Skip to main content
SpringerLink
Log in

Design and Initial Evaluation of Bluetooth Low Energy Separate Channel Fingerprinting

  • Chapter
  • First Online:
New Trends in E-service and Smart Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 742))

Abstract

Bluetooth Low Energy (BLE) based localization is a next candidate for indoor localization. In this paper, we propose a new BLE-based fingerprinting localization scheme that improves localization accuracy. BLE is a narrow bandwidth communication that is highly affected by frequency selective fading. Frequency selective fading is mainly caused by multipaths between a transmitter and receiver, which are dependent on channels. We utilize channel specific features by separately measure received signal strength (RSS) on different channels to improve localization accuracy. BLE standards provide no API to retrieve channel information of incoming packets. We therefore developed a separate channel advertising scheme to separately measure RSS on different channels. To demonstrate the feasibility of the separate channel fingerprinting, we conducted preliminary experiments as well as initial evaluations. Experimental evaluations demonstrated that the separate channel fingerprinting improves localization accuracy by approximately 12%.

A conference paper [11] containing preliminary results of this paper appeared in IIAI AAI ESKM 2016.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedongs of the IEEE International Conference on Computer Communications (INFOCOM), pp. 775–784 (2000)

    Google Scholar 

  2. Bluetooth Special Interest Group: Core specification v4.0 (2010). http://www.bluetooth.com/

  3. Bulusu, N., Heidemann, J., Estrin, D.: GPS-less low-cost outdoor localization for very small devices. IEEE Pers. Commun. Mag. 7(5), 28–34 (2000)

    Article  Google Scholar 

  4. Chen, L., Pei, L., Kuusniemi, H., Chen, Y., Kröger, T., Chen, R.: Bayesian fusion for indoor positioning using Bluetooth fingerprints. Int. J. Wirel. Pers. Commun. 70(4), 1735–1745 (2013)

    Article  Google Scholar 

  5. Contreras, D., Castro, M., de la Torre, S.: Performance evaluation of Bluetooth Low Energy in indoor positioning systems. Trans. Emerg. Telecommun. Technol. 25(8), 1–10 (2014)

    Google Scholar 

  6. Faragher, R., Harle, R.: Location fingerpriting with Bluetooth Low Energy beacons. IEEE J. Sel. Areas Commun. 33(11), 2418–2428 (2015)

    Article  Google Scholar 

  7. He, T., Huang, C., Blum, B.M., Stankovic, J.A., Abdelzaher, T.: Range-free localization schemes for large scale sensor networks. In: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, pp. 81–95 (2003)

    Google Scholar 

  8. He, T., Huang, C., Blum, B.M., Stankovic, J.A., Abdelzaher, T.F.: Range-free localization and its impact on large scale sensor networks. ACM Trans. Embed. Comput. Syst. (TECS) 4(4), 877–906 (2005)

    Article  Google Scholar 

  9. Huang, L., Wang, F., Ma, C., Duan, W.: The analysis of anchor placement for self-localization algorithm in wireless sensor networks. In: Advances Wireless Sensor Networks, Communications in Computer and Info. Science, vol. 334, pp. 117–126 (2013)

    Google Scholar 

  10. Ionescu, G., de la Osa, C.M., Deriaz, M.: Improving distance estimation in object localisation with Bluetooth Low Energy. In: Proceedings of the International Conference on Sensor Technologies and Applications (SENSORCOMM), pp. 1–5 (2014)

    Google Scholar 

  11. Ishida, S., Takashima, Y., Tagashira, S., Fukuda, A.: Proposal of separate channel fingerprinting using Bluetooth Low Energy. In: Proceedings of the IIAI International Congress Advanced Applied Informatics (AAI), ESKM, pp. 230–233 (2016)

    Google Scholar 

  12. Kaemarungsi, K., Krishnamurthy, P.: Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive Mob. Comput. 8(2), 292–316 (2012)

    Article  Google Scholar 

  13. Kushki, A., Plataniotis, K.N., Venetsanopoulos, A.N.: Intelligent dynamic radio tracking in indoor wireless local area networks. IEEE Trans. Mobile Comput. 9(1), 405–419 (2010)

    Article  Google Scholar 

  14. Kyritsis, A.I., Kostopoulos, P., Deriaz, M., Konstantas, D.: A BLE-based probabilistic room-level localization method. In: Proceedings of the International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6 (2016)

    Google Scholar 

  15. LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., Schili, B.: Place lab: device positioning using radio beacons in the wild. In: LNCS, vol. 3468, pp. 116–133 (2005). Proceedings of the ACM International Conference on Pervasive Computing (PERVASIVE)

    Google Scholar 

  16. Minami, M., Fukuju, Y., Hirasawa, K., Yokoyama, S., Mizumachi, M., Morikawa, H., Aoyama, T.: DOLPHIN: A practical approach for implementing a fully distributed indoor ultrasonic positioning system. In: LNCS, vol. 3205, pp. 437–365 (2004). Proceedings of the ACM Conference Ubiquitous Computing (Ubicomp)

    Google Scholar 

  17. Nagpal, R., Shrobe, H., Bachrach, J.: Organizing a global coordinate system from local information on an ad hoc sensor network. In: LNCS, vol. 2634, pp. 333–348 (2003). Proceedings of the IPSN

    Google Scholar 

  18. Niculescu, D., Nath, B.: Ad hoc positioning system (APS). In: Proceedings of the IEEE GLOBECOM, pp. 2926–2931 (2001)

    Google Scholar 

  19. Peng, Y., Fan, W., Dong, X., Zhang, X.: An iterative weighted KNN (IW-KNN) based indoor localization method in Bluetooth Low Energy (BLE) environment. In: Proceedings of the IEEE International Conference on Cloud and Big Data Computing (CBDCom), pp. 794–800 (2016)

    Google Scholar 

  20. Peterson, B.S., Baldwin, R.O., Kharoufeh, J.P.: Bluetooth inquiry time characterization and selection. IEEE Trans. Mob. Comput. 5(9), 1173–1187 (2006)

    Article  Google Scholar 

  21. Prasithsangaree, P., Krishnamurthy, P., Chrysanthis, P.K.: On indoor position location with wireless LANs. In: Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 720–724 (2002)

    Google Scholar 

  22. Qui, J.W., Lin, C.P., Tseng, Y.C.: BLE-based collaborative indoor localization with adaptive multi-lateration and mobile encountering. In: Proceedings of the IEEE International Wireless Communications and Networking Conference, pp. 1–7 (2016)

    Google Scholar 

  23. Schmalenstroeer, J., Haeb-Umbach, R.: Investigations into Bluetooth Low Energy localization precision limits. In: Proceedings of the European Signal Processing Conference (EUSIPCO), pp. 652–656 (2016)

    Google Scholar 

  24. Sen, S., Radunović, B., Choudhury, R.R., Minka, T.: You are facing the Mona Lisa: Spot localization using PHY layer information. In: Proceedings of the ACM MobiSys, pp. 183–196 (2012)

    Google Scholar 

  25. Subhan, F., Hasbullah, H., Rozyyev, A., Bakhsh, S.T.: Indoor positioning in Bluetooth networks using fingerprinting and lateration approach. In: Proceedings of the IEEE International Conference on Information Science Applications (ICISA), pp. 1–9 (2011)

    Google Scholar 

  26. Tsui, A.W., Chuang, Y.H., Chu, H.H.: Unsupervised learning for solving RSS hardware variance problem in WiFi localization. Mob. Netw. Appl. 12(5), 677–691 (2009)

    Article  Google Scholar 

  27. Zhu, J., Chen, Z., Luo, H., Li, Z.: RSSI based Bluetooth Low Energy indoor positioning. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 526–533 (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by JSPS KAKENHI Grant Number 15H05708 and the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University.

Author information

Authors and Affiliations

  1. ISEE, Kyushu University, Fukuoka, 819-0395, Japan

    Shigemi Ishida, Yoko Takashima & Akira Fukuda

  2. Faculty of Informatics, Kansai University, Osaka, 569-1095, Japan

    Shigeaki Tagashira

Authors
  1. Shigemi Ishida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoko Takashima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shigeaki Tagashira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akira Fukuda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shigemi Ishida .

Editor information

Editors and Affiliations

  1. Advanced Institute of Industrial Technology, Tokyo, Japan

    Tokuro Matsuo

  2. Kyushu University, Fukuoka, Japan

    Tsunenori Mine

  3. Kyushu University, Fukuoka, Japan

    Sachio Hirokawa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ishida, S., Takashima, Y., Tagashira, S., Fukuda, A. (2018). Design and Initial Evaluation of Bluetooth Low Energy Separate Channel Fingerprinting. In: Matsuo, T., Mine, T., Hirokawa, S. (eds) New Trends in E-service and Smart Computing. Studies in Computational Intelligence, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-70636-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70636-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70634-4

  • Online ISBN: 978-3-319-70636-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation