Di Zhang
Assistant Professor, School of Information Engineering, Zhengzhou University; National Telemedicine Center, China
Title: 5GB e-Health Networks and Related Services, from Theory to Reality
Abstract: Information and communication technology (ICT) for health has been widely discussed. However, due to the limited number of connected devices, slow transmission speed, low communication reliability and higher latency drawbacks of prior cellular communications generations, e-health was not widely adopted. The ongoing development of 5G is accelerating the practical use of e-health networks and related services. Stunning 5G envisions are the ultra-reliable low latency communications (URLLC), enhanced mobile broadband (eMMB) and massive machine type communications (mMTC), which fit the requirements of e-health networks and related services perfectly. However, due to the technology limitations of 5G’s current version, more endeavors are still needed, especially for the URLLC. Besides the technical limitations, ethics and privacy information protection are also challenging the deployments of 5G and beyond technologies (5GB) e-health networks and related services. Motivated by the above, in this tutorial, we cover the related technology solutions, user cases, future research trends and challenges for the 5GB e-health networks and related services. Ultimately, the intent of this tutorial is to provide an in-depth exposition of 5GB research and to serve as a basis for stimulating more out-of-the-box research about the 5GB e-health networks and services.
Bio: Di Zhang currently is an Assistant Professor at Zhengzhou University, China, he is also an adjunct technician with the National Telemedicine Center, National Engineering Laboratory for Internet Medicine Systems and Applications. He has served as a temporary Deputy Team Leader of Henan Provincial Big Data Administration. He received his Ph.D. degree from Waseda University, Japan (2013-2017). He was a visiting Senior Researcher with Seoul National University, Seoul, South Korea, and a visiting student with National Chung Hsing University, Taiwan. Dr. Zhang has engaged in two international projects in wireless communications and networking co-funded by the EU FP-7, EU Horizon 2020, Japanese Monbushou and NICT. He is serving as the editor of the IEEE ACCESS, KSII Transactions on Internet and Information Systems, IET Quantum Communication. He has served as the guest editor of IEEE Wireless Communications, IEEE NETWORK, IEEE ACCESS, etc.; Chair of IEEE WCNC 2020, IEEE/CIC ICCC 2020, IEEE/CIC ICCC 2019, etc.; and TPC member of various IEEE flagship conferences, such as ICC, GlobalSIP, WCNC, VTC, CCNC, HEALTHCOM. In 2019, he received the ITU Young Author Recognition Award and the IEEE Outstanding Leadership Award. His research interests include wireless communications, signal processing, Internet of things and e-health.
Liang Zhao
Associate Professor, Shenyang Aerospace University, China
Title: Intelligence-Empowered Vehicular Networking
Abstract: Vehicular networks (VNs) have been studied profoundly aiming to provide the efficient connectivity among vehicles and infrastructures to access to various of applications in which such networks can support all types of services in the internet of vehicles (IoV). Over the past two decades, VANET (vehicular ad-hoc network) has been studied to connect the vehicles in wide areas with its multi-hop connectivity. However, traditional VANET still faces challenges to enable intelligent networking and communication with its decentralized nature in which individual vehicle lacks the ability to collect and compute such large amount of data. Hence, learning algorithms and dedicated networking architecture should be applied to improve the networking quality. In this talk, the speaker will present the AI-enabled vehicular networking techniques and the related architectures, in the aspects of routing metrics, protocol switching, adaptive routing, softwarized VN, and digital twin-based VN.
Biography: Liang Zhao is an associate professor at Shenyang Aerospace University, China. He received his Ph.D. degree from the School of Computing at Edinburgh Napier University in 2011. Before joining Shenyang Aerospace University, he worked as associate senior researcher in Hitachi (China) Research and Development Corporation from 2012 to 2014. His research interests include ITS, VANET, WMN and SDN. He has published more than 80 papers, including IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Mobile Computing, Computer Network, IEEE Transactions on Fuzzy Systems, IEEE NETWORK, IEEE Transactions on Wireless Communications, IEEE ITS Magazine, IEEE ICC, IEEE IPDPS, and so forth. He served as the Chair of several international conferences and workshops, including 2019 IEEE IUCC (Program Co-Chair), 2019 IEEE Scalcom (Poster/Demo Co-Chair), IoT-Smart-2015 (Program Co-Chair), 2019 AI-driven Network Workshop (Program Co-Chair), and NGDN workshop (founder). He is/has been a guest editor of IEEE Transactions on Network Science and Engineering, Springer Journal of Computing, and Wiley Internet Technology Letters. He was the recipient of the Best Paper Awards at IEEE IUCC 2015 and ACM MOMM 2013.
Xianfu Chen
Senior Scientist, VTT Technical Research Centre of Finland, Finland
Title: Machine Learning Empowered Multi-Access Edge Computing in Beyond 5G Networks
Abstract:
With the proliferation of smart devices, more and more mobile applications, such as location-based virtual/augmented reality and online gaming, are emerging and gaining popularity. To improve the computation qualities of service and experience, multi-access edge computing (MEC) is envisioned as a promising paradigm by providing computing capabilities in close proximity to mobile users (MUs). In addition to local processing, a resource-constrained MU can also offload the computation to resource-rich MEC servers for remote execution. Beyond 5G networks are expected to enhance the 5G capabilities towards the support of seamless wireless connectivity. The trend of merging wireless communications and MEC motivates the investigation of computation offloading in beyond 5G networks. Nevertheless, the design of computation offloading policies remains challenging due to the environmental uncertainties and the limited resource sharing. In this talk, we adopt a multi-agent Markov decision process to formulate the computation offloading problems, for which a distributed learning framework is proposed. We examine the potentials of the proposed distributed learning framework through use case studies.
Bio:
Xianfu Chen received his Ph.D. degree with honors in Signal and Information Processing, from the Department of Information Science and Electronic Engineering (ISEE) at Zhejiang University, Hangzhou, China, in March 2012. Since April 2012, Dr. Chen has been with the VTT Technical Research Centre of Finland, Oulu, Finland, where he is currently a Senior Scientist. He was a visiting scholar at the Wireless Networking, Signal Processing and Security Lab, University of Houston, USA, from March to April 2016, and the Department of Computer and Network Engineering, University of Electro-Communications, Japan, from June to August 2017. His research interests cover various aspects of wireless communications and networking, with emphasis on human-level and artificial intelligence for resource awareness in next-generation communication networks. Dr. Chen serves as an Associate Editor of China Communications and served as a Member of the First Editorial Board of Journal of Communications and Information Networks. Dr. Chen is serving and served as a Track Co-Chair and a TPC member for a number of IEEE ComSoc flagship conferences. He is a Vice Chair of IEEE Special Interest Group on Big Data with Computational Intelligence, the members of which come from over 15 countries worldwide.
Zhenyu Zhou
Professor, School of Electrical and Electronic Engineering, North China Electric Power University, China
Title: Researches on Technologies of Ubiquitous Intelligent Power Internet of Things
Abstract:
Through the integration of communication, sensing, automation, cloud computing and other technologies, the Power Internet of Things can realize the safe and reliable transmission, real-time processing and judgment of the information of power generation, transmission, transformation, distribution and consumption. It comprehensively improves the intelligence level of the power grid, and realizes the panoramic and holographic perception, interconnection, and seamless integration of power grid operation links. The arrival of 5G can accelerate the ubiquitous intelligent pace of power Internet of Things from the aspects of interconnection, precise control, massive data processing and broadband communication. This report explores the development background, application scenarios, key technologies and challenges of the ubiquitous intelligent Power Internet of Things.
Bio:
Zhenyu Zhou is a full professor and Ph.D supervisor at School of Electrical and Electronic Engineering, North China Electric Power University, China. His research interests mainly focus on wireless resource allocation and access control in Power Internet of Things. He was the recipient of the Ministry of Education of the People’s Republic of China Science and Technology Second Prize, the IEEE Communications Society Asia-Pacific Board Outstanding Young Researcher 2019, IEEE ComSoc Communications Systems Integration and Modeling (CSIM) Technical Committee 2019 Best Paper Award, IEEE International Wireless Communications and Mobile Computing Conference (IWCMC) 2019 Best Paper Award, the IEEE ComSoc Green Communications and Computing Technical Committee 2018 Best Paper Award, the IEEE Globecom 2018 Best Paper Award, the IET Premium Award in 2017, and the IEEE ComSoc Green Communications and Computing Technical Committee 2017 Best Paper Award. He served as an Associate Editor for IEEE Internet of Things Journal, IET Quantum Communication, IEEE Access, EURASIP Journal on Wireless Communications and Networking. He also served as the TPC Member for numerous international conferences such as IEEE Globecom, IEEE ICC, IEEE CCNC, IEEE APCC, IEEE VTC, IEEE PIMRC, etc. He is a voting member of IEEE Standard Association P1932.1 Working Group “Licensed/Unlicensed Spectrum Interoperability in Wireless Mobile Networks”. He is a senior member of IEEE, Chinese Institute of Electronics (CIE), and China Institute of Communications (CIC).
Ryohei Banno
Assistant Professor in Kogakuin University, Tokyo, Japan
Title: Various Tradeoffs in IoT Systems and Edge Computing
Abstract:
Internet of Things (IoT) has attracted much interest from both the academic and industry communities since the early 2010s. While many sorts of advanced IoT devices appeared, developing large scale IoT services still remains a challenge, where information sharing among devices over a wide area is required. In this presentation, we focus on IoT messaging techniques which are the basis for such large-scale information exchange. We clarify various conflicts of requirements that occurred in the large-scale information exchange and introduce our research activities to address those tradeoffs.
Bio:
Ryohei Banno received the Bachelor of Engineering degree and Master of Information Science and Technology degree from Hokkaido University, Sapporo, Japan, in 2010 and 2012 respectively, and the Ph.D. degree in science from Tokyo Institute of Technology, Tokyo, Japan, in 2018. From 2012 to 2018, he was a Researcher in NTT Network Innovation Laboratories. From 2018 to 2020, he was a Researcher in Tokyo Institute of Technology. Since 2020, he has been an Assistant Professor in Kogakuin University, Tokyo, Japan. His research interests include distributed systems and Internet of Things (IoT). Dr. Banno’s awards and honors include the Outstanding Paper Award from Information Processing Society of Japan (IPSJ) in 2015, Inoue Research Award for Young Scientist from Inoue Foundation for Science in 2020, and Funai Research Award from Funai Foundation for Information Technology in 2020.