RSSI Based Indoor Localization with Reduced Feature Dimension
RSSI Based Indoor Localization with Reduced Feature Dimension
Wifi based indoor localization gains the interest of researchers for several purposes. Among various techniques, fingerprinting based on Wifi received signal strength indicator (RSSI) is a widely used feature in indoor localization because of its simplicity in implementation and minimal hardware requirement conditions. However, the amount of access points (AP) at which the RSSI is measured from in the network increases the computational load. This paper presents an alternative approach for dimension reduction in RSSI based indoor localization. We focus on recognizing the building and floor of the test user which is a multi-class problem for both cases. In a multiple class problem, inter-class differences are obtained by Manhattan distance in pair-wise manner. From each pair calculation, top-25 and top-50 features with the largest variances are chosen and merged to generate the final feature set. The proposed algorithm is implemented and evaluated on UJIIndoorLoc dataset. According to the outcomes, our method provides 99.1% accuracy for building and 82.8% accuracy for floor estimation
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- W. Cui, L. Zhang, B. Li, J. Guo, W. Meng, H. Wang, and L. Xie, “Received signal strength based indoor positioning using a random vector
functional link network,” IEEE Transactions on Industrial Informatics,
vol. 14, no. 5, pp. 1846–1855, 2018.
- K. Lee, Y. Nam, and S. D. Min, “An indoor localization solution using bluetooth rssi and multiple sensors on a smartphone,” Multimedia Tools
and Applications, vol. 77, pp. 1–20, 05 2018.
- F. Seco and A. R. Jimenez, “Smartphone-based cooperative indoor ´localization with rfid technology,” Sensors, vol. 18, no. 1, 2018.
[Online]. Available: https://www.mdpi.com/1424-8220/18/1/266.
- Z. Liu, L. Zhang, Q. Liu, Y. Yin, L. Cheng, and R. Zimmermann, “Fusion of magnetic and visual sensors for indoor localization:
Infrastructure-free and more effective,” IEEE Transactions on Multimedia, vol. 19, no. 4, pp. 874–888, 2017.
- H. Zhang, K. Liu, F. Jin, L. Feng, V. Lee, and J. Ng, “A scalable indoor localization algorithm based on distance fitting and fingerprint mapping
in wi-fi environments,” Neural Computing and Applications, vol. 32, 05 2020.
- I. Alshami, N. Ahmad, and S. Sahibuddin, “Automatic wlan fingerprint radio map generation for accurate indoor positioning based on signal
path loss model,” vol. 10, pp. 17 930–17 936, 01 2015.
- S.-Y. Jung, S. Hann, and C.-S. Park, “Tdoa-based optical wireless indoor localization using led ceiling lamps,” IEEE Transactions on Consumer
Electronics, vol. 57, no. 4, pp. 1592–1597, 2011.
- J. Torres-Sospedra, R. Montoliu, A. Mart´ınez-Uso, J. P. Avariento, T. J. ´Arnau, M. Benedito-Bordonau, and J. Huerta, “Ujiindoorloc: A new
multi-building and multi-floor database for wlan fingerprint-based indoor localization problems,” in 2014 International Conference on Indoor
Positioning and Indoor Navigation (IPIN), 2014, pp. 261–270.
- M. Nowicki and J. Wietrzykowski, “Low-effort place recognition with wifi fingerprints using deep learning,” 11 2016.
- M. Ibrahim, M. Torki, and M. ElNainay, “Cnn based indoor localization using rss time-series,” in 2018 IEEE Symposium on Computers and
Communications (ISCC), 2018, pp. 01 044–01 049.
- K. S. Kim, S. Lee, and K. Huang, “A scalable deep neural network architecture for multi-building and multi-floor indoor localization based
on wi-fi fingerprinting,” Big Data Analytics, vol. 3, 04 2018.
- K. A. Nguyen, “A performance guaranteed indoor positioning system using conformal prediction and the wifi signal strength,” Journal of
Information and Telecommunication, vol. 1, no. 1, pp. 41–65, 2017.
- A. Bosch, A. Zisserman, and X. Munoz, “Image classification using random forests and ferns,” in 2007 IEEE 11th International Conference
on Computer Vision, 2007, pp. 1–8.