Abstract:

With the ubiquitous deployment of Wi-Fi infrastructure in homes, office buildings and public spaces, WiFi-based contactless sensing has become an ideal way for long-term human and environment monitoring. In this talk, I will introduce the Fresnel zone model as a new theoretic basis for device-free and contactless human sensing with Wi-Fi signals. The Fresnel-zone based theory not only reveals the relationship among the WiFi CSI signal, the distance between two WiFi transceivers, the sensing target’s relative location and orientation with respect to WiFi transceivers, and the environment, but also sheds light on the sensing limit of Wi-Fi devices. Building on the Fresnel Zone Model and the frequency diversity of WiFi signals, millimeter-scale human activity sensing could be achieved. By exploiting MIMO technology, we further propose a CSI Ratio model to increase the signal to noise ratio and push the range limit of WiFi-based device-free sensing. I will use human respiration detection and other application examples to demonstrate the power of the proposed theory and techniques.

Biography:

Daqing Zhang has been a professor with Peking University/Telecom SudParis, France. Dr Zhang’s research interests include context-aware computing, mobile computing, big data analytics and Internet of Things. He has published more than 300 technical papers in leading conferences and journals, where his work on context model and wireless sensing model is widely accepted by pervasive computing, mobile computing and service computing communities. His research work got over 21,400 citations with an H-index of 72 (according to Google Scholar). He is the winner of the Ten Years CoMoRea Impact Paper Award at IEEE PerCom 2013 and Ten Years Influential Paper Award at IEEE UIC 2019, the 2021 ACM IMWUT Distinguished Paper Award, the Honorable Mention Award at ACM UbiComp 2015 and 2016, etc.. He served as the general or program chair for over 20 international conferences, giving 20+ keynote talks in various international events. He is in the editorial board of IEEE Pervasive Computing, ACM TIST, and ACM IMWUT. Daqing Zhang is a Fellow of IEEE and Member of Academy of Europe, he obtained his Ph.D. from University of Rome "La Sapienza", Italy in 1996.

Abstract:

Cloud computing has gradually evolved into an infrastructural tool for many scientific and business applications with intensive data or computing requirements. One of the challenges in cloud computing now is how to run software efficiently on cloud platforms since lots of classic sequential codes are not ready to be executed in parallel in cloud environments, resulting in long execution time and low efficiency. It is also costly and labor intensive to redesign and convert current sequential codes into cloud codes running on cloud programming models such as MapReduce or Spark. Thus, automatic translation from sequential codes to cloud codes is one of the directions that could resolve the problem of slow code migration from traditional computing platforms to cloud infrastructures. In this talk, I will present several automatic translators (M2M, J2M and J2S) for cloud programming models MapReduce and Spark. I will provide details of the design of our translators and their performance results based on many experiments. Performance comparisons between hand coded cloud programs and automatically translated codes will also be carried out. Semi-automatic translation with human tuning will also be introduced. Our experimental results indicate that the translators we have designed not only can precisely translate the sequential codes such as MATLAB codes or Java codes into cloud codes, but also can achieve almost a linear speedup in performance if the data sizes in the applications are huge enough. Since cloud computing is used for big data anyway, this demonstrates that automatic translation to reduce labor costs is a possible effective way to go for cloud programming. In addition, the limitations and shortcomings of our automatic translation and semi-automatic translation will be identified and future directions in this area will be provided.

Biography:

Dr. Yi Pan is currently a Chair Professor and the Dean of the College of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technologies, Chinese Academy of Sciences and Regents’ Professor Emeritus at Georgia State University. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. Dr. Pan has published more than 450 papers including over 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals. In addition, he has edited/authored 43 books. His work has been cited more than 15000 times based on Google Scholar and his current h-index is 78. Dr. Pan has served as an editor-in-chief or editorial board member for 20 journals including 7 IEEE Transactions. Currently, he is serving as an Associate Editor-in-Chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics. He is the recipient of many awards including one IEEE Transactions Best Paper Award, five IEEE and other international conference or journal Best Paper Awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, IEEE Outstanding Leadership Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized numerous international conferences and delivered keynote speeches at over 60 international conferences around the world.

Abstract:

In this talk, I will introduce the network layers with their work and focus on how the AI components will be embeeded into the layers. In particular, I will talk about the smart edge networks and their resource allocation which are crucial for the success of applications in smart city and industry.

Biography:

Weijia Jia is currently a Chair Professor and Director of Joint AI and Future Networking Research Institute of Beijing Normal University (BNU, Zhuhai) and United International College (UIC), Zhuhai, Guangdong, China. He also serves as the VP for Research at UIC and Zhiyuan Chair Professor at Shanghai Jiaotong University, China. Prior joing BNU-UIC, he served as the Deputy Director of State Kay Laboratory of Internet of Things for Smart City at the University of Macau. He received BSc/MSc from Center South University, China in 82/84 and Master of Applied Sci./PhD from Polytechnic Faculty of Mons, Belgium in 92/93, respectively, all in computer science. For 93-95, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine) as a research fellow. From 95-13, he worked in City University of Hong Kong as a professor. His contributions have been reconganized as optimal network routing and deployment; vertex cover; anycast and QoS routing, and sensors networking; knowledge relation extractions; NLP and intelligent edge computing. He has over 600 publications in the prestige international journals/conferences and research books and book chapters. He has received the best product awards from the International Science & Tech. Expo (Shenzhen) in 2011/2012 and the 1st Prize of Scientific Research Awards from the Ministry of Education of China in 2017 (list 2) and many provincial science and tech awards. He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many top international conferences. He is the Fellow of IEEE and the Distinguished Member of CCF.

Abstract:

In this talk, we mainly introduce our recent studies on Digital Twin (DT) for edge computing, 6G, Internet of Vehicles, and IoT. We will first introduce the main concepts and challenges related to Digital Twin. Then, we present a novel scenario DITEN (Digital Twin Edge Networks) and the research challenges. Throughout the talk, we join DT with machine learning to add intelligence (e.g., deep reinforcement learning, federated learning) for low-latency, privacy-preservation, and energy-efficiency.

Biography:

Yan Zhang is currently a Full Professor with the Department of Informatics, University of Oslo, Norway. He received the Ph.D. degree from the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore. He received M.S. and B.S from Beihang University and Nanjing University of Post and Telecommunications, respectively. His research interests include next-generation wireless networks leading to 6G, green and secure cyber-physical systems (e.g., smart grid and transport). Dr. Zhang is an Editor (or Area Editor, Senior Editor, Associate Editor) for several IEEE transactions/magazine, including IEEE Network Magazine, IEEE Transactions on Green Communications and Networking, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Vehicular Technology, IEEE Transactions on Industrial Informatics, IEEE Communications Survey and Tutorials, IEEE Internet of Things Journal, IEEE Systems Journal, IEEE Vehicular Technology Magazine, and IEEE Blockchain Technical Briefs. He is a symposium/track chair in a number of conferences, including IEEE ICC 2021, IEEE SmartGridComm 2021, and IEEE Globecom 2017. He is the Chair of IEEE Communications Society Technical Committee on Green Communications and Computing (TCGCC). He is an IEEE Communications Society Distinguished Lecturer and IEEE Vehicular Technology Society Distinguished Speaker. He was an IEEE Vehicular Technology Society Distinguished Lecturer during 2016-2020. Since 2018, Prof. Zhang was a recipient of the global “Highly Cited Researcher” Award (Web of Science top 1% most cited worldwide). He is Fellow of IEEE, Fellow of IET, elected member of Academia Europaea (MAE), elected member of the Royal Norwegian Society of Sciences and Letters (DKNVS), and elected member of Norwegian Academy of Technological Sciences (NTVA).

Abstract:

This talk will review the emergence and development of industrial internet, which is revolutionizing the industry and our life. After discussing the industrial internet architecture, we will reveal the inevitable underlying vulnerabilities via analyzing the specific features. We will get back to the fundamental control layer of industrial internet, and explain its importance of converging security and safety via concrete examples. We will demonstrate how various core control devices and protocols are insecure through the most recent research.

Biography:

Peng Cheng serves as the Professor and Associate Dean of College of Control Science and Engineering, Zhejiang University. His research interests include control system security, cyber-physical systems. He has received State Science and Technology Progress Award, MOE Natural Science Award and MOE Youth Science Award. He has been awarded 2020 Changjiang Scholars Chair Professor. He serves as Associate Editor of IEEE Transactions on Control of Network Systems, and vice director of CAA Committee for Elite Young Professionals.

Abstract:

Blockchain technology has promising prospects in various application fields. This talk first introduces the evolution of the blockchain from 1.0 to 4.0. Then, we discuss the current hot issues and key challenges such as storage optimization, security defense, and vulnerability detection. Then, we introduce the blockchain system developed by the group. Finally, the open problems in blockchain area will be discussed.

Biography:

Keqiu Li is a professor and dean of the College of Intelligence and Computing, Tianjin University, China. He is the recipient of National Science Foundation for Distinguished Young Scholars of China. He received his bachelor's and master's degrees from the Department of Applied Mathematics at the Dalian University of Technology in 1994 and 1997, respectively. He received the Ph.D. degree from the Graduate School of Information Science, Japan Advanced Institute of Science and Technology in 2005. His research topics include blockchain system, internet of things, and cloud computing.

Abstract:

Over the past a few years, research and development has made significant progresses on big data analytics with the supports from both governments and industries all over the world, such as Spark, IBM Watson and Google AlphaGo. A fundamental issue for big data analytics is the efficiency, and various advances towards attacking this issues have been achieved recently, from theory to algorithms to systems. In this talk, we shall present the idea of approximate computation for efficient and effective big data analytics: query approximation and data approximation, based on our recent research experiences. Different from existing approximation techniques, the approximation computation that we are going to introduce does not necessarily ask for theoretically guaranteed approximation solutions, but asks for sufficiently efficient and effective solutions in practice.

Biography:

Shuai Ma is a full professor in the School of Computer Science and Engineering, Beihang University, China. He obtained two PhD degrees: University of Edinburgh in 2010 and Peking University in 2004, respectively. His research interests include database theory and systems, and big data. He is a recipient of the best paper award of VLDB 2010, the best challenge paper award of WISE 2013, the National Science Fund of China for Outstanding Young Scholars in 2019, and the special award of Chinese Institute of Electronics for progress in science and technology in 2017 . He is/was an Associate Editor of VLDB Journal IEEE Transactions on Big Data and Knowledge and Information Systems.

Abstract:

Feedback mechanism has been widely used in wireless communication such as channel equalization and resource allocation. In recent years, deep learning (DL) has made great progress in the field of wireless communication. There is now some work that attempts to introduce plain feedback mechanisms into DL algorithm to solve wireless communication problems. However, the improvement of plain feedback DL methods is limited in complex situations due to those methods lack sufficient learning ability on feedback information. In this talk, we propose a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer, which consists of a specific learnable feedback agent and two feed-forward agents. Three fully cooperative intelligent agents help the system improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. We further formulate it into a three-player Stackelberg Game, which helps us to optimize and train this model more efficiently. To verify the feasibility of our proposed MAFENN system and the Stackelberg Game optimization, we conduct a series of experiments to compare the symbol error rate (SER) performance of the MAFENN equalizer and the other methods which utilizes quadrature phase-shift keying (QPSK) modulation scheme. Our performance outperforms that of the other equalizers at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.

Biography:

Yang Yang received the B.S. and M.S. degrees in Radio Engineering from Southeast University, Nanjing, China, in 1996 and 1999, respectively, and the PhD degree in Information Engineering from the Chinese University of Hong Kong in 2002. He is currently a full professor with School of Information Science and Technology, Master of Kedao College, and Director of Shanghai Institute of Fog Computing Technology (SHIFT), ShanghaiTech University, China. He is also an Adjunct Professor with the Research Center for Network Communication, Peng Cheng Laboratory, China, as well as a Senior Consultant for Shenzhen SmartCity Technology Development Group, China. Before joining ShanghaiTech University, he has held faculty positions at the Chinese University of Hong Kong, Brunel University, U.K., University College London (UCL), U.K., and SIMIT, CAS, China. Yang's research interests include 5G/6G, fog/edge computing networks, service-oriented collaborative intelligence, IoT applications, and advanced testbeds and experiments. He has published more than 300 papers and filed more than 80 technical patents in these research areas. He has been the Chair of the Steering Committee of Asia-Pacific Conference on Communications (APCC) since January 2019. Yang is a fellow of the IEEE.