TY - GEN
T1 - Projection-induced access point deployment for fingerprint-based indoor positioning
AU - Pu, Qiaolin
AU - NG, Joseph K Y
AU - Liu, Kai
N1 - Funding Information:
This work was supported in part by the HKBU Research Centre for Ubiquitous Computing; the HKBU Institute of Computational and Theoretical Studies; the National Natural Science Foundation of China under Grant No.61872049 and No. 61572088; the Frontier Interdisciplinary Research Funds for the Central Universities (Project No. 2018CDQYJSJ0034); and Chongqing Application Foundation and Research in Cutting-edge Technologies (cstc2017jcyjAX0026). (Corresponding author: Kai Liu)
PY - 2019/8
Y1 - 2019/8
N2 - Location information and positioning technology are important to many of the emerging Internet of Things (IoT) applications, and WLAN-based positioning is one of the promising solutions due to the prevalence of Access Points (APs). Nevertheless, different deployments of APs may have significant impact on positioning performance as it may generate different distribution of signal features in surrounding environments, which form the basis of fingerprint-based localization. Current efforts on AP deployment mainly focused on enlarging the probabilities of small errors while ignoring the probabilities of big errors. However, big error could significantly affect the user's experience so that it should be paid more attention. Therefore, in this work, we propose a projection-induced AP deployment approach, whose principle is decreasing the probabilities of big errors. Specifically, firstly, when constructing objective function, unlike the conventional approaches which considered all Received Signal Strength (RSS) vectors collected in every Reference Point (RP), we do outlier detection using K-Nearest Neighbors (KNN) graph previously. Secondly, we solve the defined objective function from the projection perspective rather than search algorithms, which would bring computing consumption with iterations. Finally, we build the system prototype and implement in our environment and the experimental results demonstrate the effectiveness and the efficiency of the proposed AP deployment solution.
AB - Location information and positioning technology are important to many of the emerging Internet of Things (IoT) applications, and WLAN-based positioning is one of the promising solutions due to the prevalence of Access Points (APs). Nevertheless, different deployments of APs may have significant impact on positioning performance as it may generate different distribution of signal features in surrounding environments, which form the basis of fingerprint-based localization. Current efforts on AP deployment mainly focused on enlarging the probabilities of small errors while ignoring the probabilities of big errors. However, big error could significantly affect the user's experience so that it should be paid more attention. Therefore, in this work, we propose a projection-induced AP deployment approach, whose principle is decreasing the probabilities of big errors. Specifically, firstly, when constructing objective function, unlike the conventional approaches which considered all Received Signal Strength (RSS) vectors collected in every Reference Point (RP), we do outlier detection using K-Nearest Neighbors (KNN) graph previously. Secondly, we solve the defined objective function from the projection perspective rather than search algorithms, which would bring computing consumption with iterations. Finally, we build the system prototype and implement in our environment and the experimental results demonstrate the effectiveness and the efficiency of the proposed AP deployment solution.
KW - Access point deployment
KW - Knn graph
KW - Positioning
KW - Projection
KW - WLAN
UR - http://www.scopus.com/inward/record.url?scp=85083574214&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00208
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00208
M3 - Conference contribution
AN - SCOPUS:85083574214
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 1093
EP - 1100
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - IEEE
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -