Virtual Seminar by Xinyu Zhang

Title: Printable Smart Surfaces for IoT Communication and Sensing

Time and Date: November 17, 2021, at 9:00 am ET

Presenter:  Professor  Xinyu Zhang, University of California San Diego, USA

Venue: (Password: 4Zn7xZ)

Abstract: Embedding sensing and communication capabilities seamlessly into ambient environment is a long-term aspiration of the Internet of Things (IoT). Smart surfaces, with conformable shape, thin form factors, and ease of fabrication, can potentially materialize the vision of intelligent and connected things. In this talk, I will present the design of passive, batteryless, chipless smart surfaces that facilitate IoT communication and sensing.  These surfaces can be fabricated through ordinary inkjet printing, PCB printing, or 3D printing.  They can help sensing the interaction between human users and everyday objects, thus enabling challenging use cases such as experience sampling and mobile VR interaction.  In addition, they can communicate with ordinary radio/radar devices, and boost the quality of existing wireless links. Realizing such capabilities involves non-trivial challenges, especially since the surfaces are fully passive and do not possess the computing/communication components in typical IoT devices.  This talk will introduce a set of solutions that span the areas of electromagnetics, wireless communications, and application-specific signal processing.  

Bio: Xinyu Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California San Diego. Prior to joining UC San Diego in 2017, he was an Assistant Professor at the University of Wisconsin-Madison. He received his Ph.D. degree from the University of Michigan in 2012. His research interest lies in wireless systems and ubiquitous computing, and more specifically in (i) designing next-generation wireless architectures and physical-layer informed protocols;  (ii) designing ubiquitous systems that leverage wireless signals to sense micro-locations and micro-activities at near-vision precision.  His research work has been regularly published in top conferences in these areas, especially ACM MobiCom, MobiSys, USENIX NSDI, and IEEE INFOCOM. He is the recipient of two ACM MobiCom Best Paper Awards (2011 and 2020), Communications of the ACM Research Highlight (2018), ACM SIGMOBILE Research Highlight (2018), NSF CAREER Award (2014), Google Research Award (2017, 2018, 2020), and Sony Research Award (2018, 2020).  He served as the TPC chair for ACM MobiCom 2019, IEEE SECON 2017, co-chair of NSF millimeter-wave research coordination network, and Associate Editor for IEEE Transactions on Mobile Computing from 2017 to 2020.

Virtual Seminar by Danijela Cabric

Title: Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Practical Considerations

Date and Time: November 19, 2021 at 11AM ET

Registration Process: Please register at

Abstract: As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems, referred as RF fingerprinting, have been introduced recently for this purpose. RF fingerprinting approaches use wireless signals directly to verify identity of radio frequency transmitters based on imperfections in their radio hardware. However, most of the existing work using machine learning for RF fingerprinting has mainly focused on classification approaches assuming a closed set of transmitters. In practice, most serious security problems would arise if malicious transmitters outside this closed set are misclassified and authorized. In this talk, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multi-class classifiers, and signal reconstruction, and study how these approaches scale with the number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The authorization robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. For this work, we have created a large Wi-Fi dataset consisting of about 10 million packets sent by 174 off-the-shelf Wi-Fi radios and simultaneously captured by 41 USRPs during 4 captures performed in ORBIT testbed along a month. We have also developed generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We explored two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Our results indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small. Another practical problem in authentication systems based on deep learning is training time when the new transmitters are added. We have developed a fast authentication algorithm based on information retrieval that uses feature vectors as RF fingerprints and locality sensitive hashing(LSH) to create a database that can quickly searched by approximate nearest neighbor algorithm. The proposed algorithm matches the accuracy of deep learning models, while allowing for more than 100x faster retraining.

Bio: Danijela Cabric is Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. Her research interests include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. Dr. Cabric received the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012 and Qualcomm Faculty Award in 2020 and 2021. Dr. Cabric is an IEEE Fellow. Her research on deep learning based RF transmitter fingerprinting is supported by SRC/JUMP CONIX center (

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Jelena Mišić

Title: Adapting PBFT for Use with Blockchain-enabled IoT Systems

Time and Date: October 15, 2021, at 9:00am EDT

Presenter:  Professor Jelena Mišić, Ryerson University, Canada


Abstract: This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the ordererís queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. We also address cluster interconnections which can increase coverage and capacity of PBFT system.

Bio: Jelena Mišić is a Professor in the Department of Computer Science at Ryerson University, Canada. She received her PhD in Computer Engineering from University of Belgrade, Serbia, in 1993. She is an internationally recognized expert in the area of IoT, blockchain, wireless networking and network security, where she has authored or co-authored four books, 150+ journal papers, 24 book chapters, and 210+ conference papers. She has chaired more than a dozen major international events and guest-edited more than a dozen special issues of various journals. She serves on the editorial boards of IEEE Transactions on Vehicular Technology, IEEE Internet of Things Journal, IEEE Network, and Ad Hoc Networks journal (published by Elsevier). She is IEEE Fellow, ACM member and serves as IEEE VTS distinguished lecturer.

Virtual Seminar by Walid Saad

Title: Brainstorming Generative Adversarial Networks (BGANs): Framework and Application to Wireless Networks

Date and Time: October 22, 2021 at 10AM EDT

Registration Process: Please register at

Abstract: Due to major communication, privacy, and scalability challenges stemming from the emergence of large-scale Internet of Things services, machine learning is witnessing a major departure from traditional centralized cloud architectures toward a distributed machine learning (ML) paradigm where data is dispersed and processed across multiple edge devices. A prime example of this emerging distributed ML paradigm is Google’s renowned federated learning framework. Despite the tremendous recent interest in distributed ML, remarkably, prior work in the area remains largely focused on the development of distributed ML algorithms for inference and classification tasks. In contrast, in this talk, we introduce the novel framework of brainstorming generative adversarial networks (BGANs) that constitutes one of the first implementations of distributed, multi-agent generative GAN models that does not rely on a centralized parameter server. We show how BGAN allows multiple agents to gain information from one another, in a fully distributed manner, without sharing their real datasets but by “brainstorming” their generated data samples. We then demonstrate the higher accuracy and scalability of BGAN compared to the state of the art through extensive experiments. We then illustrate how BGAN can be used to address key problems in the field of wireless communications by analyzing a millimeter wave channel modeling problem for wireless networks that rely on unmanned aerial vehicles (UAVs). We conclude this talk with an overview on the future outlook of the exciting area of distributed ML and its current and future applications in wireless systems.

Bio: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013 and of the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of ten conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and of the 2019 IEEE ComSoc Communication Theory Technical Committee. He was also a co-author of the 2019 IEEE Communications Society Young Author Best Paper and of the 2021 IEEE Communications Society Young Author Best Paper. He currently serves as an editor for several major IEEE Transactions.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Dusit Niyato

Title: Reliable Federated Learning for Mobile Networks

Time and Date: August 13, 2021 at 9:00am EDT

Presenter:  Professor Dusit Niyato, Nanyang Technological University, Singapore


Abstract: In this talk, we present an integration of blockchain technology into federated learning for secure and reliable federated learning. The concept of reputation is introduced as a reliable metric for federated learning workers. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is
leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering.

Bio: Dusit Niyato is currently a professor in the School of Computer Science and Engineering, Nanyang Technological University. His research interests are in the areas of wireless and mobile networking and distributed computing. He won the Best Young Researcher Award of IEEE Communications Society (ComSoc) Asia Pacific (AP) and The 2011 IEEE Communications Society Fred W. Ellersick Prize Paper Award. Currently, he is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials, an area editor of IEEE Transactions on Wireless Communications (Radio
Management and Multiple Access), an associate editor of IEEE Transactions on Mobile Computing, IEEE Transactions on Vehicular Technology, IEEE Transactions on Cognitive Communications and Networking, IEEE Wireless Communications, and IEEE Network.

Virtual Seminar by Ekram Hossain

Title: Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks

Date: May 26, 2021; Time: 11AM EDT

Registration: Please register at

Abstract: Federated learning is a machine learning setting where the centralized location trains a learning model by using remote devices. Federated learning algorithms cannot be employed in wireless networks unless the unreliable and resource-constrained nature of the wireless medium is taken into account. In this talk, I shall present a federated learning algorithm that is suitable for cellular wireless networks in a real-world scenario. I shall discuss its convergence properties, and the effects of local computation steps and communication steps on its convergence. Through experiments on real and synthetic datasets, I shall demonstrate the convergence of the proposed algorithm.

Bio: Ekram Hossain (IEEE Fellow) is a Professor in the Department of Electrical and Computer Engineering at University of Manitoba, Winnipeg, Canada. He is a Member (Class of 2016) of the College of the Royal Society of Canada, a Fellow of the Canadian Academy of Engineering, and also a Fellow of the Engineering Institute of Canada ( He received his Ph.D. in Electrical Engineering from University of Victoria, Canada, in 2001. Dr. Hossain’s current research interests include design, analysis, and optimization of wireless communication networks (with emphasis on beyond 5G/6G cellular), applied machine learning and game theory, and network economics. He was elevated to an IEEE Fellow “for contributions to spectrum management and resource allocation in cognitive and cellular radio networks”. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017, 2018, 2019, and 2020. Dr. Hossain has won several research awards including the “2017 IEEE Communications Society Best Survey Paper Award, the 2011 IEEE Communications Society Fred Ellersick Prize Paper Award, University of Manitoba Merit Award in 2010, 2013, 2014, and 2015 (for Research and Scholarly Activities), and the IEEE Wireless Communications and Networking Conference 2012 (WCNC’12) Best Paper Award. He received the 2017 IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) Distinguished Technical Achievement Recognition Award “for outstanding technical leadership and achievement in green wireless communications and networking”. Currently he serves as the Editor-in-Chief of the IEEE Press and an Editor for IEEE Transactions on Mobile Computing. Previously, he served as an Area Editor for the IEEE Transactions on Wireless Communications in the area of “Resource Management and Multiple Access” (2009-2011) and an Editor for the IEEE Journal on Selected Areas in Communications – Cognitive Radio Series (2011-2014). He serves as the Director of Magazines for the IEEE Communications Society (2020-2021). Dr. Hossain was an elected Member of the Board of Governors of the IEEE Communications Society for the term 2018-2020. He is a Distinguished Lecturer of the IEEE Communications Society. He is a registered Professional Engineer in the province of Manitoba, Canada.

About the Monthly Virtual Seminar Series: The IEEE TCCN Special Interest Group for AI and Machine Learning in Security conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.


Virtual Seminar by Gene Tsudik

Title: Secure Code Execution on Untrusted Remote Devices

Date: April 28, 2021; Time: 1PM EDT

Registration: Please register using the following link. You will receive a link in your email to attend the talk online.

Abstract: Our society is increasingly reliant upon a wide range of Cyber-Physical Systems (CPS), Internet-of-Things (IoT), embedded, and so-called “smart” devices. They often perform safety-critical functions in numerous settings, e.g., home, office, medical, automotive and industrial. Some devices are small, cheap and specialized sensors and/or actuators. They tend to have meager resources, run simple software, sometimes upon “bare metal”. If such devices are left unprotected, consequences of forged sensor readings or ignored actuation commands can be catastrophic, particularly, in safety-critical settings. This prompts the following three questions: (1) How to trust data produced by a simple remote embedded device? (2) How to ascertain that this data was produced via execution of expected software? And, (3) Is it possible to attain (1) and (2) under the assumption that all software on the remote device might be modified or compromised? In this talk, we answer these questions by describing APEX: (Verified) Architecture for Proofs of Execution, the first of its kind result for low-end embedded systems. This work has a range of applications, especially, to authenticated sensing and trustworthy actuation, APEX incurs low overhead, making it affordable even for lowest-end embedded devices; it is also publicly available.

Bio: Gene Tsudik is a Distinguished Professor of Computer Science at the University of California, Irvine (UCI). He obtained his PhD in Computer Science from USC in 1991. Before coming to UCI in 2000, he was at the IBM Zurich Research Laboratory (1991-1996) and USC/ISI (1996-2000). His research interests include many topics in security, privacy and applied cryptography. Gene Tsudik is a Fulbright Scholar, Fulbright Specialist (twice), a fellow of ACM, IEEE, AAAS, IFIP and a foreign member of Academia Europaea. From 2009 to 2015 he served as Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC, renamed TOPS in 2016). Gene was the recipient of 2017 ACM SIGSAC Outstanding Contribution Award. He is also the author of the first crypto-poem published as a refereed paper.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.

Virtual Seminar by Zhu Han

Title: MetaSensing: Reconfigurable intelligent surface Assisted RF 3D Sensing using Machine Learning

Time and Date: March 30, 2021 at 9:00AM EDT

Presenter:  Professor Zhu Han, University of Houston, USA

Venue: online registration via (Zoom meeting link will be provided prior to the seminar)

Abstract: Reconfigurable intelligent surface (RIS) stands out as a novel approach to improve the communication and sensing in the future wireless networks. It is capable to actively shape the uncontrollable wireless environments into a desirable form via flexible phase shift reconfiguration without extra hardware or power costs. To better exploit the potential of such a technique, it is essential to develop distributed configuration, to design new protocols, to explore and implement suitable application scenarios, as well as to perform intelligent control and orchestration. First we provide a general introduction of the intelligent meta-surface along with the state-of-the-art research in different areas. Then we introduce the unique features of intelligent meta-surface which enlighten its broad applications to communication and sensing, in a comprehensive way. Related design, analysis, optimization, and signal processing techniques will be presented. Finally, we explore typical meta-surface applications and discuss implementation issues with an emphasis on high-resolution smart RF sensing. Formalized analysis of several up-to-date challenges and technical details on system design will be provided for different applications.

Bio: Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communication and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communication Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han is the winner 2021 IEEE Kiyo Tomiyasu Award. He has been IEEE fellow since 2014, AAAS fellow since 2020 and IEEE Distinguished Lecturer from 2015 to 2018. Dr. Han is 1% highly cited researcher according to Web of Science since 2017.

March 2021 edition of the TCCN Newsletter available

(Download the newsletter here)

While 5G is a reality and has already been commercially launched in several parts of the world, there exist already numerous efforts and initiatives from industry and academia to look beyond 5G and conceptualize 6G by describing its roadmap along with the emerging trends and requirements, as well as several enabling techniques and architectures. Future wireless systems should effectively support a universal and ubiquitous cyber physical structure, new spectrum access schemes, and new forms of communications, while taking into account the energy efficiency and security/privacy considerations.

Two promising technologies for enabling the 6G ecosystem are full-duplex (FD) communications and integrated space-air-terrestrial communication (ISATC) systems. In this regard, this Newsletter will delve on these two key technologies for 6G wireless networks. In the FD communications area, we have interviewed Mr. Kenneth Kolodziej, from MIT Lincoln Laboratory, USA, Prof. Ioannis Krikidis, from University of Cyprus, Cyprus, Prof. Özgür Gürbüz, from Sabanci University, Turkey, and Prof. Chan-Byoung Chae, from Yonsei University, South Korea. We have also had the pleasure to get two position papers written by Prof. Alexios Balatsoukas-Stimming, from Eindhoven University of Technology, Netherlands, and by Prof. Jeyanandh Paramesh, from Carnegie Mellon University, USA, Susnata Mondal, from Intel Inc., and Ahmed Alkhateeb, from Arizona State University, USA. Within the context of ISATC systems, we have interviewed Prof. Pei Xiao, from the University of Surrey, UK, Prof. Mohamed-Slim Alouini from King Abdullah University of Science and Technology, Saudi Arabia, and Prof. Jianping An, from Beijing Institute of Technology, China, who provided us with their outlook on the opportunities and challenges on the theme.

I would like to thank our two feature topic editors: Prof. Himal A. Suraweera, from University of Peradeniya, Sri Lanka, and Prof. Gaofeng Pang, from Beijing Institute of Technology, China, for their hard efforts in arranging the content of this Newsletter. Moreover, we want to thank all authors and interviewees for sharing with us their experience and time. I would finally like to acknowledge the gracious support from the previous TCCN Chair, Dr. Yue Gao, from the current TCCN Chair, Prof. Lingyang Song, and from all TCCN officers. If you have any suggestion, feel free to contact me at: We hope that you enjoy the material of this Newsletter!

Daniel Benevides da Costa
Director, IEEE ComSoc TCCN Newsletter
Federal University of Ceará, Brazil

Virtual Seminar by Yalin Sagduyu

Title: Adversarial Machine Learning for Wireless Security in 5G and Beyond

Date and Time: March 26, 2021 at 10AM ET

Registration Process: Please register using the following link. You will receive a link in your email to attend the talk online.

Abstract: Machine learning provides powerful means to learn from the dynamic spectrum environment and solve complex tasks for wireless communications. Supported by recent advances in algorithmic and computational capabilities, deep learning has emerged as a viable solution to efficiently utilize the limited spectrum resources and optimize wireless communications, with 5G and beyond enhancements to meet the ever-growing demands for high-rate and low-latency communications. As deep learning is becoming a key component in emerging wireless technologies, a new security threat arises due to adversarial machine learning that exploits the vulnerabilities of deep learning to adversarial manipulations. Adversarial machine learning has been applied to different data domains ranging from computer vision to natural language processing. By considering the unique characteristics of the wireless medium, this talk will present adversarial machine learning as a new attack surface for the next-generation communication systems. Novel attack and defense mechanisms built upon adversarial machine learning will be described with examples from signal classification, dynamic spectrum access, and 5G and beyond applications related to spectrum co-existence, user authentication, covert communications, and network slicing. Research challenges and directions will be discussed for effective and safe adoption of much-needed machine learning techniques in the emerging wireless technologies.

Bio: Dr. Yalin Sagduyu is the Director of Networks and Security Division at Intelligent Automation, Inc. (IAI). He received his Ph.D. degree in Electrical and Computer Engineering from University of Maryland, College Park. At IAI, he directs a division of over 50 research scientists and engineers, and executes a broad portfolio of R&D projects on wireless communications, networks, security, machine learning, adversarial machine learning, and 5G and beyond. He has been a Visiting Research Professor in the Electrical and Computer Engineering Department of University of Maryland, College Park. He served as a Conference Track Chair at IEEE PIMRC, IEEE GlobalSIP and IEEE MILCOM, and in the organizing committee of IEEE GLOBECOM. He organized and chaired workshops at IEEE CNS, IEEE ICNP, ACM Mobicom, and ACM WiSec. He received the Best Paper Award at IEEE HST.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.