August 2026 (1)
- Aug.
07
- 2026
IEEE DLT: Synergizing Digital Twins and UAVs for Post-Disaster Network Recovery and Management
2026-08-07T15:00:00.000Z,
2026-08-07T15:00:00.000Z
–
2026-08-07T16:30:00.000Z
Walter Light Hall, Room WLH 314, Queen's University
Hosted by Kingston Section Chapter, C16/COM19
Co-hosted by Kingston Section; Newfoundland Labrador Sect Chap, C16/COM19/CAS04;
Abstract: Ensuring resilient network connectivity after natural disasters is critical to the operation of modern smart cities. However, post-disaster network recovery faces significant challenges, including incomplete user traffic profiles, unique post-disaster user movement patterns, and unknown network traffic dynamics. In this talk, I will present a digital-twin-assisted network recovery and management (DT-NRM) framework to address these challenges. Specifically, in the proposed framework, we build and maintain a high-fidelity DT model through two-stage closed-loop DT lifecycles, DT lifecycle 1 (DT-C1) and DT lifecycle 2 (DT-C2), which predict the post-disaster network traffic variations and further assist network reconstruction via unmanned aerial vehicle (UAV) deployment. In DT-C1, we establish the DT model through partial physical network traffic data collection and refinement, integrating graph neural network (GNN)-based user traffic prediction with a first-stage residual-based calibration to enhance the DT model performance. Based on the predicted user traffic from DT-C1, DT-C2 implements a two-timescale network recovery and management solution via large-timescale proactive UAV deployment and small-timescale UAV resource allocation. Aerial-terrestrial data fusion is then applied to restore the complete network traffic profile for a second-stage DT model calibration through transfer learning. The iterative two-stage DT lifecycles enable continuous DT model evolution. A case study demonstrates that the proposed DT-NRM accurately predicts network traffic dynamics and outperforms state-of-the-art approaches. We will also discuss some future research directions at the end of the talk.
Bio: Dr. Qiang (John) Ye received the PhD degree in Electrical and Computer Engineering from the University of Waterloo, ON, Canada, in 2016. Since 2023, he has been a faculty member with the Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, AB, Canada. Before joining UCalgary, he worked as a faculty member with the Memorial University of Newfoundland, NL, Canada from 2021 to 2023 and with the Minnesota State University, Mankato, USA, from 2019 to 2021, respectively. He was with the Department of Electrical and Computer Engineering, University of Waterloo as a Postdoctoral Fellow and then a Research Associate from 2016 to 2019. He has published over 90 research articles on top-ranked journals and conference proceedings. He is/was the General, Publication, Program Co-chairs for different reputable international conferences and workshops (e.g., IEEE INFOCOM, GLOBECOM, VTC, ICCC, ICCT, WISEE, SWC). He also serves/served as the IEEE Vehicular Technology Society (VTS) Region 7 Chapter Coordinator in 2024, the IEEE Communications Society (ComSoc) Southern Alberta Chapter Vice Chair from 2024, and the VTS Regions 1-7 Chapters Coordinator from 2022 to 2023. He is the leading SIG co-chair in the IEEE ComSoc - IoT-AHSN Technical Committee. Dr. Ye serves/served as an Associate Editor for prestigious IEEE journals, such as IEEE TNSM, IEEE WCL, IEEE IoT-J, IEEE TVT, IEEE TCCN, and IEEE OJ-COMS. He received the IEEE Open Journal of Vehicular Technology (OJVT) Best Paper Award in 2026, the Best Paper Award in the IEEE/CIC International Conference on Communications in China (ICCC) in 2024, the Early Career Research Excellence Award, Schulich School of Engineering, University of Calgary, in 2025, the IEEE OJ-COMS Exemplary Editor Award in 2025, the IEEE TCCN Exemplary Editor Award in 2023. He has been selected as an IEEE Communication Society Distinguished Lecturer for the class of 2025-2026. He has been named among the World’s Top 2% Scientists in 2021-2025 (by Stanford/Elsevier). Dr. Ye is Senior Member of IEEE.
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