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Indoor Positioning: Novel Approach for Bluetooth Networks using RSSI Smoothing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
J.A.D.C Anuradha Jayakody, Shashika Lokuliyana, Dinusha Chathurangi, Demini Vithana

Anuradha J A D C Jayakody, Shashika Lokuliyana, Dinusha Chathurangi and Demini Vithana. Article: Indoor Positioning: Novel Approach for Bluetooth Networks using RSSI Smoothing. International Journal of Computer Applications 137(13):26-32, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {J.A.D.C Anuradha Jayakody and Shashika Lokuliyana and Dinusha Chathurangi and Demini Vithana},
	title = {Article: Indoor Positioning: Novel Approach for Bluetooth Networks using RSSI Smoothing},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {137},
	number = {13},
	pages = {26-32},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In recent years, localization and navigation have been important topics in research. The most popular navigation system is an outdoor navigation with “GPS”; however, the positioning within indoor environments is not possible with “GPS”, so it is resulting in limited operation for indoor environments. In order to overcome this limitation, this paper discussed Bluetooth Low Energy technology based localization model. The “BLE” provides several major forms of parameters linked to location estimation such as “RSSI” and “LQI”. In real time applications such as object tracking and distance estimates require continuous reception of RSSI measurements to estimate the position of the object accurately. Nevertheless, on that point, there are some constraints such as signal attenuation, signal loss, multipath effects, temperature, reflection, a human body and other communication signals. Hence, this research work considered the “RSSI” smoothing approaches. Although there are so many solutions, no RSSI smoothing method has been recognized as a standard method. This paper presents a Feedback filter together with shifting technique at distance domain to reduce fluctuations of the real-time signals. Experiments show that the probability of locating errorless and it is better than the other existing interference avoidance algorithms.


  1. Dr. Rainer Mautz, Indoor Positioning Technologies. Institute of Geodesy and Photogrammetry, Department of Civil, Environmental and Geomantic Engineering, ETH Zurich, February 2012.
  2. Manh Hung V. Le, Indoor Navigation System for Handheld Devices. Worcester Polytechnic Institute Worcester, Massachusetts, USA, October 2009.
  3. Zhen Fang, Zhan Zhao, Dao Geng, Yundong Xuan, Lidong Du,Xun Xue Cui, “RSSI Variability Characterization and Calibration Method in Wireless Sensor Network”, International Conference on Information and Automation, June 20-23, 2010, pp.1532-1537.
  4. A. Awad, T. Frunzke, and F. Dressler, “Adaptive distance estimation and localization in won using RSSI measures,” in DSD, 2007, pp.471–478.
  5. H. Y. Shi, “A new weighted centroid localization algorithm based on RSSI,” in Proc. International Conference on Information andAutomation, 2012, pp. 137-141.
  6. Pei-gang SUN, Hai ZHAO, Ding-ding LUO, “Research onRSSI-based Location in Smart Space,” ACTA ELECTRONICASINICA, 2007, 35(7), pp:1240- 1245.
  7. Diaz, J. M.; Maues, Rodrigo de A.; Soares, Rodrigo B.;Nakamura, Eduardo F., Figueiredo, Carlos M. S."Bluepass:An indoor Bluetooth-based localization system for mobile applications, “Computers and Communications (ISCC), 2010 IEEE Symposium on , vol., no., pp.778-783, 22-25 June 2010 doi: 10.1109/ISCC.2010.5546506.
  8. Yim J., Jeong S., Gwon K. and Joo J. (2010) Improvement of Kalman filters for WLAN based indoor tracking. ExpertSystems with Applications, 37(1), pp.426-433.
  9. Kotanen A., Hännikäinen M., Leppäkoski H. and Hämäläinen T.D., "Experiments on local positioning with Bluetooth," International Conference on Information Technology Computers and Communications, pp.297, 2003. DOIs 10.1109/ITCC.2003.1197544.
  10. Mao G., Baris, Fidan and Anderson D.O., “Wireless sensor network localization techniques,” The International Journal of Computer and Telecommunications Networking.
  11. Avanti K., “Comparative study of RSS-based collaborative localization methods in wireless sensor networks,” MSc Thesis, Toulouse School of Graduate Studies, 2006.
  12. Roxie A., Gaber J., Wack M., and Nait-S.M.A., “Survey of wireless relocation techniques,” IEEE Workshop on Service Discovery and Composition in Ubiquitous and Pervasive Environments (SUPE), 2007. DOIs, 10.1109/GLOCOMW.2007.4437809.
  13. Zhou S. and Pollard J.K., “Position measurement using Bluetooth,” IEEE Transactions on Consumer Electronics, vol.52, no.2, p.555, 2006. DOIs, 10.1109/TCE.2006.1649679.
  14. O.Oguejiofor, A.Andiedu, H.Ejiofor, and A.Okolibe,(2013)" Trilateration Based Localization Algorithm for Wireless Sensor Network" IJISME.ISSN: 2319-6386, Volume-1, Issue-10.Availabe:http://www.
  15. L. Pei et al., ”Inquiry-based Bluetooth Indoor Positioning via RSSI Probability Distributions”, Second International Conference on Advances in Satellite and Space Communications, 2010, pp. 151-156.


LQI, BLE, RSSI, Feedback Filter, Shifting Technique.