A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Universitas Islam Indonesia Kaliurang KM 14.5 Street Yogyakarta, Indonesia

2 Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia Kuala Lumpur 54100, Malaysia

3 Malaysian Administrative, Modernisation and Management Planning Unit, Putrajaya 62502, Malaysia

Abstract

The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

Keywords

Main Subjects

References

  1. Xia, S., Liu, Y., Yuan, G., Zhu, M., and Wang, Z. “Indoor fingerprint positioning based on Wi-Fi: An overview.” ISPRS International Journal of Geo-Information, Vol. 6, No. 5, (2017). https://doi.org/10.3390/ijgi6050135
  2. Hadian Jazi, S., Farahani, S., and Karimpour, H. “Map-merging in multi-robot simultaneous localization and mapping process using two heterogeneous ground robots.” International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 4, (2019), 608-616. https://doi.org/10.5829/ije.2019.32.04a.20
  3. Hamidi, H., and Valizadeh, A. “Improvement of navigation accuracy using tightly coupled Kalman filter.” International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 2, (2017), 1293-1301. https://doi.org/10.5829/idosi.ije.2017.30.02b.08
  4. Nguyen, K. A., Luo, Z., Li, G., and Watkins, C. “A review of smartphones-based indoor positioning: Challenges and applications.” IET Cyber-systems and Robotics, Vol. 3, No. 1, (2021), 1-30. https://doi.org/10.1049/csy2.12004
  5. Korayem, M. H., Peydaie, P., and Azimirad, V. “Investigation on the effect of different parameters in wheeled mobile robot error.” International Journal of Engineering, Transactions A: Basics, Vol. 20, No. 2, (2007), 195-210.
  6. He, S., and Chan, S.-H. G. “INTRI: Contour-Based Trilateration for Indoor Fingerprint-Based Localization.” IEEE Transactions on Mobile Computing, Vol. 16, No. 6, (2017), 1676-1690. https://doi.org/10.1109/TMC.2016.2604810
  7. Choi, M. S., and Jang, B. “An accurate fingerprinting based indoor positioning algorithm.” International Journal of Applied Engineering Research, Vol. 12, No. 1, (2017), 86-90. Retrieved from https://yonsei.pure.elsevier.com/en/publications/an-accurate-fingerprinting-based-indoor-positioning-algorithm
  8. Mrindoko, N. R., and Minga, D. L. M. “A Comparison Review of Indoor Positioning Techniques.” International Journal of Computer, Vol. 21, No. 1, (2016), 42-49. https://doi.org/10.1093/hmg/1.8.655-a
  9. Firdaus, F., Ahmad, N. A., and Sahibuddin, S. “Accurate indoor-positioning model based on people effect and ray-tracing propagation.” Sensors, Vol. 19, No. 24, (2019). https://doi.org/10.3390/s19245546
  10. Firdaus, F., Ahmad, N. A., and Sahibuddin, S. “A review of hybrid indoor positioning systems employing WLAN fingerprinting and image processing.” International Journal of Electrical and Computer Engineering Systems, Vol. 10, No. 2, (2019), 59-72. https://doi.org/10.32985/ijeces.10.2.2
  11. Kim, Y., Shin, H., Chon, Y., and Cha, H. “Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem.” Pervasive and Mobile Computing, Vol. 9, No. 3, (2013), 406-420. https://doi.org/https://doi.org/10.1016/j.pmcj.2012.12.003
  12. Mautz, R. “Overview of current indoor positioning systems.” Geodesy and Cartography, Vol. 35, No. 1, (2009), 18-22. https://doi.org/10.3846/1392-1541.2009.35.18-22
  13. Subedi, S., and Pyun, J. Y. “A survey of smartphone-based indoor positioning system using RF-based wireless technologies.” Sensors, Vol. 20, No. 24, (2020), 1-32. https://doi.org/10.3390/s20247230
  14. Batistić, L., and Tomic, M. “Overview of indoor positioning system technologies.” In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 473-478. https://doi.org/10.23919/MIPRO.2018.8400090
  15. Firdaus, Ahmad, N. A., and Sahibuddin, S. “Fingerprint indoor positioning based on user orientations and minimum computation time.” Telkomnika (Telecommunication Computing Electronics and Control), Vol. 17, No. 4, (2019), 1740-1749. https://doi.org/10.12928/TELKOMNIKA.V17I4.12774
  16. Mendoza-Silva, G. M., Torres-Sospedra, J., and Huerta, J. “A meta-review of indoor positioning systems.” Sensors, Vol. 19, No. 20, (2019). https://doi.org/10.3390/s19204507
  17. Wang, H., Ma, L., Xu, Y., and Deng, Z., "Dynamic Radio Map Construction for WLAN Indoor Location" in Third International Conference on Intelligent Human-Machine Systems and Cybernetics, IEEE, (2011). https://doi.org/10.1109/IHMSC.2011.110
  18. Zulkiflie, S. A., Kamaruddin, N., and Wahab, A. “Dynamic navigation indoor map using wi-fi fingerprinting mobile technology.” Bulletin of Electrical Engineering and Informatics, Vol. 9, No. 2, (2020), 739-746. https://doi.org/10.11591/eei.v9i2.2066
  19. Pérez-Navarro, A., Torres-Sospedra, J., Montoliu, R., Conesa, J., Berkvens, R., Caso, G., Costa, C., Dorigatti, N., Hernández, N., Knauth, S., … Wilk, P. “Challenges of fingerprinting in indoor positioning and navigation.” Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap, No. July 2018, (2018), 1-20. https://doi.org/10.1016/B978-0-12-813189-3.00001-0
  20. Pascacio, P., Casteleyn, S., Torres-Sospedra, J., Lohan, E. S., and Nurmi, J. “Collaborative indoor positioning systems: A systematic review.” Sensors, Vol. 21, No. 3, (2021), 1-39. https://doi.org/10.3390/s21031002
  21. Mashuk, M. S., Pinchin, J., Siebers, P. O., and Moore, T. “A smart phone based multi-floor indoor positioning system for occupancy detection.” in IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings, No. December, (2018), 216-227. https://doi.org/10.1109/PLANS.2018.8373384
  22. Wang, J., and Park, J. G. “An enhanced indoor positioning algorithm based on fingerprint using fine-grained csi and rssi measurements of ieee 802.11n wlan.” Sensors, Vol. 21, No. 8, (2021). https://doi.org/10.3390/s21082769
  23. Alshami, I. H., Ahmad, N. A., Sahibuddin, S., and Firdaus, F. “Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments.” Sensors, Vol. 17, No. 8, (2017). https://doi.org/10.3390/s17081789
  24. Yudha, D. P., Hasbi, B. I., and Sukarna, R. H. “Indoor Positioning System Berdasarkan Fingerprinting Received Signal Strength (Rss) Wifi Dengan Algoritma K-Nearest Neighbor (K-Nn).” ILKOM Jurnal Ilmiah, Vol. 10, No. 3, (2018), 274-283. https://doi.org/10.33096/ilkom.v10i3.364.274-283
  25. Firdaus, Ahmad, N. A., Sahibuddin, S., and Dziyauddin, R. A. “Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning.” International Journal of Integrated Engineering, Vol. 12, No. 7, (2020), 72-80. https://doi.org/10.30880/ijie.2020.12.07.008
  26. Wang, L., Liu, J., and Zhou, W. A Survey on Motion Detection Using WiFi Signals. https://doi.org/10.1109/MSN.2016.040
  27. Zhou, B., Li, Q., Mao, Q., and Tu, W. “A robust crowdsourcing-based indoor localization system.” Sensors, Vol. 17, No. 4, (2017), 1-16. https://doi.org/10.3390/s17040864
  28. Xue, J., Liu, J., Sheng, M., Shi, Y., and Li, J. “A WiFi fingerprint based high-adaptability indoor localization via machine learning.” China Communications, Vol. 17, , (2020), 247-259. https://doi.org/10.23919/J.CC.2020.07.018
  29. Li, Y., Williams, S., Moran, B., and Kealy, A. “A Probabilistic Indoor Localization System for Heterogeneous Devices.” IEEE Sensors Journal, Vol. 19, No. 16, (2019), 6822-6832. https://doi.org/10.1109/JSEN.2019.2911707
  30. Park, J. G., Curtis, D., Teller, S., and Ledlie, J. “Implications of device diversity for organic localization.” Proceedings - IEEE INFOCOM, No. July 2011, (2011), 3182-3190. https://doi.org/10.1109/INFCOM.2011.5935166
  31. Yang, F., Xiong, J., Liu, J., Wang, C., Li, Z., Tong, P., and Chen, R. “A pairwise SSD fingerprinting method of smartphone indoor localization for enhanced usability.” Remote Sensing, Vol. 11, No. 5, (2019). https://doi.org/10.3390/rs11050566
  32. Li, L., Yang, W., Bhuiyan, M. Z. A., and Wang, G. “Unsupervised learning of indoor localization based on received signal strength: Unsupervised learning of indoor localization.” Wireless Communications and Mobile Computing, Vol. 16, , (2016). https://doi.org/10.1002/wcm.2678
  33. Zhang, L., Meng, X., and Fang, C. “Linear Regression Algorithm against Device Diversity for the WLAN Indoor Localization System.” Wireless Communications and Mobile Computing, Vol. 2021, , (2021). https://doi.org/10.1155/2021/5530396
  34. Meneses, F., Moreira, A., Costa, A., and Nicolau, M. J. Radio maps for fingerprinting in indoor positioning. Geographical and Fingerprinting Data for Positioning and Navigation Systems: Challenges, Experiences and Technology Roadmap. Elsevier Inc. https://doi.org/10.1016/B978-0-12-813189-3.00004-6
International Journal of Engineering
Volume 36, Issue 4
TRANSACTIONS A: Basics
April 2023
Pages 649-658

Files

Cited by

Share

How to cite

Statistics