Skip to main content
Log in

Neural network-based indoor localization system with enhanced virtual access points

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Wi-Fi indoor positioning systems are based on received signal strength indicator (RSSI) measurements and fingerprinting, by matching measured data with the database. Hence, generation of RSSI fingerprint database is essential for a Wi-Fi-based indoor positioning system; this requires significant time and effort. The study utilizes virtual access points to increase the number of access points in an indoor environment without necessitating additional hardware. Increases in the total access points is advantageous because it makes the database more granular, and the Kriging algorithm is introduced to solve the issue with less effort. The study also aims to apply deep learning neural network (DNN) in Wi-Fi fingerprinting using RSSI. The proposed system utilizes the neural network (NN) and Kriging algorithm to perform standard Wi-Fi fingerprinting, without difficulties in generating a fingerprint map. The result of a simple location estimation test led to an accuracy of 97.14%, thereby indicating that the application of NN and Kriging improves indoor localization. Further experiments should be conducted to test the complete effectiveness of the proposed system as compared to other systems employing DNN.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Macagnano D, Destino G, Abreu G (2014) Indoor positioning: a key enabling technology for IoT applications. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp 117–118

  2. Zafari F, Gkelias A, Leung KK (2019) A survey of indoor localization systems and technologies. IEEE Commun Surv Tutor 21:2568–2599

    Article  Google Scholar 

  3. Pallasena RK, Sharma M, Krishnaswamy V (2019) Context-sensitive smart devices-definition and a functional taxonomy. Int J Soc Hum Comput 3(2):108–134

    Google Scholar 

  4. Ahmed SH, Bouk SH, Mehmood A, Javaid N, Iwao S (2012) Effect of fast moving object on RSSI in WSN: an experimental approach. In: International Multi Topic Conference, pp 43–51

  5. Adege A, Lin HP, Tarekegn G, Jeng SS (2018) Applying deep neural network (DNN) for robust indoor localization in multi-building environment. Appl Sci 8(7):1062

    Article  Google Scholar 

  6. Félix G, Siller M, Alvarez EN (2016) A fingerprinting indoor localization algorithm based deep learning. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp 1006–1011

  7. Kim KS, Lee S, Huang K (2018) A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting. Big Data Anal 3(1):4

    Article  Google Scholar 

  8. Lee DM, Labinghisa B (2019) Indoor localization system based on virtual access points with filtering schemes. Int J Distrib Sensor Netw 15(7):1550147719866135

    Article  Google Scholar 

  9. Li B, Wang Y, Lee HK, Dempster A, Rizos C (2005) Method for yielding a database of location fingerprints in WLAN. IEE Proc Commun 152(5):580–586

    Article  Google Scholar 

  10. Sen S et al. (2011) Precise indoor localization using PHY layer information. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, pp 1–6

  11. Bahl P, Padmanabhan V (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the INFOCOM’00: Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp 775–784. Tel Aviv, Israel

  12. Kaemarungsia K, Krishnamurthy P (2011) Analysis of WLANs received signal strength indication for indoor location fingerprinting. Pervasive Mob Comput 8(2):292–316

    Article  Google Scholar 

  13. Wang X, Gao L, Mao S, Pandey S (2017) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776

    Google Scholar 

  14. Turgut Z, Üstebay S, Aydın GZ, Sertbaş A (2019) Deep learning in indoor localization using WiFi. In: International Telecommunications Conference 2019, pp 101–110. Springer, Singapore

  15. Carvalho EC, Ferreira BV, Geraldo Filho PR, Gomes PH, Freitas GM, Vargas PA, Ueyama J, Pessin G (2019) Towards a smart fault tolerant indoor localization system through recurrent neural networks. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp 1–7. IEEE

  16. Karadeniz AS, Efe MÖ (2019) Room-level indoor localization with artificial neural networks. In: Mediterranean Conference on Pattern Recognition and Artificial Intelligence, pp 1–8. Springer, Cham

  17. Zhou B, Yang J, Li Q (2019) Smartphone-based activity recognition for indoor localization using a convolutional neural network. Sensors 19(3):1–15 621

    Article  Google Scholar 

  18. Hassan MR, Haque MSM, Hossain MI, Hassan MM, Alelaiwi A (2019) A novel cascaded deep neural network for analyzing smart phone data for indoor localization. Future Gener Comput Syst 10:760–769

    Article  Google Scholar 

  19. Hossain AKMM, Van HN, Soh WS (2008) Fingerprint-based location estimation with virtual access points. In: 2008 Proceedings of 17th International Conference on Computer Communications and Networks, pp 1–6

  20. Jan SS, Yeh SJ, Liu YW (2015) Received signal strength database interpolation by Kriging for a Wi-Fi indoor positioning system. Sensors 15(9):21377–21393

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the Ministry of Trade, Industry & Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT) under the R&D Rediscovery Project (P0010209). This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. 2019R1F1A1062670).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dong Myung Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Labinghisa, B.A., Lee, D.M. Neural network-based indoor localization system with enhanced virtual access points. J Supercomput 77, 638–651 (2021). https://doi.org/10.1007/s11227-020-03272-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03272-4

Keywords

Navigation