Mobile and People-centric Systems and Sensing

The design of mobile devices that collect and reason over people-centric data raise a unique combination of sensing, networking and systems challenges. We study how to achieve high-fidelity privacy-preserving continuous mobile sensing of a rich cross-section of the lives of both individuals and groups (e.g., health, workplaces, social interactions). Primarily, this activity considers how such goals can be accomplished through a mix of innovation spanning: networking, protocols and cloud resources; learning and systems algorithms; and, hardware/sensor design.

Selected Publications

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• Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues

  Chris Xiaoxuan Lu‚ Xuan Kan‚ Bowen Du‚ Changhao Chen‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni and John Stankovic

  In The Web Conference (WWW). 2019.

  Details about Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues | BibTeX data for Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues | Download  (pdf) of Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues

• Simultaneous Localization and Mapping with Power Network Electromagnetic Field

  Chris Xiaoxuan Lu‚ Yang Li‚ Peijun Zhao‚ Changhao Chen‚ Linhai Xie‚ Hongkai Wen‚ Rui Tan and Niki Trigoni

  In Annual International Conference on Mobile Computing and Networking (MobiCom). 2018.

  Details about Simultaneous Localization and Mapping with Power Network Electromagnetic Field | BibTeX data for Simultaneous Localization and Mapping with Power Network Electromagnetic Field | Download slides.key of Simultaneous Localization and Mapping with Power Network Electromagnetic Field | Download [MobiCom2018]emr_slam.pdf of Simultaneous Localization and Mapping with Power Network Electromagnetic Field

• DeepAuth: In−situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics

  Chris Xiaoxuan Lu‚ Bowen Du‚ Peijun Zhao‚ Hongkai Wen‚ Yiran Shen‚ Andrew Markham and Niki Trignoni

  In International Symposium on Wearable Computers (ISWC). 2018.

  Details about DeepAuth: In−situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics | BibTeX data for DeepAuth: In−situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics | Download  (pdf) of DeepAuth: In−situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics