Enabling Accurate Indoor Localization Using a Machine Learning Algorithm

Authors

  • Haidar Abdulrahman Abbas Department of Computer¸ College of Science, University of Sulaimani, Sulaymaniyah, Iraq
  • Kayhan Zrar Ghafoor Department of Software Engineering, University of Salahaddin, Erbil, Iraq

DOI:

https://doi.org/10.21928/uhdjst.v4n1y2020.pp96-102

Keywords:

Received Signal Strength, Wireless Access Points, Wireless Fidelity Fingerprinting, Indoor Localization, Decision Tree, Naïve Bayes, Support Vector Machine

Abstract

In this paper, fingerprint referencing methods based on wireless fidelity Wi-Fi received signal strength (RSS) have used for indoor positioning. More precisely, Naïve Bayes, decision tree (DT), and support vector machine (SVM) one-to-one multi-classes and error-correcting-output-codes classifier are to enable accurate indoor positioning. Then, normalization is used to reduce positioning error by reducing the fluctuation and diverse distribution of the RSS values. Different devices are used in this experiment; the training dataset is not included in the main dataset. Nonetheless, the learned model by the SVM algorithm cannot be affected by the elimination of train datasets of the test device. The efficiency of DT is lower than the other machine learning algorithms, because it performs by Boolean function, and it provides the low accuracy of prediction for dataset than the algorithms. Naïve Bayes technique based on Bayes Theorem is better than DT and close to SVM for positioning approves that 1–1.5 m positioning accuracy for indoor environments can be achieved by the proposed approach which is an excellent result than traditional protocol.

References

[1] J. Xiao, Z. Zhou, Y. Yi and L. M. Ni. “A survey on wireless indoor localization from the device perspective,” ACM Computing Surveys, vol. 49, no. 2, p. 2933232, 2016.
[2] A. S. Paul and E. A. Wan. “RSSI-Based indoor localization and tracking using sigma-point kalman smoothers,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 5, pp. 860-873, 2009.
[3] N. Alikhani, S. Amirinanloo, V. Moghtadaiee and S. A. Ghorashi. “Fast Fingerprinting Based Indoor Localization by Wi-Fi Signals,” 2017 7th International Conference on Computer and Knowledge Engineering, vol. 2017 Janua, pp. 241-246, 2017.
[4] S. Dai, L. He and X. Zhang. “Autonomous WiFi Fingerprinting for Indoor Localization”. In: 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), pp. 141-150, 2020.
[5] F. Li, M. Liu, Y. Zhang and W. Shen.“A two-level wifi fingerprintbased indoor localization method for dangerous area monitoring,” Sensors (Basel), vol. 19, no. 19, p. 4243, 2019.
[6] J. W. Jang and S. N. Hong. “Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network,” International Conference on Ubiquitous and Future Networks, vol. 2018, pp. 753-758, 2018.
[7] M. Alfakih and M. Keche. “An enhanced indoor positioning method based on Wi-fi RSS fingerprinting,” The Journal of Communications Software and Systems, vol. 15, no. 1, pp. 18-25, 2019.
[8] C. Chen, Y. Chen, Y. Han, H. Q. Lai and K. J. R. Liu, “Achieving centimeter-accuracy indoor localization on wifi platforms: A frequency hopping approach,” IEEE Internet of Things Journal, vol. 4, no. 1, pp. 111-121, 2017.
[9] A. Sahar and D. Han. “An LSTM-based Indoor Positioning Method Using Wi-Fi Signals,” ACM’s International Conference Proceedings, 2018.
[10] M. Brunato and R. Battiti. “Statistical learning theory for location fingerprinting in wireless LANs,” Computer Networks, vol. 47, no. 6, pp. 825-845, 2005.
[11] Y. Li. “Predicting materials properties and behavior using classification and regression trees,” Materials Science and Engineering A, vol. 433, no. 1-2, pp. 261-268, 2006.
[12] N. Gutierrez, C. Belmonte, J. Hanvey, R. Espejo and Z. Dong. “Indoor Localization for Mobile Devices,” Proceeding. 11th IEEE International Conference on Sensing Control, pp. 173-178, 2014.
[13] Z. Wu, Q. Xu, J. Li, C. Fu, Q. Xuan and Y. Xiang. “Passive indoor localization based on CSI and naive bayes classification,” IEEE Transactions on Systems, Man, and Cybernetics Systems, vol. 48, no. 9, pp. 1566-1577, 2018.
[14] B. Schölkopf. “Slides learning with kernels,” Journal of the
Electrochemical Society, vol. 129, p. 2865, 2002.
[15] T. Joachims. “Transductive Inference for Text Classification Using Support Vector Machines,” Proceeding 20th International Conference on Machine Learning, 2000.
[16] Z. Zhong, Z. Tang, X. Li, T. Yuan, Y. Yang, M. Wei, Y. Zhang, R. Sheng and N. Grant. “XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field,” Proceeding 2018 6th Internationl Symposium Computer Netwwork, pp. 228-234, 2018.
[17] A. H. Salamah, M. Tamazin, M. A. Sharkas and M. Khedr. “An Enhanced WiFi Indoor Localization System Based on Machine Learning,” 2016 International Conference Indoor Position Indoor Navigation, pp. 4-7, 2016.

Published

2020-06-27

Issue

Section

Articles