievements of Liaoning Province, etc. He has published more than 400 papers in journals and conferences, and ten books in CS domain. He has obtained more than 50 authorized invention patents. His team was selected into the Innovation Team of Universities in Liaoning Province. He has been selected into Elsevier's China's highly cited scholars list for seven consecutive years (from 2014 on).

 

 

Prof. Dong Yang, Beijing Jiaotong University, China

Dong Yang (Member, IEEE) received the B.S. degree from Central South University, Hunan, China, in 2003, and the Ph.D. degree in communications and information science from Beijing Jiaotong University, Beijing, China, in 2009.,From March 2009 to June 2010, he was a Postdoctoral Research Associate with Jönköping University, Jönköping, Sweden. In August 2010, he joined the School of Electronic and Information Engineering, Beijing Jiaotong University. Since 2017, he has been a Full Professor of communication engineering with Beijing Jiaotong University. His research interests include network architecture, wireless sensor networks, industrial networks, and Internet of Things

Title:AI-assisted Resource Scheduling for Next-generation Industrial IoT
Abstract: With the wide deployment of industrial Internet of Things (IoT), numerous devices are connected to the Internet, and voluminous industrial data is generated at the network edge, which needs to be transmitted and processed with multiple satisfied requirements (e.g., low-latency, high-reliability, and data privacy). In such a case, spectrum, computing, caching, and time resources should be efficiently allocated and utilized to support various industrial IoT applications. However, due to the high computation complexity and intolerable time cost, it is difficult to solve the resource scheduling problem via traditional optimization methods. How to make the resource scheduling more efficient and intelligent is still an open issue to be addressed. Very recently, advanced learning-based algorithms hold the potential to improve resource utilization and decision-making efficiency via learning network dynamics. Particularly, deep reinforcement learning (RL) approaches have been utilized to solve resource scheduling problems in industrial IoT. In this talk, we will present three our previous works to introduce the topic about how to leverage deep RL approaches to flexibly and intelligently schedule multi-dimensional resources for time-sensitive industrial IoT.

 

 

Assoc. Prof. Jie Gong, Sun Yat-sen University, China

Jie Gong received his B.S. and Ph.D. degrees in Department of Electronic Engineering in Tsinghua University, Beijing, China, in 2008 and 2013, respectively. From Jul. 2012 to Jan. 2013, he visited Institute of Digital Communications, University of Edinburgh, Edinburgh, UK. From Jul. 2013 to Oct. 2015, he worked as a postdoctoral scholar in Tsinghua University. He is currently an associate professor in School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. He is serving as an editor for IEEE Trans. Green Commun. Netw., IEEE/CIC ICCC 2022 Workshop Co-Chair and Publicity Co-chair for Workshop on Intelligent Computing and Caching at the Network Edge in IEEE WCNC since 2018. He was a co-recipient of the Best Paper Award from IEEE Communications Society Asia-Pacific Board in 2013, and the Best Paper Award of the 5th EAI International Conference on IoT as a Service in 2019. His research interests include Age of Information, reinforcement learning, mobile edge computing and green communications and networking.
龚杰,中山大学数据科学与计算机学院副教授,博士生导师。分别于2008年和2013年在清华大学获得学士和博士学位,期间于2012年访问英国爱丁堡大学。2013年至2015年在清华大学从事博士后研究工作,2015年加入中山大学工作至今,入选“广东特支计划”科技创新青年拔尖人才。主持了国家自然基金青年基金和面上项目、广东省面上项目、广州市科学研究专项,作为骨干参加了国家973项目、国家重点研发计划国际合作项目等。已在国内外期刊和会议上发表或录用论文80余篇,授权专利8项。担任学术期刊 IEEE Transactions on Green Communications and Networking 编辑、IEEE/CIC ICCC 2022 Workshop共主席、组织了IEEE WCNC边缘计算专题系列研讨会。获得了2013年第二届IEEE通信学会亚太杰出论文奖,2016年 IEEE Wireless Communications Letters 模范审稿人称号。

Title: Information Timeliness of Joint Transmission and Processing with Mobile Edge Computing
Abstract:
date scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time-consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this talk, we will investigate the AoI for computation-intensive messages with MEC, and consider how to jointly schedule data transmission and processing to minimize the average AoI.

 

 

Assoc. Prof. Liwei Yang, China Agricultural University, China (Personal Page)

LIWEI YANG, associate professor of China Agricultural University. She received the B.E. degree in Telecommunication Engineering from Chongqing University of Posts and Telecommunications, China, and the Ph.D. degree in Information and Communications Engineering from Beijing University of Posts and Telecommunications, China. From 2009 to 2011, she was a Postdoctoral Research Fellow with the Department of Electronic Engineering, Tsinghua University, China. In 2014, she joined the faculty of the College of Information and Electrical Engineering, China Agricultural University. Her research interests include optical networks, optical wireless communications and visible light communication. She participated in a number of national projects and published more than 80 papers. She served as a TPC member of several international academic conferences and a reviewer for several international journals.

Title: Mitigation of Multi-user Interference in MIMO-OFDM based Visible Light Communication Systems
Abstract:
Visible light communications (VLC) are a short-range wireless transmission technology and one of the candidate technologies for 6G. However, interference has a significant effect on system performance. This paper proposes an improved MMSE algorithm to mitigate the interference in MIMO-OFDM-based VLC systems. The performance is investigated in the presence of interference from other users. The simulation results indicate that the proposed interference mitigation technique is more reliable in multi-user systems.