Queen Mary University of London, UK
is Professor of Wireless Communications and
Head of the Communication Systems Research
(CSR) group in the School of Electronic
Engineering and Computer Science at Queen
Mary University of London since September
2017. He was with the Department of
Informatics at King’s College London from
December 2007 to August 2017, where he was
Professor of Wireless Communications from
April 2013 to August 2017 and a Visiting
Professor from September 2017. He was an
Assistant Professor in the Department of
Electrical and Computer Engineering,
National University of Singapore from August
2000 to December 2007.
His research interests include Artificial Intelligence for Wireless Systems, 5G and beyond Wireless Networks, Internet of Things (IoT) and Molecular Communications. He published nearly 500 technical papers (including more than 200 top IEEE journal papers) in scientific journals and international conferences. He is a co-recipient of the Best Paper Awards presented at the IEEE International Conference on Communications 2016 (ICC’2016), IEEE Global Communications Conference 2017 (GLOBECOM’2017) and IEEE Vehicular Technology Conference 2017 (VTC’2017). He is an Editor for IEEE Transactions on Communications and a Senior Editor for IEEE Wireless Communications Letters. He was an Editor for IEEE Transactions on Wireless Communications (2006-2011), IEEE Transactions on Vehicular Technology (2006-2017) and IEEE Signal Processing Letters. He served as the Chair for the Signal Processing and Communication Electronics Technical Committee of IEEE Communications Society and Technical Program Chair and member of Technical Program Committees in numerous IEEE conferences. He received the IEEE Communications Society SPCE outstanding service award 2012 and IEEE Communications Society RCC outstanding service award 2014. He has been selected as a Web of Science (ISI) Highly Cited Researcher in 2016. He is an IEEE Fellow and IEEE Distinguished Lecturer.
Speech Title: When Machine Learning Meets Massive IoT: A Non-orthogonal Multiple Access Perspective
Abstract: With the emergence of the massive Internet of things (IoT), billions of physical and virtual devices are expected to be connected via wireless networks. The exponential growth of IoT devices has greatly reduced the performance of current wireless technologies. One of the main challenges to the next generation cellular network is providing massive connectivity. The traditional multiple access schemes utilise orthogonal time/frequency resources to support different users, which results in ineffective spectral efficiency and connectivity. To solve this problem, non-orthogonal multiple access (NOMA) is a hopeful approach that can be integrated into the existing random access schemes to enhance the spectral efficiency. However, optimising the resource allocation in NOMA-enabled massive IoT is challenging because of dynamic environments and high temporal correlation. For this NP-hard problem, model-free machine learning (ML) methods become an efficient substitute to conventional model-driven approaches. One additional bonus is that as ML-based algorithms preserve learning experience, these algorithms can be readily transferred to other massive IoT scenarios with the similar environment. In order to reduce the signal overhead and access latency, a transmit power pool for uplink grant-free (GF) NOMA users is created for offering an open-loop power control. In this talk, machine learning approaches in semi-GF NOMA IoT system will be presented. Promising research directions and possible ML solutions will also be discussed.
University of Sydney, Australia
Teng Joon (T. J.) Lim (S’92-M’95-SM’02-F’17) obtained the
B.Eng. degree in Electrical Engineering with
first-class honours from the National
University of Singapore (NUS) in 1992, and
the Ph.D. degree from the University of
Cambridge in 1996. From September 1995 to
November 2000, he was a researcher at the
Centre for Wireless Communications in
Singapore, one of the predecessors of the
Institute for Infocomm Research (I2R). From
December 2000 to May 2011, he was Assistant
Professor, Associate Professor, then
Professor at the University of Toronto’s
Edward S. Rogers Sr. Department of
Electrical and Computer Engineering. Since
June 2011, he has been a Professor at the
Electrical & Computer Engineering Department
of NUS, where he served as a Deputy Head
from July 2014 to August 2015. From
September 2015 through December 2019, he
served as Vice-Dean (Graduate Programs) in
the NUS Faculty of Engineering. Since
January 2020, he has served as the Associate
Dean (Education) at the Faculty of
Engineering in the University of Sydney.
Professor Lim was an Area Editor of the IEEE Transactions on Wireless Communications from September 2013 to September 2018, and previously served as an Associate Editor for the same journal. He has also served as an Associate Editor for IEEE Wireless Communications Letters, Wiley Transactions on Emerging Telecommunications Technologies (ETT), IEEE Signal Processing Letters and IEEE Transactions on Vehicular Technology. He has volunteered on the organizing committee of a number of IEEE conferences, including serving as the TPC co-chair of IEEE Globecom 2017. He chaired the Singapore chapter of the IEEE Communications Society in 2017 and 2018, and is a Distinguished Lecturer of the IEEE Vehicular Technology Society for 2019-20.
His research interests span many topics within wireless communications, including cyber-security in the Internet of Things, heterogeneous networks, cooperative transmission, energy-optimized communication networks, multi-carrier modulation, MIMO, cooperative diversity, cognitive radio, and stochastic geometry for wireless networks, and he has published widely in these areas.
Speech Title: Data Tampering in Vehicular Networks
Abstract: Vehicular networks that enable communication between vehicles and fixed infrastructure have applications ranging from road safety to entertainment but are vulnerable to data tampering attacks, realized through compromise of roadside units (RSUs). The detection mechanisms for data tampering at the PHY layer and at higher layers need to be different – PHY data tampering mimics a bad radio channel, while higher layer data tampering impairs message integrity. In this talk, we will discuss recent work in detection of data tampering at both levels. For PHY layer attacks, we devised a trust-based detection system to identify the malicious RSU, with trust values generated through maximum likelihood estimation by individual vehicles with respect to each RSU they interact with. Trust values from all vehicles are aggregated centrally to derive an overall trust value for each RSU, which are then compared against a threshold to decide whether an RSU is compromised. For MAC and higher layer attacks, we discuss an approach to validate un-encrypted packets transmitted and received by the vehicles via their RSUs. The proposed detection system identifies any RSU traying to modify data, selectively forward a packet and/or inject malicious packets. In both cases, we also consider the possibility of a small fraction of the vehicles colluding with the RSUs to deceive the detection system.
Hong Kong University of Science and Technology, China
Dr. Xiaofang Zhou
is the Otto Poon Professor of Engineering
and Chair Professor of Computer Science and
Engineering at the Hong Kong University of
Science and Technology. From 2004 to 2020,
he was a Professor of Computer Science at
the University of Queensland, leading its
Data and Knowledge Engineering (DKE)
research group and Data Science Discipline.
He has been working in the area of
spatiotemporal databases, data mining and
machine learning, data quality management,
big data analytics, and data science. He was
a Program Committee Chair of IEEE
International Conference on Data Engineering
(ICDE 2013), ACM International Conference on
Information and Knowledge Management (CIKM
2016), and International Conference on Very
Large Databases (PVLDB 2020). He was the
Chair of IEEE Technical Committee on Data
Engineering from 2015-2018. Professor Zhou
is a Fellow of IEEE.
Speech Title: From Spatial Databases to Spatial Data Science: the Problems and Approaches
Abstract: Location and time are two ubiquitous data types of big data, but none of them can be natively supported by many existing database management technologies such as relational database management systems. Significant research and development progresses have been made in the past three decades to extend database technologies for scalable spatiotemporal data management and analytics. In this talk, we will define the concept of spatial data science and review the past and current research activities under this new framework. Key components that define spatial data science as a discipline will be identified, and new research opportunities will be discussed to bridge the gap between traditional database research and the data science approach for spatiotemporal data analytics.