TY - JOUR
T1 - A Hybrid Fingerprint Quality Evaluation Model for WiFi Localization
AU - Li, Lin
AU - Guo, Xiansheng
AU - Ansari, Nirwan
AU - Li, Huiyong
N1 - Funding Information:
Manuscript received April 12, 2019; revised July 12, 2019; accepted July 28, 2019. Date of publication August 1, 2019; date of current version December 11, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61371184, Grant 61671137, Grant 61771114, and Grant 61771316, and in part by the Application Foundation Projects of Science and Technology Department in Sichuan Province under Grant 2018JY0242 and Grant 2018JY0218. (Corresponding author: Xiansheng Guo.) L. Li, X. Guo, and H. Li are with the Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: linli9419@gmail.com; xsguo@uestc.edu.cn; hyli@uestc.edu.cn).
Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61371184, Grant 61671137, Grant 61771114, and Grant 61771316, and in part by the Application Foundation Projects of Science and Technology Department in Sichuan Province under Grant 2018JY0242 and Grant 2018JY0218.
Publisher Copyright:
© 2014 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user's location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.
AB - The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user's location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.
KW - Evaluation model
KW - WiFi fingerprint
KW - group of fingerprints (GOOFs)
KW - indoor localization
KW - multiple supporting sets (MSSs)
UR - http://www.scopus.com/inward/record.url?scp=85076757570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076757570&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2019.2932464
DO - 10.1109/JIOT.2019.2932464
M3 - Article
AN - SCOPUS:85076757570
SN - 2327-4662
VL - 6
SP - 9829
EP - 9840
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 6
M1 - 8784202
ER -