Research on the success of unsupervised learning algorithms in indoor location prediction

Abstract

With location-based smart applications, the flow of life can be facilitated and support can be provided in case of security and emergency situations. Indoor location detection provides various conveniences in complex structures such as hospitals, schools, shopping centers, etc. Indoor location detection studies are carried out by using data related to location and signal and machine learning methods. Machine learning has become frequently used as a solution method in this field, as in many other fields. When the studies in the literature are examined, it is seen that the studies are mainly focused on producing solutions with supervised machine learning algorithms. Unsupervised algorithms are frequently used to determine the labels of data groups that do not have labels. In this direction, it can be seen as the first step in labeling the data collected in indoor positioning studies and then using it for training predictive models to be developed with supervised learning methods. For this reason, the results to be obtained regarding the success and usefulness of cluster analysis will constitute an important basis for further studies. In this study, it is aimed to examine the success of unsupervised learning, in other words, clustering algorithms. The Wireless Indoor Localization Data Set and well-known k-Means and Fuzzy c-Means algorithms have been used with different distance measure. The obtained methods performances have been evaluated with internal and external indices. The results show that the clustering algorithms can cluster correctly data points in the range of 93-95% according to the accuracy and F measure value. Although performances indicators are very close to each other according to the internal indexes, it can be stated that the model obtained using the Manhattan distance measure and the k-Means algorithm has higher performance in terms of clustering success.

Keywords

• Custering
• External indices
• Fuzzy c-Means
• Indoor location prediction
• Indoor positioning
• Internal indices
• k-Means
• Position prediction
• Unsupervised learning

References

1. 1. Hazas, M., J. Scott, and J. Krumm, Location-aware computing comes of age. Computer, 2004. 37(2): p. 95–97.
2. 2. Oguntala, G., R. Abd-Alhameed, S. Jones, J. Noras, M. Patwary, and J. Rodriguez, Indoor location identification technologies for real-time IoT-based applications: An inclusive survey. Computer Science Review, 2018. 30: p. 55–79.
3. 3. Curran, K. E. Furey, T. Lunney, J. Santos, D. Woods, and A. McCaughey, An evaluation of indoor location determination technologies. Journal of Location Based Services, 2011. 5(2): p. 61–78.
4. 4. Nath, R.K., R. Bajpai, and H. Thapliyal, IoT based indoor location detection system for smart home environment, in IEEE International Conference on Consumer Electronics (ICCE). 2018. Las Vegas, USA: p. 1-3.
5. 5. Roy P. and C. Chowdhury, A survey of machine learning techniques for indoor localization and navigation systems. J Intell Robot Syst, 2021. 101(3): p. 63.
6. 6. Jedari, E., Z. Wu, R. Rashidzadeh, and M. Saif, Wi-Fi based indoor location positioning employing random forest classifier, in International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015. Calgary, Canada: p. 1–5.
7. 7. Tabbakha, N.E., W.-H. Tan, and C.-P. Ooi, Indoor location and motion tracking system for elderly assisted living home, in International Conference on Robotics, Automation and Sciences (ICORAS), 2017. Melaka, Malaysia: p. 1–4.
8. 8. Chao C. and M. Xiaoran, An innovative indoor location algorithm based on supervised learning and wifi fingerprint classification, in Signal and Information Processing, Networking and Computers, 2018. Singapore: pp. 238–246.

9. 9. Nuño-Maganda, M.A., H. Herrera-Rivas, C. Torres-Huitzil, H. Marisol Marín-Castro, and Y. Coronado-Pérez, On-device learning of indoor location for wifi fingerprint approach. Sensors, 2018. 18(7).
10. 10. Elbes, M., E. Almaita, T. Alrawashdeh, T. Kanan, S. AlZu’bi, and B. Hawashin, An indoor localization approach based on deep learning for indoor location-based services, in IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2019. Amman, Jordan: p. 437–441.
11. 11. Dai, P., Y. Yang, M. Wang, and R. Yan, Combination of DNN and improved KNN for indoor location fingerprinting. Wireless Communications and Mobile Computing, 2019. p. e4283857.
12. 12. Ouameur, M.A., M. Caza-Szoka, and D. Massicotte, Machine learning enabled tools and methods for indoor localization using low power wireless network. Internet of Things, 2020. 12: 100300.
13. 13. Polak, L., S. Rozum, M. Slanina, T. Bravenec, T. Fryza, and A. Pikrakis, Received signal strength fingerprinting-based indoor location estimation employing machine learning. Sensors, 2021. 21(13): 4605.
14. 14. Rizk, H., M. Abbas, and M. Youssef, Device-independent cellular-based indoor location tracking using deep learning. Pervasive and Mobile Computing, 2021. 75: 101420.
15. 15. Ge, H., Z. Sun, Y. Chiba, and N. Koshizuka, Accurate indoor location awareness based on machine learning of environmental sensing data. Computers & Electrical Engineering, 2021. 98: 107676.
16. 16. Xie, Y., T. Wang, Z. Xing, H. Huan, Y. Zhang, and Y. Li, An improved indoor location algorithm based on backpropagation neural network. Arab J Sci Eng, 2022. https://doi.org/10.1007/s13369-021-06529-z.
17. 17. Rohra, J.G., B. Perumal, S. J. Narayanan, P. Thakur, and R. B. Bhatt, User localization in an indoor environment using fuzzy hybrid of particle swarm optimization & gravitational search algorithm with neural networks, in Proceedings of Sixth International Conference on Soft Computing for Problem Solving. 2017. Singapore: p. 286–295.
18. 18. Rokach L. and O. Maimon, Clustering methods, in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. 2005, Boston, MA: Springer, p. 321–352.
19. 19. Omran, M.G.H., A. P. Engelbrecht and A. Salman, An overview of clustering methods. Intelligent Data Analysis, 2007. 11(6): p. 583–605.
20. 20. Ghosh S. and S. K. Dubey, Comparative analysis of k-means and fuzzy cmeans algorithms. International Journal of Advanced Computer Science and Applications, 2013. 4(4): p. 35–39.
21. 21. Izakian H. and A. Abraham, Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications,2011. 38(3): p. 1835–1838.