research-article

Next2You: Robust Copresence Detection Based on Channel State Information

Authors:
Mikhail Fomichev

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0001-9697-0359
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,
Luis F. Abanto-leon

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0002-3533-7928
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,
Max Stiegler

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0001-8420-7217
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,
Alejandro Molina

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0003-4509-9174
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,
Jakob Link

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0003-1068-6053
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,
Matthias Hollick

Technical University of Darmstadt, Darmstadt, Germany

Technical University of Darmstadt, Darmstadt, Germany

0000-0002-9163-5989
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Authors Info & Claims

ACM Transactions on Internet of Things Volume 3 Issue 2Article No.: 11pp 1–31https://doi.org/10.1145/3491244

Published:15 February 2022Publication History

ACM Transactions on Internet of Things

Abstract

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.

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Index Terms

Next2You: Robust Copresence Detection Based on Channel State Information
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Security and privacy
  1. Security services
    1. Authentication

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Published in
ACM Transactions on Internet of Things Volume 3, Issue 2
May 2022
214 pages
EISSN:2577-6207
DOI:10.1145/3505220
Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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ACM Journals for the Design of Smart and Connected Systems
Publication History
- Published: 15 February 2022
- Accepted: 1 October 2021
- Revised: 1 September 2021
- Received: 1 February 2021
Published in tiot Volume 3, Issue 2

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Author Tags
Copresence detection
context-based
Internet of Things
channel state information
neural networks
Qualifiers
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Next2You: Robust Copresence Detection Based on Channel State Information

ACM Transactions on Internet of Things

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