Vehicular Technology

On Wednesday, September 30, 2020 at 9:00 a.m., Shu Fu of Chongqing University, China will present “Collaborative Multi-Resource Allocation in Terrestrial-Satellite Network (TSN) Towards 6G”. Day & Time: Wednesday, September 20, 2020 9:00 a.m. – 10:00 a.m. Speaker: Shu Fu of Chongqing University, China Organizer: IEEE Toronto Vehicular Technology Chapter Location: Virtual – Zoom Contact: Lian Zhao Abstract: Terrestrial-Satellite Network (TSN) is critical for achieving the integrated ground-air-space in 6G by its employment of flight equipments to increase space resource diversity. The employment of flight equipments and the equipped caching, computing, and communication (3C) resources lead to the problem of multi-resource co-allocation while challenging the objective of low delay, large throughput, and high energy efficiency. We focus on solving this problem in terms of the following aspects: firstly, we will build a Nash bargaining model to implement the 3C resource allocation to maximize the user fairness guaranteed throughput. Secondly, we will discuss about the optimization of the satellite-terrestrial allocation of the unmanned aerial vehicle (UAV) based relays. Moreover, for the weak coverage areas of the ground gateways, we will discuss the mobile co-allocation of multi-resource model for UAV-BS. Finally, we will consider the heterogeneous traffic data characteristics to build co-allocation of multi-resource models, based on which the low delay constrained inter-satellite relaying and routing mechanism and satellite-terrestrial store-and-forward mechanism with high energy efficiency will be achieved. Register: Please visit https://events.vtools.ieee.org/m/240949 for the Zoom link. Biography: Shu Fu received the Ph.D. degree in communication and information system from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2016. He joined the College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China, as an Assistant Professor in 2016, and has been an Associate Professor since 2018. He had been awarded twice “National Scholarship” during his PhD’s study. He was a visiting PhD student at University of Waterloo in 2014-2015; and a visiting professor at Ryerson University, Canada in 2019. He is a communication committee member of Chinese Institute of Electronics (CIE) Internet of Things Youth Specialist Group. He has published more than 30 IEEE journal papers and conference papers. His research interests include B5G network, UAV network, and terrestrial-satellite network, etc.

The IEEE Toronto Vehicular Technology Chapter is hosting two talks as part of their Rising Star Series! Haixia Peng and Huaqing Wu are at their final stages of their PhD studies at the University of Waterloo. They will share their research on mobile edge computing/caching/ communication, network slicing, Artificial Intelligence (AI) enabled IoV networks, and integrated space-air-ground vehicular networks during their PhD studies. Location: All events are held with Zoom Meeting https://ryerson.zoom.us/j/96808290854 Meeting ID: 968 0829 0854 Contact: Please contact Lian Zhao at l5zhao@ryerson.ca for any questions The details of each talk are below. Intelligent Multi-Dimensional Resource Slicing in MEC-Assisted Vehicular Networks Date & Time: Tuesday, March 16, 2021 7:00 p.m. – 8:00 p.m. Speaker: Haixia Peng, University of Waterloo Abstract: Benefiting from advances in the automobile industry and wireless communication  technologies, the vehicular network has been emerged as a key enabler of intelligent  transportation services. However, with more and more services and applications, mobile data  traffic generated by vehicles has been increasing and the issue of the overloaded computing task  has been getting worse. Because of the limitation of spectrum resources and vehicles’ onboard  computing/caching resources, it is challenging to promote vehicular networking technologies to  support the emerged services and applications, especially those requiring sensitive delay and  diverse resources. To effectively address the above challenges, two potential technologies, multi access edge computing (MEC) and unmanned aerial vehicle (UAV), can be exploited in  vehicular networks. In this presentation, I will introduce how to adopt optimization and AI technologies for efficient resource slicing, and therefore supporting various applications with  satisfied quality of service (QoS) requirements in MEC- and/or UAV-assisted vehicular  networks. For a relatively simple vehicular network scenario with only terrestrial MEC servers, a  model-based method is applied for dynamic spectrum management, including spectrum slicing,  spectrum allocating, and transmit power controlling. For a vehicular network supported by both  terrestrial and aerial MEC servers, an AI-based method is applied to effectively manage the  spectrum, computing, and caching resources while satisfying the QoS requirements of different  applications. Biography: Haixia Peng received her M.S. and Ph.D. degrees in Electronics and  Communication Engineering and Computer Science from Northeastern University, Shenyang,  China, in 2013 and 2017, respectively. She is currently a Ph.D. student in the Department of  Electrical and Computer Engineering at the University of Waterloo, Canada. Her current  research focuses on Internet of vehicles, resource management, multi-access edge computing,  and reinforcement learning. She has authored or co-authored more than 30 technical papers.  She serves/served as a reviewer for IEEE Journals on Selected Areas in Communications (JSAC),  IEEE Transactions on Communications, IEEE Transactions on Vehicular Technologies, etc. more  than 20 prestigious journals, and as a TPC member in IEEE ICC, Globecom, VTC, etc.  conferences. Content Caching and Delivery in Heterogeneous Vehicular Networks Date & Time: Tuesday, March 30, 2021 7:00 p.m. – 8:00 p.m. Speaker: Huaqing Wu, University of Waterloo Abstract: Connected and automated vehicles (CAVs), which enable information exchange and  content delivery in real time, are expected to revolutionize current transportation systems.  However, the emerging CAV applications such as content delivery pose stringent requirements on  latency, throughput, and global connectivity. To empower multifarious CAV content delivery,  heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial  networks and space networks, can guarantee reliable, flexible, and globally seamless service  provisioning. In addition, edge caching can facilitate content delivery by caching popular files in  the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. In this  talk, we investigate the content caching and delivery schemes in the caching-enabled HetVNet.  First, we study the content caching in terrestrial HetVNets with intermittent network connections.  A coding-based caching scheme is designed and a matching-based content placement algorithm is  proposed to minimize the content delivery delay. Second, UAV-aided caching is considered to  assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching  and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching,  content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic  vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem.  Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated  vehicular networks (SAGVNs), where the vehicle-to-AP association, bandwidth allocation, and  content delivery ratio are jointly optimized. To address the tightly coupled optimization variables,  we propose a load- and mobility-aware cooperative delivery scheme to solve the joint optimization  problem with the consideration of user fairness, load balancing, and vehicle mobility. Biography: Huaqing Wu received the B.E. and M.E. degrees in Electrical Engineering  from Beijing University of Posts and Telecommunications, Beijing, China, in 2014 and 2017,  respectively. She is currently working toward the Ph.D. degree at the Department of Electrical and  Computer Engineering, University of Waterloo, Waterloo, ON, Canada. Her current research  interests include vehicular networks with emphasis on edge caching, wireless resource  management, space-air-ground integrated networks, and application of artificial intelligence (AI)  for wireless networks. She has authored/co-authored more than 30 technical papers which are  published in prestigious refereed journals (IEEE JSAC, TWC, WCM, etc.) and conferences (IEEE  ICC, Globecom, VTC, etc.).

Cyberattack is a growing concern in connected vehicles because of the possibility of accessing, changing, or destroying sensitive information. On the other hand, many existing security protocols are infeasible to apply because of high resource consumption. Containers are a method of providing added security through virtualization. Advantages of containers include increasing utilization of bare-metal resources, and adding security isolation properties to various types of systems. These advantages make containers well-suited for the connected vehicle software. This paper describes a specific consideration in the development of a container architecture pattern for embedded systems, aimed at enforcing multiple modes of application functionality. Speaker(s): Dr. Akramul Azim, Virtual: https://events.vtools.ieee.org/m/283789

Over the last few decades wind energy has emerged as one of the fastest growing mainstream power technologies due to its low cost and environmentally friendly nature compared to conventional fossil fuel based power generation. Considering available options of state-of-the-art generator technologies in wind energy conversion system (WECS), doubly fed induction generator (DFIG) has become popular because of its economic operation, ability to regulate in sub-synchronous or super-synchronous speed and decoupled control of active and reactive power. Harnessing regulated power supply from unpredictable wind blow, extraction of maximum power from intermittent generation and supervision on nonlinear system dynamics of DFIG-WECS are some of the critically challenging issues for wind energy system. Maximization of the power yielded from wind turbine is possible by optimizing tip-speed ratio, turbine rotor speed or torque and blade angle. Traditionally, maximum power point tracking (MPPT) control algorithm is based on the Hill Climb Search (HCS) method due to its simple implementation and turbine parameter-independent scheme. Since the conventional HCS algorithm has few drawbacks such as power fluctuation and speed-efficiency trade-off, a new adaptive step size based HCS controller is developed in this work to mitigate its deficiencies by incorporating wind speed measurement in the controller. Again, conventional feedback linearization controllers are sensitive to system parameter variations and disturbances on grid-connected WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. An adaptive backstepping based nonlinear control (ABNC) scheme with iron-loss minimization algorithm for DFIG is also developed in this work to obtain both improved dynamic performance and reduced power loss. In order to verify the effectiveness of the proposed control schemes, simulation models are designed using Matlab/Simulink. The proposed MPPT control, nonlinear control for grid-connected mode of DFIG-WECS has been successfully implemented in real-time using DSP controller board DS1104 for a laboratory 350 W DFIG. In the laboratory environment a 4-quadrant dynamometer is used to emulate the wind turbine to provide variable wind speed to the generator. The performance of the proposed ABNC is also compared with the benchmark tuned proportional-integral (PI) controller under different operating conditions such variable wind speed, grid voltage disturbance and parameter uncertainties and it exhibits excellent grip over the rotor side and grid side converter control. Virtual: https://events.vtools.ieee.org/m/283915

In the 5G era, wireless networks are anticipated to provide connectivity for massive mobile devices and to enable a variety of innovative applications, which generate enormous computing service demands with diverse and stringent Quality of Service requirements. To support the emerging computing service demands, Mobile Edge Computing (MEC), as a cutting-edge technology in 5G, utilizes computing resources on the network edge to provide computing services for mobile devices within a radio access network. In this talk, we will investigate computing resource management for MEC to satisfy diverse computing requirements in wireless networks. We will introduce three computation offloading and task scheduling schemes tailored for supporting representative use cases and network scenarios in 5G, including autonomous driving, Unmanned Aerial Vehicle (UAV) assisted networks, and highly dense vehicular networks. Machine learning algorithms are applied to facilitate low-latency and reliable computing services in complex and dynamic network environments. Speaker(s): Mushu Li Biography: Dr. Mushu Li received the Ph.D. degree from the University of Waterloo, ON, Canada, in 2021, and the M.A.Sc. degree from Ryerson University, Toronto, ON, Canada, in 2017. She is currently a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Waterloo.  Dr. Li was a recipient of the NSERC Canada Graduate Scholarship (2018-2021) and Ontario Graduate Scholarship in 2015 and 2016, respectively. Her research interests include Internet of vehicles, resource management, multi-access edge computing, and reinforcement learning. She has authored/co-authored over 20 technical papers. She serves/served as a reviewer for IEEE Journals on Selected Areas in Communications (JSAC), IEEE Transactions on Communications, IEEE Transactions on Vehicular Technologies, etc. Virtual: https://events.vtools.ieee.org/m/305769

The transition to electric vehicles (EVs) is gaining momentum around the world and the major drivers for this acceleration are the rising awareness by the public for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies. EVs acceptance however is hindered by several challenges; among them is their shorter driving range, slower charging rates, and the ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. To mitigate this anxiety, a naïve approach is to expand the charging network, while an unplanned expansion may challenge the generation, transmission and distribution sector of the grid along with being a potential cyber-physical attack platform. As a consequence, to attain a graceful EV penetration for curtailing GHG emission, along with the socioeconomic initiatives, an extensive research is required, especially to mitigate the range anxiety and ameliorate the load congestion on the grid. Fortunately, the IoT enabled charging ecosystem (i.e., EVs, charging stations, the grid etc.) enables smart and informed charging schemes to exploit the benefit of different distributed energy sources (e.g., renewable energy based standalone chargers, vehicle to grid or vehicle to vehicle energy transfer technology, etc.) to minimize the load burden of the grid. But, on the other hand, this IoT enabled charging ecosystem unveils a new cyber-physical attack surface and hence, new challenges also need to be addressed to make this charging ecosystem secure as well. Virtual: https://events.vtools.ieee.org/m/309875 Speaker: Dr. Mohammad Ekramul Kabir Biography: Dr. Mohammad Ekramul Kabir is currently working as a Horizon postdoctoral research fellow in CIISE at Concordia University, Montreal, Canada. He obtained his PhD on Information and Systems Engineering from Concordia University in May 2021. He has received the B.Sc. and M.S. degree in Applied Physics, Electronics and Communication engineering from University of Dhaka, Bangladesh. His research interests include green, smart, and secure charging of electric vehicle, cloud/edge computing security and applications of artificial intelligence. He is a coauthor of a number of peer-reviewed journal and conference papers. He also serves/served as a reviewer for IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, IEEE Transactions on Network and Service Management, IEEE Intelligent Transportation Systems Magazine, IEEE PES General Meeting, etc.

After two-years’ online events, IEEE Vehicular Technology Chapter of IEEE Toronto Section, is pleased to announce our first in-person Distinguished Lecturer (DL) Series Talks on June 23, 2022, for a theme as Connecting People/Things/Vehicles. This in-person series of talks will be a great opportunity to meet and chat and exchange with our International and National visitors, colleagues, and Chapter members in Toronto area. Details of the events are given below. All are welcome! Speaker(s): Dr. Duist Niyato, Dr. Jelena Mišić, Dr. Ping Wang, Dr. Hina Tabassum, Dr. Jie Gao Register: https://events.vtools.ieee.org/m/315859 Biographies: Dr. Ping Wang is an Associate Professor at the Department of Electrical Engineering and Computer Science, York University, and a Tier 2 York Research Chair. Prior to that, she worked with Nanyang Technological University, Singapore, from 2008 to 2018. Her research interests are mainly in the area of wireless communication networks, cloud computing and Internet of Things with the recent focus on integrating Artificial Intelligence (AI) techniques into communications networks. She has published more than 250 papers/conference proceedings papers. Her scholarly works have been widely disseminated through top-ranked IEEE journals/conferences and received the Best Paper Awards from IEEE Wireless Communications and Networking Conference (WCNC) in 2022, 2020 and 2012, from IEEE Communication Society: Green Communications & Computing Technical Committee in 2018, and from IEEE International Conference on Communications (ICC) in 2007. Her work received 21,000+ citations with H-index 70 (Google Scholar). She is an IEEE Fellow and a Distinguished Lecturer of the IEEE Vehicular Technology Society. 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. Agenda: Program Chair 1:30-1:33 Dr. Lian Zhao, Opening and welcome 1:33-2:15 Dr. Duist Niyato, “Metaverse virtual service management: game theoretic approaches” Dr. Khalid Hafeez 2:15-3:00 Dr. Jelena Mišić, “Blockchain in IoT based on practical Byzantine fault tolerance” Dr. Khalid Hafeez 3:00-3:15 Break 3:15-3:45 Dr. Ping Wang, “Towards Fast-Convergent Federated Learning with non-IID data” Dr. Jie Gao 3:45-4:15 Dr. Hina Tabassum, “Mobility-Aware Performance Optimization for Next Generation Vehicular Networks” Dr. Jie Gao 4:15-4:30 Break 4:30-5:00 Dr. Lian Zhao, “Computing offloading and task scheduling at network edge” Dr. Ajmery Sultana 5:00-5:30 Dr. Jie Gao, “Network Planning: from Slicing to Digital Twin”  Dr. Ajmery Sultana 5:40-5:33 Dr. Lian Zhao, Closing remark