Skip to main content

A Hybrid Indoor Localization Framework in an IoT Ecosystem

  • Conference paper
  • First Online:
  • 1267 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 745))

Abstract

The Global Position System (GPS) does not work in the indoor environment because of the satellite signal attenuation. To overcome this lack, we propose a Hybrid Indoor Positioning and Navigation System (HIPNS), based on Li-Fi (Light-Fidelity) localization and optical camera positioning analyses deployed in an indoor environment. The localization approach is based on the fuse of two positioning strategies where the camera-based part is responsible for localizing individuals and recovering their trajectories in zones with low coverage of Li-Fi LEDs. A third-party element is planned to operate in the event of loss of contact. So, the step detection technique and heading estimation are applied in a smartphone-based indoor localization context between two referenced points. The main contribution of this paper focuses on the use of techniques, algorithms, and methods from different spheres of application that generate heterogeneous data. We apply a data integration approach based on REST Web service architecture to allow localization operations in this hybrid indoor positioning system (HIPS). In this work-in-progress paper, we also present a state-of-the-art survey of techniques and algorithms for indoor positioning with the help of smartphones, as well as the main concepts and challenges related to this emergent area.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. ITU (2015) IoT global standards initiative. https://handle.itu.int/11.1002/1000/11559

  2. Brena RF et al (2017) Evolution of indoor positioning technologies: a survey. J Sens 2017, Article ID 26304113

    Google Scholar 

  3. Davidson P, Piche R (2017) A survey of selected indoor positioning methods for smartphones. IEEE Commun Surv Tutor 19(2):1347–1370

    Article  Google Scholar 

  4. Mendoza-Silva GM, Torres-Sospedra J, Huerta J (2019) A meta-review of indoor positioning systems. Sensor 19:4507. https://doi.org/10.3390/s19204507

    Article  Google Scholar 

  5. Zafari F, Gkelias A, Leung KK (2019) A survey of indoor localization systems and technologies. arXiv:1709.01015v3 [cs.NI] 16 jan 2019

  6. Wu X et al (2020) Hybrid LIFI and Wifi networks: a survey. arXiv:2001.04840v1

  7. Rahman ABMM, Li T, Wang Y (2020) Recent advances in indoor localization via visible lights: a survey. Sensors 20(5):1382

    Google Scholar 

  8. Yang S, Ma L, Jia S, Qin D (2020) An improved vision-based indoor positioning method. IEEE Access 8:26941–26949. https://doi.org/10.1109/ACCESS.2020.2968958

    Article  Google Scholar 

  9. Nummiaro K, Koller-Meier E, Van Gool L (2002) Object tracking with an adaptive color-based particle filter. Pattern Recogn, 353–360

    Google Scholar 

  10. Shimada A et al (2006) Dynamic control of adaptive mixture-of-Gaussians background model. In: Video and signal based surveillance, 2006. IEEE-AVSS'06, 2006, pp 5–5

    Google Scholar 

  11. Sun M et al (2019) See-your-room: indoor localization with camera vision. In: Proceedings of the ACM turing celebration conference-China, pp 1–5

    Google Scholar 

  12. Oledcomm (2020) GEOLiFi kit. https://www.oledcomm.net/lifimax-discovery-kit/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ivan Madjarov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nkengue, M.J.P., Madjarov, I., Damoiseaux, J.L., Iguernaissi, R. (2022). A Hybrid Indoor Localization Framework in an IoT Ecosystem. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-33-6893-4_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6892-7

  • Online ISBN: 978-981-33-6893-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics