IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p10943-d1192581.html
   My bibliography  Save this article

Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System

Author

Listed:
  • Bhulakshmi Bonthu

    (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India)

  • Subaji Mohan

    (Institute for Industry and International Programmes, Vellore Institute of Technology, Vellore 632014, India)

Abstract

Wi-Fi-based indoor positioning systems are becoming increasingly prevalent in digital transitions; therefore, ensuring accurate and robust positioning is essential to supporting the growth in demand for smartphones’ location-based services. The indoor positioning system on a smartphone, which is generally based on Wi-Fi received signal strength (RSS) measurements or the fingerprinting comparison technique, uses the K-NN algorithm to estimate the position due to its high accuracy. The fingerprinting algorithm is popular due to its ease of implementation and its ability to produce the desired accuracy. However, in a practical environment, the Wi-Fi signal strength-based positioning system is highly influenced by external factors such as changes in the environment, human interventions, obstacles in the signal path, signal inconsistency, signal loss due to the barriers, the non-line of sight (NLOS) during signal propagation, and the high level of fluctuations in the RSS, which affects location accuracy. In this paper, we propose a method that combines pedestrian dead reckoning (PDR) and Wi-Fi fingerprinting to select a k-node to participate in the K-NN algorithm for fingerprinting-based IPSs. The selected K-node is used for the K-NN algorithm to improve the robustness and overall accuracy. The proposed hybrid method can overcome practical environmental issues and reduces the KNN algorithm’s complexity by selecting the nearest neighbors’ search space for comparison using the PDR position estimate as the reference position. Our approach provides a sustainable solution for indoor positioning systems, reducing energy consumption and improving the overall environmental impact. The proposed method has potential applications in various domains, such as smart buildings, healthcare, and retail. The proposed method outperforms the traditional KNN algorithm in our experimental condition since its average position error is less than 1.2 m, and provides better accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10943-:d:1192581
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/10943/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/14/10943/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Haoran Zhuang & Jian Zhang & Sivaparthipan C. B. & Bala Anand Muthu, 2020. "Sustainable Smart City Building Construction Methods," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

    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. Sinha, Shreya & Narain, Nivedita & Bhanjdeo, Arundhita, 2022. "Building back better? Resilience as wellbeing for rural migrant households in Bihar, India," World Development, Elsevier, vol. 159(C).
    2. Melendres, Clark N. & Lee, Ji Yong & Kim, Bongkyun & Nayga, Rodolfo M., 2022. "Increasing yield and farm income of upland farmers: The case of Panay Island Upland Sustainable Rural Development Project in the Philippines," Journal of Asian Economics, Elsevier, vol. 82(C).
    3. Nguyet Anh Dang & Rubianca Benavidez & Stephanie Anne Tomscha & Ho Nguyen & Dung Duc Tran & Diep Thi Hong Nguyen & Ho Huu Loc & Bethanna Marie Jackson, 2021. "Ecosystem Service Modelling to Support Nature-Based Flood Water Management in the Vietnamese Mekong River Delta," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    4. 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.
    5. Brown, Austin L. & Fleming, Kelly L. & Lipman, Timothy & Fulton, Lew & Saphores, Jean Daniel & Tal, Gil & Murphy, Colin W & Shaheen, Susan & Austin, Bernadette & Garcia Sanchez, Juan Carlos & Martin, , 2020. "Carbon Neutrality Study 1:Driving California’s Transportation Emissions to Zero," Institute of Transportation Studies, Working Paper Series qt5zb1238j, Institute of Transportation Studies, UC Davis.
    6. Obiamaka A. Nwobu & Collins C. Ngwakwe, 2020. "Corporate Responsibility Reporting in Africa: The Effect of Macroeconomic Indicators and Political Regime," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(10), pages 1203-1219, October.
    7. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Jamal, Taskin & Dyduch, Joanna & Arif, M.T. & Manoj Kumar, Nallapaneni & Shafiullah, GM & Chopra, Shauhrat S. & Nadarajah, Mithulananthan, 2021. "Envisioning the UN Sustainable Development Goals (SDGs) through the lens of energy sustainability (SDG 7) in the post-COVID-19 world," Applied Energy, Elsevier, vol. 292(C).
    8. Tarek Hatem Al-Rimawi & Michael Nadler, 2023. "Evaluating Cities and Real Estate Smartness and Integration: Introducing a Comprehensive Evaluation Framework," Sustainability, MDPI, vol. 15(12), pages 1-31, June.
    9. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    10. Beryl Wong Xin Xian & Yani Rahmawati & Al-Hussein Mohammed Hassan Al-Aidrous & Christiono Utomo & Noor Amila Wan Abdullah Zawawi & Raflis, 2021. "Value-Based Decision to Redevelop Transportation Facilities: A Case Study of an Abandoned Airport," Sustainability, MDPI, vol. 13(9), pages 1-24, April.
    11. Li, Shengping & Rismanchi, Behzad & Aye, Lu, 2022. "A simulation-based bottom-up approach for analysing the evolution of residential buildings’ material stocks and environmental impacts – A case study of Inner Melbourne," Applied Energy, Elsevier, vol. 314(C).

    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:15:y:2023:i:14:p:10943-:d:1192581. 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.