Advertisement

WITS 2020 pp 3-13 | Cite as

A Hybrid Indoor Localization Framework in an IoT Ecosystem

Conference paper
  • 23 Downloads
Part of the Lecture Notes in Electrical Engineering book series (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.

Keywords

Indoor navigation Li-Fi-based localization Scene analysis Smartphone-based positioning IoT ecosystem 

References

  1. 1.
    ITU (2015) IoT global standards initiative. https://handle.itu.int/11.1002/1000/11559
  2. 2.
    Brena RF et al (2017) Evolution of indoor positioning technologies: a survey. J Sens 2017, Article ID 26304113Google Scholar
  3. 3.
    Davidson P, Piche R (2017) A survey of selected indoor positioning methods for smartphones. IEEE Commun Surv Tutor 19(2):1347–1370CrossRefGoogle Scholar
  4. 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/s19204507CrossRefGoogle Scholar
  5. 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. 6.
    Wu X et al (2020) Hybrid LIFI and Wifi networks: a survey. arXiv:2001.04840v1
  7. 7.
    Rahman ABMM, Li T, Wang Y (2020) Recent advances in indoor localization via visible lights: a survey. Sensors 20(5):1382Google Scholar
  8. 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.2020.2968958CrossRefGoogle Scholar
  9. 9.
    Nummiaro K, Koller-Meier E, Van Gool L (2002) Object tracking with an adaptive color-based particle filter. Pattern Recogn, 353–360Google Scholar
  10. 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 5–5Google Scholar
  11. 11.
    Sun M et al (2019) See-your-room: indoor localization with camera vision. In: Proceedings of the ACM turing celebration conference-China, pp 1–5Google Scholar
  12. 12.

Copyright information

© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.Aix Marseille Univ, Université de Toulon, CNRS, LISMarseilleFrance

Personalised recommendations