IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i3p1183-d485712.html
   My bibliography  Save this article

A Structure Landmark-Based Radio Signal Mapping Approach for Sustainable Indoor Localization

Author

Listed:
  • Tao Liu

    (College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450002, China
    Key Laboratory of New Materials and Facilities for Rural Renewable Energy (MOA of China), Henan Agricultural University, Zhengzhou 450002, China)

  • Xing Zhang

    (Guangdong Key Laboratory of Urban Informatics, the School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, the School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
    MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, the School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China)

  • Huan Zhang

    (Key Laboratory of New Materials and Facilities for Rural Renewable Energy (MOA of China), Henan Agricultural University, Zhengzhou 450002, China)

  • Nadeem Tahir

    (Key Laboratory of New Materials and Facilities for Rural Renewable Energy (MOA of China), Henan Agricultural University, Zhengzhou 450002, China)

  • Zhixiang Fang

    (State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430072, China)

Abstract

Low cost and high reproducible is a key issue for sustainable location-based services. Currently, Wi-Fi fingerprinting based indoor positioning technology has been widely used in various applications due to the advantage of existing wireless network infrastructures and high positioning accuracy. However, the collection and construction of signal radio map (a basis for Wi-Fi fingerprinting-based localization) is a labor-intensive and time-cost work, which limit their practical and sustainable use. In this study, an indoor signal mapping approach is proposed, which extracts fingerprints from unknown signal mapping routes to construct the radio map. This approach employs special indoor spatial structures (termed as structure landmarks) to estimate the location of fingerprints extracted from mapping routes. A learning-based classification model is designed to recognize the structure landmarks along a mapping route based on visual and inertial data. A landmark-based map matching algorithm is also developed to attach the recognized landmarks to a map and to recover the location of the mapping route without knowing its initial location. Experiment results showed that the accuracy of landmark recognition model is higher than 90%. The average matching accuracy and location error of signal mapping routes is 96% and 1.2 m, respectively. By using the constructed signal radio map, the indoor localization error of two algorithms can reach an accuracy of 1.6 m.

Suggested Citation

  • Tao Liu & Xing Zhang & Huan Zhang & Nadeem Tahir & Zhixiang Fang, 2021. "A Structure Landmark-Based Radio Signal Mapping Approach for Sustainable Indoor Localization," Sustainability, MDPI, vol. 13(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1183-:d:485712
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/3/1183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/3/1183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jong Hyuk Park & Han-Chieh Chao, 2017. "Advanced IT-Based Future Sustainable Computing," Sustainability, MDPI, vol. 9(5), pages 1-4, May.
    2. Yunsick Sung, 2016. "RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments," Sustainability, MDPI, vol. 8(11), pages 1-9, November.
    3. Xingquan Cai & Yufeng Gao & Mengxuan Li & Wei Song, 2016. "Infrared Human Posture Recognition Method for Monitoring in Smart Homes Based on Hidden Markov Model," Sustainability, MDPI, vol. 8(9), pages 1-17, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sorena Vosoughkhosravi & Amirhosein Jafari, 2022. "Developing A Conceptual Passive Contact Tracing System for Commercial Buildings Using WiFi Indoor Positioning," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    2. Bhulakshmi Bonthu & Subaji Mohan, 2023. "Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jong Hyuk Park & Han-Chieh Chao, 2017. "Advanced IT-Based Future Sustainable Computing," Sustainability, MDPI, vol. 9(5), pages 1-4, May.
    2. Sewoong Hwang & Zoonky Lee & Jonghyuk Kim, 2019. "Real-Time Pedestrian Flow Analysis Using Networked Sensors for a Smart Subway System," Sustainability, MDPI, vol. 11(23), pages 1-16, November.
    3. Yoon-Soo Shin & Junhee Kim, 2022. "A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space," Sustainability, MDPI, vol. 14(13), pages 1-13, June.
    4. Dong Myung Lee & Boney Labinghisa, 2019. "Indoor localization system based on virtual access points with filtering schemes," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
    5. Jonghyuk Kim & Hyunwoo Hwangbo & Sung Jun Kim & Soyean Kim, 2019. "Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    6. Jorge Morato & Sonia Sanchez-Cuadrado & Ana Iglesias & Adrián Campillo & Carmen Fernández-Panadero, 2021. "Sustainable Technologies for Older Adults," Sustainability, MDPI, vol. 13(15), pages 1-35, July.
    7. Hosun Ryou & Han Hee Bae & Hee Soo Lee & Kyong Joo Oh, 2020. "Momentum Investment Strategy Using a Hidden Markov Model," Sustainability, MDPI, vol. 12(17), pages 1-16, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1183-:d:485712. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.