@article{fomichev_abanto-leon_stiegler_molina_link_hollick_2021, 
  title={Next2You: Robust Copresence Detection Based on Channel State Information}, 
  DOI={10.1145/3491244}, 
  abstractNote={Context-based copresence detection schemes are a necessary prerequisite to
building secure and usable authentication systems in the Internet of Things
(IoT). Such schemes allow one device to verify proximity of another device
without user assistance utilizing their physical context (e.g., audio). The
state-of-the-art copresence detection schemes suffer from two major
limitations: (1) they cannot accurately detect copresence in low-entropy
context (e.g., empty room with few events occurring) and insufficiently
separated environments (e.g., adjacent rooms), (2) they require devices to have
common sensors (e.g., microphones) to capture context, making them impractical
on devices with heterogeneous sensors. We address these limitations, proposing
Next2You, a novel copresence detection scheme utilizing channel state
information (CSI). In particular, we leverage magnitude and phase values from a
range of subcarriers specifying a Wi-Fi channel to capture a robust wireless
context created when devices communicate. We implement Next2You on
off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and
evaluate it based on over 95 hours of CSI measurements that we collect in five
real-world scenarios. Next2You achieves error rates below 4%, maintaining
accurate copresence detection both in low-entropy context and insufficiently
separated environments. We also demonstrate the capability of Next2You to work
reliably in real-time and its robustness to various attacks.}, 
  author={Fomichev and Abanto-Leon and Stiegler and Molina and Link and Hollick}, 
  year={2021}, 
  month={Nov}
  }