Lian Zhao

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications in the proximity of the mobile users. In this talk, motivation for task offloading and edge computing is introduced. Followed by two case studies for task scheduling and resource management for IoV and IoT networks respectively. For the IoV network, a proactive indexed-based scheduling scheme, considering the predicted future vehicle mobility dynamics using deep reinforcement learning (DRL) approach, is proposed to schedule the offloaded tasks. For the IoT network, user collaboration is considered. A joint offloading decision and resource management scheme, inspired from bio-optimization and DRL, is proposed to minimize the total system operation energy consumption. 

Biography:

Lian Zhao received her Ph.D. degree from the Department of Electrical and Computer Engineering (ELCE), University of Waterloo, Canada, in 2002. She joined the Department of Electrical, Computer, & Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University), Toronto, Canada, in 2003 and as a professor in 2014. She has been a distinguished lecture for IEEE Communication Society (ComSoc) (2020-2021) and IEEE Vehicular Technology Society (VTS) (starting July 2022). She has been serving as an Editor for IEEE Trans. on wireless communication, Trans. on vehicular technologies, and IEEE Internet of Things Journal. She has served various Chair positions in organizating IEEE conferences.

She received the Best Land Transportation Paper Award from IEEE Vehicular Technology Society in 2016; Best Paper Award from the 2013 International Conference on Wireless Communications and Signal Processing (WCSP) and Best Student Paper Award (with her student) from Chinacom in 2011; the Canada Foundation for Innovation (CFI) New Opportunity Research Award in 2005. She is a licensed Professional Engineer in the Province of Ontario, a senior member of the IEEE ComSoc and VTS.

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Jie Gao

Service-specific network planning is essential for 5G and beyond since the same network has to simultaneously support a variety of services. Unlike real-time network operations, network planning faces unique challenges such as the large scale and the lack of accurate information. This presentation introduces ideas on fine-grained and flexible network planning for future networks. Specifically, for network planning with network slicing, designs to improve planning granularity along space, time, and slice dimensions are presented. The role of data-driven methods and the potential of digital twins in network planning for meeting diverse service requirements and improving resource utilization are also highlighted.  

Biography:

Jie Gao received his Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, in 2014. He was an NSERC postdoctoral fellow with Toronto Metropolitan University (formerly Ryerson University), Toronto, ON, from 2017 to 2019, a research associate with the University of Waterloo, Waterloo, ON, from 2019 to 2020,  and an assistant professor with Marquette University, Milwaukee, WI, USA, from 2020 to 2022. He will join the School of Information Technology, Carleton University, Ottawa, ON, in July 2022. Dr. Gao’s research interests include machine learning for communications and networking, cloud and multi-access edge computing, Internet of Things (IoT) and industrial IoT solutions, and next-generation (6G) wireless networks in general. He is a senior member of IEEE, a member of IEEE ComSoC and IEEE VTS, and the Lead Associate Editor for the Vehicular Technology Society Section in IEEE Access.