WiTrack: Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength

Ting Han Chen, Sok Ian Sou, Yinman Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the widespread adoption of smartphones and other wearable devices nowadays, the massive amount of transmitted wireless signals left behind by the users provides a significant potential source of time and location information regarding human mobility. Furthermore, WiFi and Bluetooth wireless signal strength measurements have emerged as a promising solution for delivering proximity services to users. Accordingly, the present study proposes a system designated as WiTrack for tracking human-to-human mobility relationships in indoor environments based on the correlation between their wireless fingerprints. In particular, the mobility similarity between each pair of individuals is evaluated using the signal power features observed by a set of scanners deployed at different locations (i.e., spatial features) over time (i.e., temporal features). A higher similarity value is taken to indicate a more similar mobility behavior of the users. The feasibility of WiTrack is demonstrated using a testbed built in the corridor of a university campus.

Original languageEnglish
Title of host publicationProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages788-795
Number of pages8
ISBN (Electronic)9781728130248
DOIs
Publication statusPublished - 2019 Aug
Event17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan
Duration: 2019 Aug 52019 Aug 8

Publication series

NameProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

Conference

Conference17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
CountryJapan
CityFukuoka
Period19-08-0519-08-08

Fingerprint

Smartphones
Bluetooth
Testbeds
Wi-Fi
Scanner
Fingerprint
Testbed
Proximity
Relationships
Human
Similarity
Universities

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

Cite this

Chen, T. H., Sou, S. I., & Lee, Y. (2019). WiTrack: Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 (pp. 788-795). [8890262] (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146
Chen, Ting Han ; Sou, Sok Ian ; Lee, Yinman. / WiTrack : Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength. Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 788-795 (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019).
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abstract = "With the widespread adoption of smartphones and other wearable devices nowadays, the massive amount of transmitted wireless signals left behind by the users provides a significant potential source of time and location information regarding human mobility. Furthermore, WiFi and Bluetooth wireless signal strength measurements have emerged as a promising solution for delivering proximity services to users. Accordingly, the present study proposes a system designated as WiTrack for tracking human-to-human mobility relationships in indoor environments based on the correlation between their wireless fingerprints. In particular, the mobility similarity between each pair of individuals is evaluated using the signal power features observed by a set of scanners deployed at different locations (i.e., spatial features) over time (i.e., temporal features). A higher similarity value is taken to indicate a more similar mobility behavior of the users. The feasibility of WiTrack is demonstrated using a testbed built in the corridor of a university campus.",
author = "Chen, {Ting Han} and Sou, {Sok Ian} and Yinman Lee",
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Chen, TH, Sou, SI & Lee, Y 2019, WiTrack: Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength. in Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019., 8890262, Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019, Institute of Electrical and Electronics Engineers Inc., pp. 788-795, 17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019, Fukuoka, Japan, 19-08-05. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146

WiTrack : Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength. / Chen, Ting Han; Sou, Sok Ian; Lee, Yinman.

Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 788-795 8890262 (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - With the widespread adoption of smartphones and other wearable devices nowadays, the massive amount of transmitted wireless signals left behind by the users provides a significant potential source of time and location information regarding human mobility. Furthermore, WiFi and Bluetooth wireless signal strength measurements have emerged as a promising solution for delivering proximity services to users. Accordingly, the present study proposes a system designated as WiTrack for tracking human-to-human mobility relationships in indoor environments based on the correlation between their wireless fingerprints. In particular, the mobility similarity between each pair of individuals is evaluated using the signal power features observed by a set of scanners deployed at different locations (i.e., spatial features) over time (i.e., temporal features). A higher similarity value is taken to indicate a more similar mobility behavior of the users. The feasibility of WiTrack is demonstrated using a testbed built in the corridor of a university campus.

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U2 - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146

DO - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146

M3 - Conference contribution

AN - SCOPUS:85075178059

T3 - Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

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PB - Institute of Electrical and Electronics Engineers Inc.

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Chen TH, Sou SI, Lee Y. WiTrack: Human-to-human mobility relationship tracking in indoor environments based on spatio-temporal wireless signal strength. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 788-795. 8890262. (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019). https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146