Published in ACM SenSys 2022, 2022
Light Auditor detects private-data leakage through IoT covert channels using power measurements and machine learning.
Recommended citation: Woosub Jung, Kailai Cui, Kenneth Koltermann, Junjie Wang, Chunsheng Xin, Gang Zhou, "Light Auditor: Power Measurement can tell Private Data Leakage through IoT Covert Channels," ACM SenSys, 2022. /files/nov2022paper1.pdf
Published in ACM/IEEE SenSys 2026, 2026
UWB-PTrac provides real-time, seat-level phone localization using existing UWB keyless-entry infrastructure with a single in-cabin, single-antenna anchor. It resolves spatial ambiguity through optimized anchor placement or a lightweight retrofit RF shield. City-driving tests achieved 96% driver-vs-other-seat classification accuracy with optimized anchor placement and 91% with the RF shield.
Recommended citation: Kailai Cui, Ke Sun, Kang G. Shin, "UWB-Based Localization of Smartphones inside a Vehicle to Prevent Distracted Driving," ACM/IEEE International Conference on Embedded Artificial Intelligence and Sensing Systems (SenSys), 2026. https://doi.org/10.1145/3774906.3800472
Manuscript under review
EventHOI combines low-power event sensing, IMU-based egomotion cancellation, and interaction-aware compression to capture RGB evidence around hand-object interactions. Across 50 hours of data with more than 19,000 verified interactions, it reduced RGB frames by 50-65% and bandwidth by 2.7x compared with uniform 1-FPS RGB, with a 1.0-2.0 percentage-point accuracy loss and projected 33% daily power savings.
Recommended citation: Le Zhang, Kailai Cui, Hao Chen, Vlad Roznyatovskiy, Jianzhong Zhang, Anhong Guo, Kang G. Shin, Ke Sun, "EventHOI: Event-Driven Energy-Efficient Hand-Object Interaction Logging on Smartglasses." Manuscript under review.
Manuscript under review
InvAgent treats daily food-inventory monitoring as sparse-evidence inference: brief observations reveal item use and remaining amount, while whole-video VLM analysis spends computation on irrelevant frames. An edge context builder summarizes item interactions, and a bounded agentic workflow selects amount-evidence windows and revisits unresolved items. Across approximately 39 hours from seven households, the system reduced VLM input tokens by 9.9x versus 1-FPS whole-video inference while increasing amount-aware inventory F1 from 66.6% to 68.9%.
Recommended citation: Kailai Cui, Kaylee Yaxuan Li, Chaoyu Zhang, Peizhou Huang, Hao Chen, Jianzhong Zhang, Ke Sun, Kang G. Shin, "Agentic Edge-Cloud Orchestration for Resource-Efficient Daily Food Inventory Monitoring with Egocentric Video." Manuscript under review.