publications

2021

  1. TMC
    Touch-to-Access Device Authentication for Indoor Smart Objects
    Yan, Zhenyu, Song, Qun, and Tan, Rui
    IEEE Transactions on Mobile Computing (in press) (2021)
  2. DeepMTD: Moving Target Defense for Deep Visual Sensing against Adversarial Examples
    Song, Qun, Yan, Zhenyu, and Tan, Rui
    ACM Transactions on Sensor Networks (in press) (2021)
  3. PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems
    Luo, Wenjie, Yan, Zhenyu, Song, Qun, and Tan, Rui
    In The 20th International Conference on Information Processing in Sensor Networks (2021) (Acceptance ratio: 26/105=24.8%)
    Best Artifact Award Runner-Up
  4. Book Chapter
    Clock Synchronization for Wide-Area Applications
    Yan, Zhenyu, Li, Yang, and Tan, Rui
    Intelligent IoT for the Digital World (Chapter 6), Published by John Wiley & Sons (2021)

2020

  1. PhD Thesis
    Exploiting Induced skin electric potential for body-area IoT system functions
    Yan, Zhenyu
    Published by Nanyang Technological University, Singapore (2020)

2019

  1. Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials
    Yan, Zhenyu, Song, Qun, Tan, Rui, Li, Yang, and Kong, Adams Wai Kin
    In The 25th Annual International Conference on Mobile Computing and Networking (2019) (Acceptance ratio: 55/290=18.9%)
  2. Moving Target Defense for Embedded Deep Visual Sensing against Adversarial Examples
    Song, Qun, Yan, Zhenyu, and Tan, Rui
    In The 17th ACM Conference on Embedded Networked Sensor Systems (2019) (Acceptance ratio: 28/144=19%)

2018

  1. TMC
    Wearables Clock Synchronization Using Skin Electric Potentials
    Yan, Zhenyu, Tan, Rui, Li, Yang, and Huang, Jun
    IEEE Transactions on Mobile Computing (2018)

2017

  1. Application-Layer Clock Synchronization for Wearables Using Skin Electric Potentials Induced by Powerline Radiation
    Yan, Zhenyu, Li, Yang, Tan, Rui, and Huang, Jun
    In The 15th ACM Conference on Embedded Network Sensor Systems (2017) (Acceptance ratio: 26/151=17%)