A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing | Computing Skip to main content

Advertisement

Springer Nature Link
Log in

A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing

  • Special Issue Article
  • Published:
Computing Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel indoor localization system in a multi-indoor environment using cloud computing. Prior studies show that there are always concerns about how to avoid signal occlusion and interference in the single indoor environment. However, we find some general rules to support our system being immune to interference generated by occlusion in the multi-indoor environment. A convenient way is measured to deploy Bluetooth low energy devices, which mainly collect large information to assist localization. A neural network-based classification is proposed to improve localization accuracy, compared with several algorithms and their performance comparison is discussed. We also design a distributed data storage structure and establish a platform considering the storage load with Redis. Our real experimental validation shows that our system will meet the four aspects of performance requirements, which are higher accuracy, less power consumption, and increased levels of system magnitude and deployment efficiency.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig.5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Gu Y, Lo A, Niemegeers I (2009) A survey of indoor positioning systems for wireless personal networks. IEEE Commun Surv Tutor 11(1):13–32

    Article  Google Scholar 

  2. Sayed AH, Tarighat A, Khajehnouri N (2005) Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Process Mag 22(4):24–40

    Article  Google Scholar 

  3. Madigan D, Einahrawy E, Martin R, Ju W-H, Krishnan P, Krishnakumar A (2005) Bayesian indoor positioning systems. IEEE INFOCOM 2(2005):1217–1227

    Google Scholar 

  4. Zou H, Wang H, Xie L, Jia QS (2013) An RFID indoor positioning system by using weighted path loss and extreme learning machine. In: Proceedings of the 2013 IEEE 1st international conference on cyber-physical systems, networks, and applications (CPSNA), Taipei, Taiwan, 19–20 Aug 2013, pp 66–71.

  5. Bozkurtl S, Elibol G, Gunal S, Yayan U (2015) A comparative study on machine learning algorithms for indoor positioning. In: INISTA 2015 international symposium, Sept 2015, pp 1–8

  6. Ni LM, Liu Y, Lau YC, Patil AP (2003) LANDMARC: indoor location sensing using active RFID, PerCom

  7. Ke CK, Ho WC, Lu KC (2018) Developing a beacon-based location system using bluetooth low energy location fingerprinting for smart home device management. In: International wireless internet conference, pp 235–244. Springer.

  8. Hu Q, Yang J, Qin P, Fong S, Guo J (2020) Could or could not of Grid-Loc: grid BLE structure for indoor localisation system using machine learning. SOCA 14(3):161–174

    Article  Google Scholar 

  9. Ding G, Tan Z, Wu J, Zhang J (2014) Efficient indoor fingerprinting localization technique using regional propagation model. IEICE Trans Commun 97(8):1728–1741

    Article  Google Scholar 

  10. Youssef M, Agrawala A (2005) The horus WLAN location determination system. In: Proceedings of the 3rd international conference on mobile systems, applications, and services. ACM, pp 205–218

  11. Huang W, Xiong Y, Li X-Y, Lin H, Mao X, Yang P, Liu Y, Wang X (2015) Swadloon: direction finding and indoor localization using acoustic signal by shaking smartphones. IEEE Trans Mob Comput 14(10):2145–2157

    Article  Google Scholar 

  12. Zafari F, Papapanagiotou I, Christidis K (2016) Microlocation for internet-of-things-equipped smart buildings. IEEE Internet Things J 3(1):96–112

    Article  Google Scholar 

  13. Yu S-I, Yang Y, Hauptmann A (2014) Harry Potter’s marauder’s map: localizing and tracking multiple persons-of-interest by nonnegative discretization. IEEE CVPR

  14. Decawave, Real Time Location: An Introduction, http://www.decawave.com/sites/default/files/resources/aps003_dw1000_rtls_introduction.pdf. Online; Accessed 1 Nov 2016.

  15. Vasisht D, Kumar S, Katabi D (2016) Decimeter-level localization with a single Wi-Fi access point. In: 13th USENIX symposium on networked systems design and implementation (NSDI 16), pp 165–178

  16. Zafari F, Gkelias A, Leung K A survey of indoor localization systems and technologies. Available: https://arxiv.org/abs/1709.01015

  17. Kuo Y-S, Pannuto P, Hsiao K-J, Dutta P (2014) Luxapose: indoor positioning with mobile phones and visible light. In: Proceedings of the 20th annual international conference on mobile computing and networking. ACM, pp 447–458

  18. Oguejiofor OS, Aniedu AN, Ejiofor HC, Okolibe AU (2013) Trilateration based localization algorithm for wireless sensor network. International Journal of Science and Modern Engineering 1:21–27

    Google Scholar 

  19. Santos F (2008) Localization in wireless sensor networks. ACM J Name 5:1–19

    Google Scholar 

  20. Niculescu D, Nath B (2003) Ad hoc positioning system (APS) using AOA. In Proceedings 22nd annual joint conference of the IEEE computer and communications, vol 3, pp 1734–1743

  21. Sutton O (2012) Introduction to k-nearest neighbour classification and condensed nearest neighbour data reduction, Feb 2012

  22. Kumar S, Gil S, Katabi D, Rus D (2014) Accurate indoor localization with zero start-up cost. In: Proceedings of the 20th annual international conference on mobile computing and networking. ACM, pp 483–494

  23. Xiong J, Jamieson K (2013) ArrayTrack: a fine-grained indoor location system. In: Presented as part of the 10th USENIX symposium on networked systems design and implementation (NSDI 13), pp 71–84

  24. Kotaru M, Joshi K, Bharadia D, Katti S (2015) Spotfi: decimeter level localization using Wi-Fi. In: ACM SIGCOMM computer communication review, vol 45. ACM, pp 269–282

  25. Qiu T, Chen N, Li K, Atiquzzaman M, Zhao W. How can heterogeneous internet of things build our future: a survey. IEEE Commun Surv Tutor.

  26. Yang Q, Li W, Neuman de Souza J, Zomaya AY (2018) Resilient virtual communication networks using multi-commodity flow based local optimal mapping. Netw Comput Appl 110:43–51

    Article  Google Scholar 

  27. Chen J, Zhang K, Jia B, Gao Y. Identification of a moving object's velocity and range with a static-moving camera system. IEEE Trans Autom Control

  28. Tong W, Buglass S, Li J, Chen L, Ai C Smart and private social activity invitation framework based on historical data from smart devices. In Proceedings of the 10th EAI international conference on mobile multimedia communications

  29. Khan J, Palanisamy K (2005) Stream assignment for grid network with joint communication and computation constraints. In: Proceedings of 2nd IEEE Communications Society/Create-net international conference on broadband communications, networks and systems, BROADNET05, Boston, USA, October 2005, ISBN: 0-7803-9277-9, pp 550–558

  30. Kulshrestha T, Saxena D, Niyogi R, Raychoudhury V, Misra SmartITS M (2017) Smartphone-based identification and tracking using seamless indoor-outdoor localization. J Netw Comput Appl 98:97–113

    Article  Google Scholar 

  31. Piccialli F, Jung JJ (2018) Towards the internet of data: applications, opportunities and future challenges. J Parallel Distrib Comput 116:1–2

    Article  Google Scholar 

Download references

Acknowledgements

This research is partially supported by the Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai innovation and entrepreneurship team project (ZH01110405180056PWC).

Author information

Authors and Affiliations

  1. DACC Laboratory, Zhuhai Institute of Advanced Technology, Chinese Academic of Sciences, Zhuhai, China

    Quanyi Hu

  2. Zhuhai Institute of Advanced Technology, Chinese Academic of Sciences, Zhuhai, China

    Feng Wu

  3. School of Engineering, University of New South Wales, Kensington, Australia

    Raymond K. Wong

  4. ICT & Society Group, Durban University of Technology, Durban, South Africa

    Richard C. Millham

  5. e-Health and IoT Research Center, Lakehead University, Thunder Bay, ON, Canada

    Jinan Fiaidhi

Authors
  1. Quanyi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raymond K. Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard C. Millham
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinan Fiaidhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Quanyi Hu or Feng Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Q., Wu, F., Wong, R.K. et al. A novel indoor localization system using machine learning based on bluetooth low energy with cloud computing. Computing 105, 689–715 (2023). https://doi.org/10.1007/s00607-020-00897-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-020-00897-4

Keywords

Search

Navigation