December 13-15, 2021 · Exeter, U. K.https://ieee-msn.org/2021
Title: RFID and Backscatter Communications for Motion Capture and Fine Scale Localization
Instructor: Prof. Gregory D. Durgin,
Georgia Tech., USA
How do you capture the choreography of a ballerina’s performance? How does a drone navigate a vast, complex shipping yard to perform inventory? How do you condition a large-aperture antenna so that it is capable of beaming microwave power across long distances in space? In this tutorial, we answer these questions by exploring the emerging world of RFID-based motion capture and fine-scale localization. This tutorial first presents the fundamental barriers that wireless techniques experience in the drive for precise localization. We then survey the available techniques – from basic signal-strength mapping localization using off-the-shelf RFID tags to elegant, quantum-tunneling tags that are used to trace out the echoes of surrounding RF multipath – and quantify/rank performance. RFID and backscatter-based approaches are shown to have the most promise for realizing real-time, motion-capture-grade localization for wireless nodes.
Prof. Gregory D. Durgin joined the faculty of Georgia Tech's School of Electrical and Computer Engineering in Fall 2003 where he serves as a professor. He received the BSEE (96), MSEE (98), and PhD (00) degrees from Virginia Polytechnic Institute and State University. In 2001 he was awarded the Japanese Society for the Promotion of Science (JSPS) Post-doctoral Fellowship and spent one year as a visiting researcher with Morinaga Laboratory at Osaka University. He has received best paper awards for articles coauthored in the IEEE Transactions on Communications (1998 Stephen O. Rice prize), IEEE Microwave Magazine (2014), and IEEE RFID Conference (2016, 2018, 2019) as well as the 3rd place 2020 Nokia Bell Labs Prize. Prof. Durgin authored Space-Time Wireless Channels (2002), the first textbook in the field of space-time channel modeling which has influenced multiple generations of commercial cellular technologies. Prof. Durgin founded the Propagation Group (http://www.propagation.gatech.edu) at Georgia Tech, a research group that studies radiolocation, channel sounding, backscatter radio, RFID, and applied electromagnetics. He is a winner of the NSF CAREER award as well as numerous teaching awards, including the Class of 1940 Howard Ector Outstanding Classroom Teacher Award at Georgia Tech (2007). He has served on the editorial staff for IEEE RFID Virtual Journal, IEEE Transactions on Wireless Communications, and IEEE Journal on RFID. He also serves as Vice-President of Conferences for the IEEE Council of RFID. He is a frequent consultant to industry, having advised many multinational corporations on wireless technology.
Title: Federated Analytics: A New Collaborative Computing Paradigm towards Privacy Focusing World
Instructors: Prof. Dan Wang, Ms. Siping Shi,
The Hong Kong Polytechnic University, Hongkong
In this tutorial, we present federated analytics, a new distributed computing paradigm for data analytics applications with privacy concerns. Today’s edge-side applications generate massive data. In many applications, the edge devices and the data belong to diverse owners; thus data privacy has become a concern to these owners. Federated analytics is a newly proposed computing paradigm where raw data are kept local with local analytics and only the insights generated from local analytics are sent to a server for result aggregation. It differs from the federated learning paradigm in the sense that federated learning emphasizes on collaborative model training, whereas federated analytics emphasizes on drawing conclusions from data. This tutorial will be divided into three parts.
First, we will present the definition, taxonomy, application cases and architecture of the federated analytics paradigm. In particular, we present a federated video analytics framework which can be used for HD map construction using social vehicles with privacy concerns.
Second, we will present federated anomaly analytics to address the local model poisoning attack in current federated learning systems.
Third, we will present federated skewness analytics to address the data skewness problem in current federated learning systems.
Dan Wang' research falls in general computer networking and systems, where he published in ACM SIGCOMM, ACM SIGMETRICS and IEEE INFOCOM, and many others. He is the steering committee chair of IEEE/ACM IWQoS. He served as the TPC co-Chair of IEEE/ACM IWQoS 2020. His recent research focus on smart energy systems. He won the Best Paper Awards of ACM e-Energy 2018 and ACM Buildsys 2018. He has served as a TPC co-Chair of the ACM e-Energy 2020 and he will serve as General co-Chair of the ACM e-Energy 2022. He is a steering committee member of ACM e-Energy. His research has been adopted by industry, e.g., Henderson, Huawei, and IBM. He won the Global Innovation Award, TechConnect, in 2017. He got his B.Sc., M.Sc., Ph.D. from Peking University, Case Western reserve University and Simon Fraser University, all in Computer Science.
Siping Shi received her B.S. degree in computer science from Sichuan University in 2014, and her M.S. degree in computer applied technology from the University of Chinese Academy of Sciences in 2017. She is currently a Ph.D. candidate at The Hong Kong Polytechnic University. Her research interests include edge computing, federated learning and analytics.
Machine Learning Security and Privacy in Networking
Instructor: Prof. Yanjiao Chen,
Zhejiang University, China
Machine learning has gradually found its way into the networking area. Unfortunately, the vulnerability of machine learning models also infects the networking domain, raising alarming issues that may threaten the privacy and security of critical applications. In this tutorial, I will give a systematic introduction of typical attacks against machine learning models, including adversarial attacks, backdoor attacks, membership inference attacks, model extraction attacks, model inversion attacks and so on. The tutorial will cover a series of works on applying modern machine learning to networking and analyze the potential risk of current architectures of machine learning models and its impact on networking applications.
Prof. Yanjiao Chen received her B.E. degree in Electronic Engineering from Tsinghua University in 2010 and Ph.D. degree in Computer Science and Engineering from Hong Kong University of Science and Technology in 2015. She is currently a Bairen Researcher in the College of Electrical Engineering, Zhejiang University, China. Her research interests include ML security, AI in networking, and mobile sensing. Yanjiao has published papers in ACM CCS, IEEE INFOCOM, ICDCS, etc. Yanjiao has served on the editorial board of IEEE WCL and served as TPC member in IEEE INFOCOM, NDSS, ICNP, etc.
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