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Signal Processing for Internet of Things

In-Car Driver Authentication Using Wireless Sensing


Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics.

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Authors:
Beibei Wang
Submitted On:
13 May 2019 - 11:17am
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ICASSP_conf_poster_driver_authentication.pdf

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[1] Beibei Wang, "In-Car Driver Authentication Using Wireless Sensing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4487. Accessed: Jul. 16, 2019.
@article{4487-19,
url = {http://sigport.org/4487},
author = {Beibei Wang },
publisher = {IEEE SigPort},
title = {In-Car Driver Authentication Using Wireless Sensing},
year = {2019} }
TY - EJOUR
T1 - In-Car Driver Authentication Using Wireless Sensing
AU - Beibei Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4487
ER -
Beibei Wang. (2019). In-Car Driver Authentication Using Wireless Sensing. IEEE SigPort. http://sigport.org/4487
Beibei Wang, 2019. In-Car Driver Authentication Using Wireless Sensing. Available at: http://sigport.org/4487.
Beibei Wang. (2019). "In-Car Driver Authentication Using Wireless Sensing." Web.
1. Beibei Wang. In-Car Driver Authentication Using Wireless Sensing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4487

MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY


A sensor network wishes to transmit information to a fusion center to allow it to detect a public hypothesis, but at the same time prevent it from inferring a private hypothesis. We propose a multilayer sensor network structure, where each sensor first applies a nonlinear fusion function on the information it receives from sensors in a previous layer, and then a linear weighting matrix to distort the information it sends to sensors in the next layer.

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Authors:
Xin He, Wee Peng Tay
Submitted On:
1 March 2017 - 1:57am
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ICASSP17_xin.pdf

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[1] Xin He, Wee Peng Tay, "MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1521. Accessed: Jul. 16, 2019.
@article{1521-17,
url = {http://sigport.org/1521},
author = {Xin He; Wee Peng Tay },
publisher = {IEEE SigPort},
title = {MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY},
year = {2017} }
TY - EJOUR
T1 - MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY
AU - Xin He; Wee Peng Tay
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1521
ER -
Xin He, Wee Peng Tay. (2017). MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY. IEEE SigPort. http://sigport.org/1521
Xin He, Wee Peng Tay, 2017. MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY. Available at: http://sigport.org/1521.
Xin He, Wee Peng Tay. (2017). "MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY." Web.
1. Xin He, Wee Peng Tay. MULTILAYER SENSOR NETWORK FOR INFORMATION PRIVACY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1521