Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Received Signal Strength Indicator Based Localization of Bluetooth Devices Using Trilateration: An Improved Method for the Visually Impaired People

Authors: Muhammad Irfan Aziz, Thomas Owens, Uzair Khaleeq uz Zaman


The instantaneous and spatial localization for visually impaired people in dynamically changing environments with unexpected hazards and obstacles, is the most demanding and challenging issue faced by the navigation systems today. Since Bluetooth cannot utilize techniques like Time Difference of Arrival (TDOA) and Time of Arrival (TOA), it uses received signal strength indicator (RSSI) to measure Receive Signal Strength (RSS). The measurements using RSSI can be improved significantly by improving the existing methodologies related to RSSI. Therefore, the current paper focuses on proposing an improved method using trilateration for localization of Bluetooth devices for visually impaired people. To validate the method, class 2 Bluetooth devices were used along with the development of a software. Experiments were then conducted to obtain surface plots that showed the signal interferences and other environmental effects. Finally, the results obtained show the surface plots for all Bluetooth modules used along with the strong and weak points depicted as per the color codes in red, yellow and blue. It was concluded that the suggested improved method of measuring RSS using trilateration helped to not only measure signal strength affectively but also highlighted how the signal strength can be influenced by atmospheric conditions such as noise, reflections, etc.

Keywords: Bluetooth, indoor/outdoor localization, received signal strength indicator, visually impaired.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 663


[1] World Health Organization. (2018) Visual impairment and blindness. (Online) Available:
[2] R. G. Golledge, J. M. Loomis, R. L. Klatzky A. Flury, and X. L. Yang, “Designing a personal guidance system to aid navigation without sight: progress on the GIS component,” International Journal of Geographical Information Systems, vol. 5(4), pp. 373-395, 1991.
[3] S. Halde and A. Ghosal, “Mobility-assisted localization techniques in wireless sensor networks: issues, challenges and approaches,” Cooperative Robots and Sensor Networks, pp. 43–64, 2014.
[4] F. Pflaum, S. Erhardt, R. Weigel, and A. Koelpin, “RSSI-based localization with minimal infrastructure using multivariate statistic techniques,” in Proc. of IEEE Topical Conference on Wireless Sensors and Sensor Networks, 2017, p. 69-72.
[5] R. Kowalik and S. Kwasniewski, “Navigator: A Talking GPS Receiver for the Blind,” Lecture Notes in Computer Science, Computers Helping People with Special Needs, vol. 3118, pp. 446-449, 2004.
[6] J. Loomis, R. Golledge, and R. Klatzky, “GPS-based navigation systems for the visually impaired,” in Fundamentals of Wearable Computers and Augmented Reality, W. Barfield and T. Caudell Eds, pp. 429-446, 2001.
[7] Q. Wang, I. Balasingham, M. Zhang, and X. Huang, “Improving RSS-Based Ranging in LOS-NLOS Scenario Using GMMs,” IEEE Communications Letters, vol. 15(10), pp. 1065–1067, 2011.
[8] Z. Farid, R. Nordin, and M. Ismail, “Recent advances in wireless indoor localization techniques and system,” Journal of Computer Networks and Communications, doi: 10.1155/2013/185138, 2013.
[9] Dabrowski A, Kardys P, Marciniak T (2005) Bluetooth technology applications dedicated to supporting blind and hearing as well as speech handicapped people. In ELMAR 47th International Symposium: 295–298.
[10] Kriz P, Maly F, Kozel T (2016) Improving indoor localization using Bluetooth low energy beacons. Mobile Information Systems, doi: 10.1155/2016/2083094.
[11] Altini M, Brunelli D, Farella E, Benini L (2010) Bluetooth indoor localization with multiple neural networks. 5th IEEE International Symposium on Wireless Pervasive Computing, doi: 10.1109/ISWPC.2010.5483748.
[12] H. Chen and K. Lin, “An improved method for free-space antenna-factor measurement by using the MUSIC algorithm,” IEEE Transactions on Electromagnetic Compatibility, vol. 53(2), pp. 274–282, 2011.
[13] K. Agarwal and X. Chen, “Applicability of MUSIC-type imaging in two-dimensional electromagnetic inverse problems,” IEEE Transactions on Antennas Propagation, vol. 56(10), pp. 3217–3223, 2008.
[14] V. Beamspace, K. T. Wong, and M. D. Zoltowski, “Self-initiating MUSIC-based direction finding in underwater acoustic particle,” IEEE Journal of Oceanic Engineering, vol. 25(2), pp. 262–273, 2000.
[15] S. Henault, Y. M. M. Antar, S. Rajan, R. Inkol, and S. Wang, “Impact of mutual coupling on wideband Adcock direction finders,” Canadian conference on Electrical and Computer Engineering, doi: 10.1109/CCECE.2008.4564755, 2008.
[16] M. Ibrahim and M. Youssef, “CellSense: An accurate energy-efficient GSM positioning system,” IEEE Transactions on Vehicular Technology, vol. 61(1), pp. 286–296, 2012.
[17] K. Wu, J. Xiao, S. Member, Y. Yi, and D. Chen, “CSI-based indoor localization,” IEEE Transactions on Parallel and Distributed Systems, vol. 24(7), pp. 1300–1309, 2013.
[18] P. K. Sahu, E. H. K. Wu, and J. Sahoo, “DuRT: Dual RSSI trend-based localization for wireless sensor networks,” IEEE Sensors Journal, vol. 13(8), pp. 3115–3123, 2013.
[19] Y. Chen, D. Lymberopoulos, J. Liu, B. Priyantha, “Indoor localization using FM signals,” IEEE Transactions on Mobile Computing, vol. 12(8), pp. 1502–1517, 2013.
[20] J. J. M. Diaz, R. D. A. Mau, R. B. Soares, E. F. Nakamura, and C. M. S. Figueiredo, “Bluepass: an indoor Bluetooth-based localization system for mobile applications,” IEEE Symposium on Computers and Communications, doi: 10.1109/ISCC.2010.5546506, 2010.
[21] A. K. M. M. Hossain and W. S. Soh, “A comprehensive study of bluetooth signal parameters for localization,” IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, doi: 10.1109/PIMRC.2007.4394215, 2007.
[22] S. Feldmann, K. Kyamakya, A. Zapater, and Z. Lue, “An indoor Bluetooth-based positioning system: concept, implementation and experimental evaluation,” in International Conference on Wireless Networks, pp. 109–113, 2003.
[23] M. Altini, D. Brunelli, E. Farella, and L. Benini, “Bluetooth indoor localization with multiple neural networks,” 5th IEEE International Symposium on Wireless Pervasive Computing, doi: 10.1109/ISWPC.2010.5483748, 2010.
[24] D. Li and J. Wang, “Research of indoor local positioning based on Bluetooth technology,” in 5th International Conference on Wireless Communications, Networking and Mobile Computing, doi: 10.1109/WICOM.2009.5302300, 2009.
[25] S. Bohonos, A. Lee, A. Malik, C. Thai, and R. Manduchi, “Universal real-time navigational assistance (URNA): an urban bluetooth beacon for the blind,” in Proc. of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments, pp. 83–88, 2007.
[26] X. Liu, H. Makino, S. Kobayashi, and Y. Maeda, “Design of an Indoor Self-Positioning System for the Visually Impaired-Simulation with RFID and Bluetooth in a Visible Light Communication System,” in Engineering in Medicine and Biology Society, EMBS, 29th Annual International Conference of the IEEE, pp. 1655–1658, 2007.