Visitor Behavior Analysis based on Large-scale Wi-Fi Location Data

被引：2
|
作者：
Maruta, Masaki [1 ]
Sano, Yuta [1 ]
Yamaguchi, Kohei [1 ]
Mine, Tsunenori [2 ]
机构：
[1] Kyushu Univ, Grad Sch Informat Sci & Elect Engn, Nishi Ku, 744 Motooka, Fukuoka 8190395, Japan
[2] Kyushu Univ, Fac Informat Sci & Elect Engn, Nishi Ku, Fukuoka 8190395, Japan
来源：
2015 IIAI 4TH INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS (IIAI-AAI) | 2015年
关键词：
Wi-Fi location data; visitor estimation; category label; clustering; K-Means; booth vector;
D O I：
10.1109/IIAI-AAI.2015.194
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper aims to interpret visitor behaviors by analyzing a large scale Wi-Fi location data obtained at Interop Tokyo 2014. We first detected a situation of a visitor's stay at a booth, calculated the sojourn time of the visitor and represented each visitor as a booth vector whose element value is the total sojourn time of the visitor at a booth. Then, we classified the visitors by k-Means. By using category labels of each booth, we analyzed characteristics of clusters. The results illustrate that some categories appear at the top rank of most clusters, and the area of the booths included in each cluster is mostly some specific small one, not the entire one.
引用
收藏
页码：55 / 60
页数：6
相关论文
共 50 条
  • [21] Indoor Location with Wi-Fi Fingerprinting
    Pritt, Noah
    2013 IEEE (AIPR) APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP: SENSING FOR CONTROL AND AUGMENTATION, 2013,
  • [22] Estimating location using Wi-Fi
    Yang, Qiang
    Pan, Sinno Jialin
    Zheng, Vincent Wenchen
    IEEE INTELLIGENT SYSTEMS, 2008, 23 (01) : 8 - 13
  • [23] Wi-Fi and Super Wi-Fi Co-location for Exploiting Spectrum Heterogeneity
    Shin, Kyubo
    Kim, Hyoil
    Chun, Se Young
    Shin, Dong-Hoon
    2015 INTERNATIONAL CONFERENCE ON ICT CONVERGENCE (ICTC), 2015, : 674 - 679
  • [24] Evaluating location predictors with extensive Wi-Fi mobility data
    Song, LB
    Kotz, D
    Jain, R
    He, XN
    IEEE INFOCOM 2004: THE CONFERENCE ON COMPUTER COMMUNICATIONS, VOLS 1-4, PROCEEDINGS, 2004, : 1414 - 1424
  • [25] Augmented Wi-Fi: An AI-based Wi-Fi Management Framework for Wi-Fi/LTE Coexistence
    Soto, Paola
    Camelo, Miguel
    Fontaine, Jaron
    Girmay, Merkebu
    Shahid, Adnan
    Maglogiannis, Vasilis
    De Poorter, Eli
    Moerman, Ingrid
    Botero, Juan F.
    Latre, Steven
    2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), 2020,
  • [26] A Wi-Fi Indoor Positioning Modeling Based on Location Fingerprint and Cluster Analysis
    Long, Zhili
    Men, Xuanyu
    Niu, Jin
    Zhou, Xing
    Ma, Kuanhong
    COMPUTER VISION SYSTEMS, ICVS 2017, 2017, 10528 : 336 - 345
  • [27] A hierarchical auxiliary deep neural network architecture for large-scale indoor localization based on Wi-Fi fingerprinting
    Cha, Jaehoon
    Lim, Enggee
    APPLIED SOFT COMPUTING, 2022, 120
  • [28] Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks
    Carballal, Adrian
    Galego-Carro, J. Pablo
    Rodriguez- Fernandez, Nereida
    Fernandez-Lozano, Carlos
    PEERJ COMPUTER SCIENCE, 2022, 8
  • [29] Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks
    Carballal A.
    Galego-Carro J.P.
    Rodriguez-Fernandez N.
    Fernandez-Lozano C.
    PeerJ Computer Science, 2022, 8
  • [30] Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks
    Kim, Kyeong Soo
    Wang, Ruihao
    Zhong, Zhenghang
    Tan, Zikun
    Song, Haowei
    Cha, Jaehoon
    Lee, Sanghyuk
    FIBER AND INTEGRATED OPTICS, 2018, 37 (05) : 277 - 289
12345