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.