TY - GEN
T1 - WiTrack
T2 - 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
AU - Chen, Ting Han
AU - Sou, Sok Ian
AU - Lee, Yinman
PY - 2019/8
Y1 - 2019/8
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85075178059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075178059&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146
DO - 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00146
M3 - Conference contribution
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
SP - 788
EP - 795
BT - 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
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 August 2019 through 8 August 2019
ER -