报告题目：Synergizing Digital Twins and UAVs for Post-Disaster Network Recovery and Management

报告时间：2025年12月28日（星期日）15:00-16:00

报告地点：澳门博彩 中心校区王湘浩楼A521报告厅

报 告 人：叶强

报告人简介：

Dr. Qiang (John) Ye received the PhD degree in Electrical and Computer Engineering from the University of Waterloo, ON, Canada, in 2016. Since Sept. 2023, he has been an Assistant Professor with the Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, AB, Canada. 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 is the leading co-chair of a SIG in the IEEE ComSoc - IoT-AHSN Technical Committee. Dr. Ye serves as an Associate Editor for prestigious IoT-J, TVT, TCCN, and OJ-COMS. He received the Best Paper Award in ICCC. He has been named among the World’s Top 2% Scientists in 2023-2025 (by Stanford/Elsevier). He has been selected as an IEEE Communication Society Distinguished Lecturer for the class of 2025-2026. Dr. Ye is a Senior Member of IEEE.

报告内容简介：

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. 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.

主办单位：澳门博彩

澳门博彩 软件学院

澳门博彩 澳门博彩技术研究所

符号计算与知识工程教育部重点实验室

仿真技术教育部重点实验室

网络技术及应用软件教育部工程研究中心

澳门博彩 国家级计算机实验教学示范中心