Monitoring Human Movement in Building Using Bluetooth Low Energy

Authors

  • Jeffrey Hoa Bina Nusantara University
  • Benfano Soewito Bina Nusantara University

DOI:

https://doi.org/10.21512/commit.v12i2.4963

Keywords:

Tracking System, Bluetooth Low Energy (BLE), Smartphone, Indoor Positioning System (IPS), Trilateration

Abstract

In recent years, smartphones have become more popular. Along with the increasing number of smartphone users, more features are requested by the users which can be used to solve their daily life problems. One of the most popular applications is related to location-based detection. Almost all smartphones have a feature of Global Positioning System (GPS). It can help users to go from one place to another place without getting lost by connecting to Google Map. However, there are some limitations to the GPS. When the GPS is used inside a building, it is difficult to search where the users are located, on which floor or room they are, and how to access the room inside the building. It is because GPS cannot show the user which level or room location the users are currently at. One of the possible solutions is using an Indoor Positioning System (IPS) which can detect an object or person inside a building by producing some signals that can be received by smartphones. This research aims to develop an application to monitor the movement of humans inside a building with low cost and low energy by using Android smartphones as the medium. It can get the coordinate of location by using Bluetooth Low Energy (BLE) beacon inside the building. It can also be used to detect multiple users in the same room.

Dimensions

Plum Analytics

Author Biographies

Jeffrey Hoa, Bina Nusantara University

Computer Science Department, BINUS Graduate Program - Master of Computer Science

Benfano Soewito, Bina Nusantara University

Computer Science Department, BINUS Graduate Program - Master of Computer Science

References

J. Duque Domingo, C. Cerrada, E. Valero, and J. A. Cerrada, “An improved indoor positioning system using RGB-D cameras and wireless networks for use in complex environments,” Sensors, vol. 17, no. 10, p. 2391, 2017.

S. Puri, “Indoor positioning system using bluetooth,” International Journal of Engineering Research, vol. 4, no. 5, pp. 244–247, 2015.

J. Luo, L. Fan, and H. Li, “Indoor positioning systems based on visible light communication: State of the art,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2871–2893, 2017.

Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 13–32, 2009.

V. Cant´on Paterna, A. Calveras Aug´e, J. Paradells Aspas, and M. A. P´erez Bullones, “A bluetooth low energy indoor positioning system with channel diversity, weighted trilateration and kalman filtering,” Sensors, vol. 17, no. 12, p. 2927, 2017.

F. S. Danis¸ and A. T. Cemgil, “Model-based localization and tracking using bluetooth lowenergy beacons,” Sensors, vol. 17, no. 11, p. 2484, 2017.

M. H. Vargas, “Indoor navigation using bluetooth low energy (BLE) beacons,” 2016, Bachelor’s thesis. Turku University of Applied Sciences.

N. Rajagopal, S. Chayapathy, B. Sinopoli, and A. Rowe, “Beacon placement for range-based indoor localization,” in 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). Alcala de Henares, Spain: IEEE, Oct. 4–7, 2016, pp. 1–8.

GSMA (2014), A guide to bluetooth beacons. [Online]. Available: https://bit.ly/2LT9skV

J. Paek, J. Ko, and H. Shin, “A measurement study of BLE iBeacon and geometric adjustment scheme for indoor location-based mobile applications,” Mobile Information Systems, vol. 2016, pp. 1–13, 2016.

R. Allen, N. MacMillan, D. Marinakis, R. I. Nishat, R. Rahman, and S. Whitesides, “The range beacon placement problem for robot navigation,” in 2014 Canadian Conference on Computer and Robot Vision. Montreal, QC, Canada: IEEE, May 6–9, 2014, pp. 151–158.

J. Larsson, “Distance estimation and positioning based on bluetooth low energy technology,” Master’s thesis, KTH Royal Institute of Technology, 2015.

J. R¨obesaat, P. Zhang, M. Abdelaal, and O. Theel, “An improved BLE indoor localization with Kalman-based fusion: An experimental study,” Sensors, vol. 17, no. 5, p. 951, 2017.

X. Zhu and Y. Feng, “RSSI-based algorithm for indoor localization,” Communications and Network, vol. 5, no. 02, p. 37, 2013.

G. de Blasio, A. Quesada-Arencibia, C. R. Garc´ıa, J. M. Molina-Gil, and C. Caballero-Gil, “Study on an indoor positioning system for harsh environments based on wi-fi and bluetooth low energy,” Sensors, vol. 17, no. 6, p. 1299, 2017.

S. Chai, R. An, and Z. Du, “An indoor positioning algorithm using bluetooth low energy RSSI,” in 2016 International Conference on Advanced Materials Science and Environmental Engineering, Chiang Mai, Thailand, June 26–27, 2016, pp. 276–278.

Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with bluetooth low energy beacons,” Sensors, vol. 16, no. 5, p. 596, 2016.

Downloads

Published

2018-10-31
Abstract 1611  .
PDF downloaded 373  .