TY - JOUR
T1 - Packet Delivery Ratio Fingerprinting
T2 - Toward Device-Invariant Passive Indoor Localization
AU - Duan, Yaoxin
AU - Lam, Kam Yiu
AU - Lee, Victor C.S.
AU - Nie, Wendi
AU - Li, Hao
AU - Ng, Joseph K Y
N1 - Funding Information:
This work was supported in part by the National Science Foundation of China under Grant 61902043, in part by the National Key Research and Development Program of China under Grant 2017YFE0123000, in part by the Technical Innovation Application and Development Fund of Chongqing under Project cstc2019jscx-fxydX0042, in part by the Fundamental Research Fund for the Central Universities under Project 2019CDXYZDH0014, in part by the Ministry of Industry and Information Technology of China under Project [2018]282, in part by the Ministry of Industry and Information Technology of China under Project 2018282, in part by the City University Strategic under Grant 7004883, in part by the Natural Science Foundation of Chongqing under Grant cstc2018jcyjAX0139, in part by the HKBU Research Centre for Ubiquitous Computing, in part by the HKBU Institute of Computational and Theoretical Studies, and in part by the Innovation and Technology Commission of the HK SAR Government under the Innovation and Technology Fund under Project ITP/048/14LP.
PY - 2020/4
Y1 - 2020/4
N2 - Passive indoor localization for mobile Wi-Fi devices, e.g., smartphones, has attracted increasing attention from research communities recently. Existing passive localization techniques leverage received signal strength (RSS) of packets transmitted by target Wi-Fi devices and do not require a dedicated software installed on the devices. However, RSS-based passive localization techniques: 1) are device dependent, which results in poor localization accuracy for a wide variety of mobile devices and 2) cannot perform real-time passive localization. In this article, we present a novel passive localization technique, namely, packet delivery ratio (PDR) fingerprinting, to address these problems. In PDR fingerprinting, the lowest-power and highest-modulation scheme (LPHMS) is proposed to generate device-invariant PDR, which replaces RSS to construct fingerprints, to achieve device-invariant localization accuracy. Moreover, instead of passively monitoring packets rarely sent by mobile devices, in PDR fingerprinting, access points (APs) actively transmit request-to-send (RTS) frames to trigger target devices to reply clear-to-send (CTS) frames to calculate PDR. The RTS/CTS mechanism enables PDR fingerprinting to perform real-time localization. We have conducted extensive experiments in a real-world testbed. The experimental results demonstrate that PDR fingerprinting presents a competitive localization accuracy compared to RSS-based passive fingerprinting methods but is device invariant.
AB - Passive indoor localization for mobile Wi-Fi devices, e.g., smartphones, has attracted increasing attention from research communities recently. Existing passive localization techniques leverage received signal strength (RSS) of packets transmitted by target Wi-Fi devices and do not require a dedicated software installed on the devices. However, RSS-based passive localization techniques: 1) are device dependent, which results in poor localization accuracy for a wide variety of mobile devices and 2) cannot perform real-time passive localization. In this article, we present a novel passive localization technique, namely, packet delivery ratio (PDR) fingerprinting, to address these problems. In PDR fingerprinting, the lowest-power and highest-modulation scheme (LPHMS) is proposed to generate device-invariant PDR, which replaces RSS to construct fingerprints, to achieve device-invariant localization accuracy. Moreover, instead of passively monitoring packets rarely sent by mobile devices, in PDR fingerprinting, access points (APs) actively transmit request-to-send (RTS) frames to trigger target devices to reply clear-to-send (CTS) frames to calculate PDR. The RTS/CTS mechanism enables PDR fingerprinting to perform real-time localization. We have conducted extensive experiments in a real-world testbed. The experimental results demonstrate that PDR fingerprinting presents a competitive localization accuracy compared to RSS-based passive fingerprinting methods but is device invariant.
KW - Device invariant
KW - passive indoor localization
KW - Wi-Fi fingerprinting
UR - http://www.scopus.com/inward/record.url?scp=85083699643&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2019.2963436
DO - 10.1109/JIOT.2019.2963436
M3 - Journal article
AN - SCOPUS:85083699643
SN - 2327-4662
VL - 7
SP - 2877
EP - 2889
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 4
M1 - 8948034
ER -