QID: Identifying mobile devices via wireless charging fingerprints

Deliang Yang; Guoliang Xing; Jun Huang; Xiangmao Chang; Xiaofan Jiang

doi:10.1109/IoTDI49375.2020.00009

QID: Identifying mobile devices via wireless charging fingerprints

Deliang Yang
, Guoliang Xing
, Jun Huang
, Xiangmao Chang
, Xiaofan Jiang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

11 Citations (Scopus)

Abstract

Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.

Original language	English
Title of host publication	Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020
Publisher	IEEE
Pages	1-13
ISBN (Electronic)	78-1-7281-6602-5
ISBN (Print)	978-1-7281-6603-2
DOIs	https://doi.org/10.1109/IoTDI49375.2020.00009
Publication status	Published - 2020
Externally published	Yes
Event	5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020 - Virtual, Sydney, Australia Duration: 21 Apr 2020 → 24 Apr 2020 https://conferences.computer.org/iotDI/2020/

Publication series

Name	Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI

Conference

Conference	5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020
Place	Australia
City	Sydney
Period	21/04/20 → 24/04/20
Internet address	https://conferences.computer.org/iotDI/2020/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Device recognition
Qi wireless charging
Real time processing

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10.1109/IoTDI49375.2020.00009

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Yang, D., Xing, G., Huang, J., Chang, X., & Jiang, X. (2020). QID: Identifying mobile devices via wireless charging fingerprints. In Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020 (pp. 1-13). Article 9097527 (Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI). IEEE. https://doi.org/10.1109/IoTDI49375.2020.00009

@inproceedings{3eac472c3de74ff7996f9da3710f01b9,

title = "QID: Identifying mobile devices via wireless charging fingerprints",

abstract = "Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.",

keywords = "Device recognition, Qi wireless charging, Real time processing",

author = "Deliang Yang and Guoliang Xing and Jun Huang and Xiangmao Chang and Xiaofan Jiang",

year = "2020",

doi = "10.1109/IoTDI49375.2020.00009",

language = "English",

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series = "Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI",

publisher = "IEEE",

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booktitle = "Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020",

address = "United States",

note = "5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020 ; Conference date: 21-04-2020 Through 24-04-2020",

url = "https://conferences.computer.org/iotDI/2020/",

}

Yang, D, Xing, G, Huang, J, Chang, X & Jiang, X 2020, QID: Identifying mobile devices via wireless charging fingerprints. in Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020., 9097527, Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI, IEEE, pp. 1-13, 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020, Sydney, Australia, 21/04/20. https://doi.org/10.1109/IoTDI49375.2020.00009

QID: Identifying mobile devices via wireless charging fingerprints. / Yang, Deliang; Xing, Guoliang; Huang, Jun et al.
Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020. IEEE, 2020. p. 1-13 9097527 (Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - QID

T2 - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020

AU - Yang, Deliang

AU - Xing, Guoliang

AU - Huang, Jun

AU - Chang, Xiangmao

AU - Jiang, Xiaofan

PY - 2020

Y1 - 2020

N2 - Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.

AB - Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy.

KW - Device recognition

KW - Qi wireless charging

KW - Real time processing

UR - https://www.scopus.com/pages/publications/85085957900

UR - https://www.scopus.com/pages/publications/85085957900#tab=impact

U2 - 10.1109/IoTDI49375.2020.00009

DO - 10.1109/IoTDI49375.2020.00009

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-7281-6603-2

T3 - Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI

SP - 1

EP - 13

BT - Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020

PB - IEEE

Y2 - 21 April 2020 through 24 April 2020

ER -

Yang D, Xing G, Huang J, Chang X, Jiang X. QID: Identifying mobile devices via wireless charging fingerprints. In Proceedings - 5th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2020. IEEE. 2020. p. 1-13. 9097527. (Proceedings - ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI). Epub 2020 May 21. doi: 10.1109/IoTDI49375.2020.00009

QID: Identifying mobile devices via wireless charging fingerprints

Abstract

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UN SDGs

Research Keywords

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