Development and Implementation of Indoor Location-based Service in Museums Based on Bluetooth Low Energy Standard and Triangulation Algorithm

Year : 2024 | Volume : 02 | Issue : 02 | Page : 1 9
    By

    Aras Khosravi,

  • Younes Seddighi,

  • Parviz Zeaiean Firouzabadi,

  1. Master, Department of Remote Sensing and Geographic Information System, Geography, Kharazmi University, Tehran, Iran
  2. Master, Department of Remote Sensing and Geographic Information System, Geography, Kharazmi University, Tehran, Iran
  3. Professor, Department of Remote Sensing and Geographic Information System, Geography, Kharazmi University, Tehran, Iran

Abstract

Location-based services (LBS) in indoor locations are for providing information needed by users, and this information can be provided to users based on a Geofence. The most important component of location-based service systems is the Global Positioning System (GPS), which does not have the ability to determine the location in an indoor location. In this research, LBS Android software was developed for museums with the possibility of providing multimedia information based on the Geofence concept. The visitor is first positioned by Indoor Positioning System (IPS) in this software, and multimedia information is presented to the visitor by being placed in the Geofence. To determine the user’s location, Bluetooth Low Energy (BLE)-based transmitters were installed, and the Trilateration algorithm was used to develop a two-dimensional positioning system in an indoor location. The main input variables of this algorithm were the distance of the transmitters from the user and the X and Y coordinates related to the installation location of the transmitters. Received Signal Strength Indicator (RSSI) was used to obtain the distance between the visitor and the transmitters, and the coordinates of the transmitters were measured in terms of X and Y dimensions relative to the reference point. The positioning accuracy of the system was evaluated based on Root Mean Square (RMS) and apparent errors in the environment. The final apparent error is equal to 1.02975 and the apparent error range is (0.43–1.44) meters.

Keywords: Location-based services, museums, indoor positioning, triangulation, Bluetooth transmitters

[This article belongs to International Journal of Wireless Security and Networks ]

How to cite this article:
Aras Khosravi, Younes Seddighi, Parviz Zeaiean Firouzabadi. Development and Implementation of Indoor Location-based Service in Museums Based on Bluetooth Low Energy Standard and Triangulation Algorithm. International Journal of Wireless Security and Networks. 2024; 02(02):1-9.
How to cite this URL:
Aras Khosravi, Younes Seddighi, Parviz Zeaiean Firouzabadi. Development and Implementation of Indoor Location-based Service in Museums Based on Bluetooth Low Energy Standard and Triangulation Algorithm. International Journal of Wireless Security and Networks. 2024; 02(02):1-9. Available from: https://journals.stmjournals.com/ijwsn/article=2024/view=168741


References

  1. Basiri A, Lohan ES, Moore T, Winstanley A, Peltola P, Hill C, et al. Indoor location-based services challenges, requirements, and usability of current solutions. Comput Sci Rev. 2017;24:1–12. DOI: 10.1016/j.cosrev.2017.03.002.
  2. Carrasco U, Urbina Coronado PDU, Parto M, Kurfess T. Indoor location service in support of a smart manufacturing facility. Comput Ind. 2018;103:132–40. DOI: 10.1016/j.compind.2018.09.009.
  3. Cheng J, Cai Y, Zhang Q, Cheng J, Yan C. A new three-dimensional indoor positioning mechanism based on wireless LAN. Math Probl Eng. 2014;2014:1–9. DOI: 10.1155/2014/862347.
  4. Corbacho Salas A. Indoor positioning system based on Bluetooth Low Energy. [Bachelor’s Thesis]. Universitat Politècnica de Catalunya; 2014.
  5. Maghdid HS, Lami IA, Ghafoor KZ, Lloret J. Seamless outdoors-indoors localization solutions on smartphones: Implementation and challenges. ACM Comput Surv. 2016;48:1–34. DOI: 10.1145/2871166.
  6. Ni Y, Liu J, Liu S, Bai Y. An indoor pedestrian positioning method using HMM with a fuzzy pattern recognition algorithm in a WLAN fingerprint system. Sensors. 2016;16:1447. DOI: 10.3390/s16091447. PubMed: 27618053.
  7. Orujov F, Maskeliūnas R, Damaševičius R, Wei W, Li Y. Smartphone-based intelligent indoor positioning using fuzzy logic. Future Gener Comput Syst. 2018;89:335–48. DOI: 10.1016/j.future.2018.06.030.
  8. Owusu RKA. Particle filtering and information fusion of innovative location and tracking device targeting GPS hostile environments. 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies, Aalborg, Denmark. 2008. p. 1–7. DOI: 10.1109/ISABEL.2008.4712588.
  9. Röbesaat J, Zhang P, Abdelaal M, Theel O. An improved BLE indoor localization with Kalman-based fusion: An experimental study. Sensors. 2017;17:951. DOI: 10.3390/s17050951. PubMed: 28445421.
  10. Werner M. Indoor Location-based Services: Prerequisites and Foundations. Cham, Switzerland: Springer; 2014. DOI: 10.1007/978-3-319-10699-1.
Regular Issue Subscription Original Research
Volume 02
Issue 02
Received 12/06/2024
Accepted 24/06/2024
Published 23/08/2024
412

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