2023 Annual Distinguished Lecture Series Talks

#fuzzy #communications #intelligence

IEEE Vehicular Technology Chapter of IEEE Toronto Section, is pleased to announce our annual Distinguished Lecturer (DL) Series Talks on May 23, 2023.

This series of talks will be a great opportunity to exchange with our colleagues and Chapter members in Toronto area. Details of the events are given below. All are welcome! Pizza lunch will be provided. 

  Date and Time




  • Date: 23 May 2023
  • Time: 10:00 AM to 02:00 PM
  • All times are (UTC-06:00) Central Time (US & Canada)
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  • SHE651, 99 Gerrard Street East
  • Toronto, Ontario
  • Canada
  • Building: SHE (Sally Horsfall Eaton Centre)

  • Contact Event Host


Toronto Metropolitan University


Consensus protocols for IoT systems with blockchains

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. Further we address proof of stake (PoS) and delegated proof of stake consensus protocols integrated with PBFT. We also address cluster interconnections which can increase coverage of PBFT system.


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, 155+ journal papers, 24 book chapters, and 215+ 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 Transactions on Emerging Topics in Computing, IEEE Network, ACM Computing Surveys and Ad Hoc Networks journal (published by Elsevier). She is an IEEE Fellow, ACM member and serves as IEEE VTS distinguished lecturer.


A Deep Unsupervised Learning Framework for Constrained Radio Resource Management Problems

The next generation of wireless networks is anticipated to be more complex and heterogeneous due to higher transmission frequencies, massive antenna deployments, and ultra-dense access points. Subsequently, the application of traditional optimization-based radio resource management (RRM) solutions is becoming difficult as they are typically non-scalable and computationally exhaustive. In this context, deep unsupervised learning is emerging as a potential solution to enable online implementation of RRM solutions without high-quality training labels and much reduced time complexity.  Nevertheless, incorporating and satisfying various constraints with zero violation in a deep unsupervised learning architecture is a fundamental challenge.   Therefore, this talk will discuss two novel differentiable projection-based approaches and their application to solve the classical power control problem in the presence of minimum data rate constraints. The first approach utilizes a differentiable convex optimization layer to implicitly define a projection function, whereas the other approach uses an iterative differentiable correction process. To enhance the sum-rate performance of the proposed models even further, the application of Frank-Wolfe algorithm (FW) will be shown. Our results depict that the proposed solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing approaches. Also, the proposed solutions outperform the classic optimization methods in terms of computation time complexity. 


Hina Tabassum is an Assistant Professor at the Lassonde School of Engineering, York University, Canada. Prior to that, she was a PDF at the Department of ECE, University of Manitoba, Canada. She received her PhD degree from King Abdullah University of Science and Technology (KAUST) in 2013. She is a Senior member of IEEE and a P.ENG in the province of Ontario. She has published over 70 technical articles in well-reputed IEEE journals and conferences. She is the founding chair of a special interest group on THz communications in IEEE ComSoc: Radio Communications Committee (RCC). She has been recognized as an Exemplary  Editor by IEEE Communications Letters, 2020, and an Exemplary Reviewer (Top 2% of all reviewers) by IEEE Transactions on Communications in 2015-2017, 2019, and 2020. Currently, she is serving as an Associate Editor in IEEE Communications Letters, IEEE Transactions on Green Communications, IEEE Communications Surveys and Tutorials, and IEEE Open Journal of Communications Society.  Her research interests include stochastic modeling, analysis, and optimization of energy efficient multi-band 5G/6G wireless networks jointly operating on sub-6GHz, millimeter, and Terahertz frequencies with applications to vehicular, aerial, and satellite networks.