JACIII Vol.26 No.1 pp. 97-106
doi: 10.20965/jaciii.2022.p0097


Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon

Mansur As*1, Hiroshi Shimizu*2, Brahim Benaissa*3, Kaori Yoshida*2,†, and Mario Köppen*4

*1Department of Computer Science, Faculty of Mathematic and Natural Science, Universitas Negeri Medan
Jl. Willem Iskandar, Pasar V, Medan, Sumatera Utara 20221, Indonesia

*2Department of Human Intelligent Systems, Graduate School of Life Science and System Engineering, Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan

*3Department of Mechanical Systems Engineering, Toyota Technological Institute
Design Engineering Lab, 2-12-1 Hisakata, Tempaku Ward, Nagoya, Aichi 468-8511, Japan

*4Department of Creative Informatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan

Corresponding author

July 31, 2021
November 15, 2021
January 20, 2022
indoor localization, Bluetooth low energy, received signal strength, radial basis function

Indoor localization based on Bluetooth low energy (BLE) beacons has been rapidly developed, and many approaches have been developed to achieve higher estimation accuracy. In these methods, the received signal strength (RSS) is the input. However, the measurement of indoor environments is affected easily; the signal may be reflected and attenuated by obstacles such as the human body, walls, and furniture, which creates a challenge for methods based on signal mapping. In this study, BLE signal characteristics are investigated in an indoor localization setting. An experiment is performed using one BLE beacon and multiple receivers installed at different wall and ceiling positions. The raw RSS is observed, and the relationship between the BLE beacon signal strength characteristics against the human body effect as well as the receiver’s placement in the observation area are discussed. Signal mapping is performed, where the signal strength is measured from all receivers simultaneously. The position estimation accuracy is examined based on different data scenarios. The results show that the estimation position estimated by the BLE beacon based on extensive BLE beacon data does not affect the estimation accuracy.

Cite this article as:
Mansur As, Hiroshi Shimizu, Brahim Benaissa, Kaori Yoshida, and Mario Köppen, “Calibration Cost Reduction of Indoor Localization Using Bluetooth Low Energy Beacon,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.1, pp. 97-106, 2022.
Data files:
  1. [1] A. H. Omre and S. Keeping, “Bluetooth low energy: Wireless connectivity for medical monitoring,” J. of Diabetes Science and Technology, Vol.4, No.2, pp. 457-463, 2010.
  2. [2] B. Benaissa, M. Köppen, and K. Yoshida, “Activity and emotion recognition for elderly health monitoring,” Int. J. of Affective Engineering, Article No.IJAE-D-17-00020, 2017.
  3. [3] T. G. Stavropoulos, A. Papastergiou, L. Mpaltadoros, S. Nikolopoulos, and I. Kompatsiaris, “IoT wearable sensors and devices in elderly care: A literature review,” Sensors, Vol.20, No.10, Article No.2826, 2020.
  4. [4] L. Bai, F. Ciravegna, R. Bond, and M. Mulvenna, “A Low Cost Indoor Positioning System Using Bluetooth Low Energy,” IEEE Access, Vol.8, pp. 136858-136871, 2020.
  5. [5] B. Benaissa, F. Hendrichovsky, K. Yishida, M. Koppen, and P. Sincak, “Phone application for indoor localization based on Ble signal fingerprint,” 2018 9th IFIP Int. Conf. on New Technologies, Mobility and Security (NTMS), doi: 10.1109/NTMS.2018.8328729, 2018.
  6. [6] J. Rapiński, D. Zinkiewicz, and T. Stanislawek, “Influence of human body on radio signal strength indicator readings in indoor positioning systems,” Technical Sciences, Vol.2. No.19, pp. 117-127, 2016.
  7. [7] M. K. Balakrishnan, ”Indoor positioning system survey using BLE beacons,” Ph.D. Thesis, Instituto Superior de Engenharia do Porto-ISEP, 2017.
  8. [8] L. Mainetti, L. Patrono, and I. Sergi, “A survey on indoor positioning systems,” 2014 22nd Int. Conf. on Software, Telecommunications and Computer Networks (SoftCOM), pp. 111-120, 2014.
  9. [9] F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Communications Surveys & Tutorials, Vol.21, No.3, pp. 2568-2599, 2019.
  10. [10] M. Kolakowski, “Improving accuracy and reliability of bluetooth low-Energy-Based localization systems using proximity sensors,” Applied Sciences, Vol.9, No.19, Article No.4081, 2019.
  11. [11] J. Powar, C. Gao, and R. Harle, “Assessing the impact of multi-channel BLE beacons on fingerprint-based positioning,” 2017 Int. Conf. on Indoor Positioning and Indoor Navigation (IPIN), doi: 10.1109/IPIN.2017.8115871, 2017.
  12. [12] C. Xiao, D. Yang, Z. Chen, and G. Tan, “3-D BLE indoor localization based on denoising autoencoder,” IEEE Access, Vol.5, pp. 12751-12760, 2017.
  13. [13] A. Thaljaoui, T. Val, N. Nasri, and D. Brulin, “BLE localization using RSSI measurements and iRingLA,” 2015 IEEE Int. Conf. on Industrial Technology (ICIT), pp. 2178-2183, 2015.
  14. [14] B. Benaissa, K. Yoshida, M. Köppen, and F. Hendrichovsky, “Updatable indoor localization based on BLE signal fingerprint,” 2018 Int. Conf. on Applied Smart Systems (ICASS), doi: 10.1109/ICASS.2018.8652035, 2018.
  15. [15] T. Mori, S. Kajioka, T. Uchiya, I. Takumi, and H. Matsuo, “Experiments of position estimation by BLE beacons on actual situations,” 2015 IEEE 4th Global Conf. on Consumer Electronics (GCCE), pp. 683-684, 2015.
  16. [16] S. M. Darroudi and C. Gomez, “Bluetooth low energy mesh networks: A survey,” Sensors, Vol.17, No.7, Article No.1467, 2017.
  17. [17] H. H. Strey, P. Richman, R. Rozensky, S. Smith, and L. Endee, “Bluetooth low energy technologies for applications in health care: Proximity and physiological signals monitors,” 2013 10th Int. Conf. and Expo on Emerging Technologies for a Smarter World (CEWIT), doi: 10.1109/CEWIT.2013.6851347, 2013.
  18. [18] G. Celosia and M. Cunche, “Fingerprinting Bluetooth-low-energy devices based on the generic attribute profile,” Proc. of the 2nd Int. ACM Workshop on Security and Privacy for the Internet-of-Things, pp. 24-31, 2019.
  19. [19] F. Morgado, P. Martins, and F. Caldeira, “Beacons positioning detection, a novel approach,” Procedia Computer Science, Vol.151, pp. 23-30, 2019.
  20. [20] N. Kuxdorf-Alkirata, G. Maus, and D. Brückmann, “Efficient calibration for robust indoor localization based on low-cost BLE sensors,” 2019 IEEE 62nd Int. Midwest Symp. on Circuits and Systems (MWSCAS), pp. 702-705, 2019.
  21. [21] P. Martins, M. Abbasi, F. Sá, J. Cecílio, F. Morgado, and F. Caldeira, “Improving bluetooth beacon-based indoor location and fingerprinting,” J. of Ambient Intelligence and Humanized Computing, pp. 3907-3919, 2019.
  22. [22] S. Subedi and J.-Y. Pyun, “Practical fingerprinting localization for indoor positioning system by using beacons,” J. of Sensors, Vol.2017, doi: 10.1155/2017/9742170, 2017.
  23. [23] L. Kanaris, A. Kokkinis, A. Liotta, and S. Stavrou, “Fusing bluetooth beacon data with Wi-Fi radiomaps for improved indoor localization,” Sensors, Vol.17, No.4, Article No.812, 2017.
  24. [24] L. Zhang, X. Liu, J. Song, C. Gurrin, and Z. Zhu, “A comprehensive study of Bluetooth fingerprinting-based algorithms for localization,” 2013 27th Int. Conf. on Advanced Information Networking and Applications Workshops, pp. 300-305, 2013.
  25. [25] I. Alexander and G. P. Kusuma, “Predicting Indoor Position Using Bluetooth Low Energy And Machine Learning,” Int. J. of Scientific & Technology Research, Vol.8, No.9, pp. 1661-1667, 2019.
  26. [26] S. R. Jondhale and R. S. Deshpande, “GRNN and KF framework based real time target tracking using PSOC BLE and smartphone,” Ad Hoc Networks, Vol.84, pp. 19-28, 2019.
  27. [27] S. Sadowski and P. Spachos, “Optimization of BLE beacon density for RSSI-based indoor localization,” 2019 IEEE Int. Conf. on Communications Workshops (ICC Workshops), doi: 10.1109/ICCW.2019.8756989, 2019.
  28. [28] J. Larsson. “Distance estimation and positioning based on Bluetooth low energy technology,” M.Sc. Thesis, KTH Royal Institute of Technology, 2015.
  29. [29] C. Ke, M. Wu, Y. Chan, and K. Lu, “Developing a BLE beacon-based location system using location fingerprint positioning for smart home power management,” Energies, Vol.11, No.12, Article No.3464, 2018.
  30. [30] H. Namie and O. Suzuki, “Indoor Location Estimation by Bluetooth Low Energy for Pedestrian Navigation,” IEEJ J. of Industry Applications, doi: 10.1541/ieejjia.20003604, 2020.
  31. [31] A. A. Kalbandhe and S. C. Patil, “Indoor positioning system using Bluetooth low energy,” 2016 Int. Conf. on Computing, Analytics and Security Trends (CAST), pp. 451-455, 2016.
  32. [32] S. Kajioka, T. Mori, T. Uchiya, I. Takumi, and H. Matsuo, “Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon,” 2014 IEEE 3rd Global Conf. on Consumer Electronics (GCCE), pp. 337-339, 2014.
  33. [33] F. Della Rosa, M. Pelosi, and J. Nurmi, “Human-induced effects on RSS ranging measurements for cooperative positioning,” Int. J. of Navigation and Observation, Vol.2012, doi: 10.1155/2012/959140, 2012.
  34. [34] H. Hoshi, H. Ishizuka, A. Kobayashi, and A. Minamikawa, “An indoor location estimation using BLE beacons considering movable obstructions,” 2017 10th Int. Conf. on Mobile Computing and Ubiquitous Network (ICMU), doi: 10.23919/ICMU.2017.8330082, 2017.
  35. [35] I. Parewai, M. As, T. Mine, and M. Koeppen, “Identification and Classification of Sashimi Food Using Multispectral Technology,” Proc. of the 2020 2nd Asia Pacific Information Technology Conf., pp. 66-72, 2020.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 20, 2022