Skip to main content
SpringerLink
Log in

Impact of Radio Map Size on Indoor Localization Accuracy

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2022 (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13375))

Included in the following conference series:

  • 722 Accesses

Abstract

Nowadays Indoor Positioning Systems (IPS) are attracting attention in literature because of Global Positioning System (GPS) challenge to track and navigate indoors. These IPSs intend to provide information about a wireless object’s current position indoor. GPS-based localization is the most successful Location-Based Service (LBS) application deployed in an outdoor environment. However, GPS faces a challenge of the line of sight indoor. GPS is affected extensively by the multipath-effects. IPS technologies such as Wi-Fi are deployed for indoor localization, in an attempt to alleviate the GPS indoor challenges. Most IPS employs the Fingerprinting algorithm, whereby a radio-map is generated during the offline phase by collecting measurements of Received Signal Strength Indicator (RSSI) at known locations and, positioning of devices at an unknown location is performed during the online phase by utilizing Machine Learning (ML) Algorithms. The radio-map dataset plays a major role in the accuracy performance of the classifiers deployed during the online phase. RSSI fluctuates indoors because of fading, interferences, and shadowing, therefore, the correction of the radio-map RSSI measurements is mandatory to improve the classifiers performance. In this paper, we looked into the impact of the size of the calibration radio-map on the accuracy of the predictive model. We applied the Mean and Standard Deviation filter on three datasets of different sizes to reduce the RSSI instability at each required point and conducted comparative performance on how ML classification models perform on the three radio-map different in size. The radio-map was generated using our EMPsys application. The results of the simulations show that the accuracy of the Kernel Naïve Bayes significantly improved with filter as the radio-map size increased, from 64.4% with 453 observations in the first scenario to 95.4% with 1054 observations in the third scenario. We, therefore, conclude that the performance of the classifier to be used during the online phase of the fingerprinting algorithm relies on both the size of the radio-map and the filtering methods used to correct the RSSI measurements of the radio-map.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Sediela, M.S., Gadebe, M.L., Kogeda, O.P.: Indoor localization with filtered and corrected calibration RSSI. In: Zitouni, R., Phokeer, A., Chavula, J., Elmokashfi, A., Gueye, A., Benamar, N. (eds.) AFRICOMM 2020. LNICST, vol. 361, pp. 59–73. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70572-5_4

    Chapter  Google Scholar 

  2. Qiao, W., Kang, X., Li, M.: An improved XGBoost indoor localization algorithm. DEStech Trans. Comput. Sci. Eng. cisnr (2020)

    Google Scholar 

  3. Owuor, D.L., Kogeda, O.P., Agbinya, J.I.: Three tier indoor localization system for digital forensics. Int. J. Electr. Comput. Energetic Electron. Commun. Eng. 11(6), 602–610 (2017)

    Google Scholar 

  4. Khan, M., Kai, Y.D., Gul, H.U.: Indoor Wi-Fi positioning algorithm based on combination of Location Fingerprint and Unscented Kalman Filter. In: 2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 693–698. IEEE (2017)

    Google Scholar 

  5. Li, S., Rashidzadeh, R.: Hybrid indoor location positioning system. IET Wirel. Sensor Syst. 9(5), 257–264 (2019)

    Article  Google Scholar 

  6. Zhou, Z., Yang, Z., Wu, C., Sun, W., Liu, Y.: LiFi: Line-of-sight identification with WiFi. In: 2014 Proceedings IEEE INFOCOM, pp. 2688–2696. IEEE (2014)

    Google Scholar 

  7. Oksar, I.: A Bluetooth signal strength based indoor localization method. In: 2014 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 251–254. IEEE (2014)

    Google Scholar 

  8. Huh, J.-H., Seo, K.: An indoor location-based control system using bluetooth beacons for IoT systems. Sensors 17(12), 2917 (2017)

    Article  Google Scholar 

  9. Althnian, A., et al.: Impact of dataset size on classification performance: an empirical evaluation in the medical domain. Appl. Sci. 11(2), 796 (2021)

    Article  Google Scholar 

  10. Rácz, A., Bajusz, D., Héberger, K.: Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification. Molecules 26(4), 1111 (2021)

    Article  Google Scholar 

  11. Ajiboye, A., Abdullah-Arshah, R., Hongwu, Q.: Evaluating the effect of dataset size on predictive model using supervised learning technique (2015)

    Google Scholar 

  12. Roy, P., Chowdhury, C.: A survey of machine learning techniques for indoor localization and navigation systems. J. Intell. Robot. Syst. 101(3), 1–34 (2021). https://doi.org/10.1007/s10846-021-01327-z

    Article  Google Scholar 

  13. Zàruba, G.V., Huber, M., Kamangar, F., Chlamtac, I.: Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Netw. 13(2), 221–235 (2007)

    Article  Google Scholar 

  14. Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y.C., Xie, L.: Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization. Sensors 15(1), 715–732 (2015)

    Article  Google Scholar 

  15. Pirzada, N., Nayan, M.Y., Hassan, M.F., Subhan, F.: Multipath fading in device-free indoor localization system: measurements and interpretation. Mehran Univ. Res. J. Eng. Technol. 34, no. S1 (2015)

    Google Scholar 

  16. Xue, W., Qiu, W., Hua, X., Yu, K.: Improved Wi-Fi RSSI measurement for indoor localization. IEEE Sens. J. 17(7), 2224–2230 (2017)

    Article  Google Scholar 

  17. Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No. 00CH37064), vol. 2, pp. 775–784. IEEE (2000)

    Google Scholar 

  18. Zhu, X.: Indoor localization based on optimized KNN. Network Commun. Technol. 5(2), 1–34 (2021)

    Google Scholar 

  19. Cleophas, T.J., Zwinderman, A.H.: Decision Trees. In: Machine Learning in Medicine, pp. 137–150. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-7869-6_14

    Chapter  Google Scholar 

  20. Seçkin, A.Ç., Coçkun, A.: Hierarchical fusion of machine learning algorithms in indoor positioning and localization. Appl. Sci. 9(18), 3665 (2019)

    Article  Google Scholar 

  21. Sánchez-Rodríguez, D., Hernández-Morera, P., Quinteiro, J.M., Alonso-González, I.: A low complexity system based on multiple weighted decision trees for indoor localization. Sensors 15(6), 14809–14829 (2015)

    Article  Google Scholar 

  22. Maimon, O., Rokach, L.: Data mining and knowledge discovery handbook (2005)

    Google Scholar 

  23. Mirowski, P., Milioris, D., Whiting, P., Ho, T.K.: Probabilistic radio-frequency fingerprinting and localization on the run. Bell Labs Tech. J. 18(4), 111–133 (2014)

    Article  Google Scholar 

  24. Mohamed, A.E.: Comparative study of four supervised machine learning techniques for classification. Inf. J. Appl. Sci. Technol. 7(2) (2017)

    Google Scholar 

  25. Archana, S., Elangovan, K.: Survey of classification techniques in data mining. Int. J. Comput. Sci. Mob. Appl. 2(2), 65–71 (2014)

    Google Scholar 

  26. Dhiraj, K.: Top 5 advantages and disadvantages of Decision Tree Algorithm. ed (2020)

    Google Scholar 

  27. Jadhav, S.D., Channe, H.: Comparative study of K-NN, naive Bayes and decision tree classification techniques. Int. J. Sci. Res. (IJSR) 5(1), 1842–1845 (2016)

    Article  Google Scholar 

  28. McConville, R., Byrne, D., Craddock, I., Piechocki, R., Pope, J., Santos-Rodriguez, R.: Understanding the quality of calibrations for indoor localisation. In: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), pp. 676–681. IEEE (2018)

    Google Scholar 

  29. Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. LNCS, vol. 2888. Springer, Heidelberg. https://doi.org/10.1007/978-3-540-39964-3-62

  30. Brijain, R., Patel, R., Kushik, M.R., Rana. K.: A survey on decision tree algorithm for classification (2014)

    Google Scholar 

  31. Huang, Y., Li, L.: Naive Bayes classification algorithm based on small sample set. In: IEEE International Conference on Cloud Computing and Intelligence Systems 2011, pp. 34–39 (2011). https://doi.org/10.1109/CCIS.2011.6045027

  32. Ssengonzi, C., Kogeda, O.P., Olwal, T.O.: A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization. Elsevier: Array 14, 100142 (2022). https://doi.org/10.1016/j.array.2022.100142. https://www.sciencedirect.com/science/article/pii/S2590005622000133. ISSN 2590-0056

  33. Gadebe, M.L., Kogeda, O.P., Ojo, S.: A Smartphone Naïve Bayes Human Activity Recognition using Personalized dataset. J. Adv. Comput. Intell. Intell. Inf. 24(5), 685–702 (2020). https://doi.org/10.20965/jaciii.2020.p0685. https://www.fujipress.jp/jaciii/jc/jacii002400050685/. ISBN: 1343-0130/1883-8014

Download references

Acknowledgements

The authors would like to thank Tshwane University of Technology and University of the Free State for financial support. The authors declare that there is no conflict of interest regarding the publication of this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of ICT, Tshwane University of Technology, Private Bag X680, Pretoria, 0001, South Africa

    Madikana S. Sediela

  2. Department of Advanced Internet of Things, Council for Scientific and Industrial Research (CSIR), P.O. Box 395, Pretoria, 0001, South Africa

    Moses L. Gadebe

  3. Department of Computer Science and Informatics, Faculty of natural and agricultural sciences, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South Africa

    Okuthe P. Kogeda

Authors
  1. Madikana S. Sediela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moses L. Gadebe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Okuthe P. Kogeda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madikana S. Sediela .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Universidad de Málaga, Malaga, Spain

    Eligius M. T. Hendrix

  4. Monash University, Clayton, VIC, Australia

    David Taniar

  5. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sediela, M.S., Gadebe, M.L., Kogeda, O.P. (2022). Impact of Radio Map Size on Indoor Localization Accuracy. In: Gervasi, O., Murgante, B., Hendrix, E.M.T., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2022. ICCSA 2022. Lecture Notes in Computer Science, vol 13375. Springer, Cham. https://doi.org/10.1007/978-3-031-10522-7_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10522-7_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10521-0

  • Online ISBN: 978-3-031-10522-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation