A Comparison of Decision Tree Algorithms for Indoor User Localization Using Wireless Signal Strength

Abstract

Localizing users and devices indoors has a wide range of applications. Smart home systems can be used to locate criminals in restricted areas and determine the number of users at an access point. The aim of this study is to determine the location of users indoors using wireless signal strength as well as the best decision tree classification algorithm that can be used in monitoring devices that will be designed. For this purpose, the study uses 12 different algorithms and compares their performances by conducting a performance analysis. The study uses 10- fold cross validation as the performance analysis method. While evaluating the performance, the algorithms’ classification performance were compared before and after the cross-validation. Due to the study using a balanced dataset, the performance metrics used for classifying balanced datasets have bene preferred in the performance analysis. As a result of the analysis, the random forest algorithm was observed to have achieved the best performance. All metric values calculated before and after the cross-validation of the random forest algorithm were higher than those for the other algorithms. 

Keywords

• Indoor
• Decision Trees
• Wi-Fi
• Localization
• Performance analysis

Kablosuz Sinyal Gücünü Kullanarak İç Mekan Kullanıcı Lokalizasyonu için Karar Ağacı Algoritmalarının Karşılaştırılması

Abstract

İç mekanda kullanıcı ve cihazları yerelleştirmek geniş bir uygulama alanına sahiptir. Akıllı ev sistemleri, sınırlı bölgelerdeki suçluları bulma, bir erişim noktasındaki kullanıcı sayısını belirlemek için kullanılabilir. Bu çalışmanın amacı kablosuz sinyal gücüne dayalı olarak iç mekanda kullanıcıların konumunu belirlemektir. Bunun yanı sıra tasarlanacak izleme cihazlarında kullanılabilecek en iyi karar ağacı sınıflandırma algoritmasını saptamaktır. Bu amaçla çalışmada 12 farklı algoritma kullanılmış ve performans analizi yapılarak algoritmaların performansları karşılaştırılmıştır. Performans analiz yöntemi olarak 10 kat çapraz doğrulama kullanılmıştır. Performans değerlendirmesi yapılırken algoritmaların hem çaprazdoğrulama yapılmadan önceki sınıflandırma performansı hemde çapraz doğrulama sonrası yapılan sınıflandırma performansları karşılaştırılmışır. Çalışmada Dengeli bir veri seti kullanıldığı için Performans analizinde dengeli veri setlerinin sınıflandırılmasında kullanılan prformans metrikleri tercih edilmiştir. Performans analizinde doğruluk, karışıklık matrisi, kesinlik, duyarlılık, F-skoru, Kappa istatistiği, Kök ortalama hata değeri ve ROC değeri kullanılmıştır. Analiz sonucunda Analizden sonra. en iyi performansı Random Forest Rasgele orman algoritmasının elde ettiği gözlemlenmiştir. Algoritmanın çapraz doğrulama öncesi ve sonrasında hesaplanan tüm metric değerleri diğer algoritmalardan daha yüksektir. 

Keywords

• İç mekan
• Karar ağaçları
• Kablosuz sinyal gücü
• Konumlandırma
• Performans analizi

References

1. Abu Doush, I., Alshatnawi, S., Al-Tamimi, A.-K., Alhasan, B., ve Hamasha, S. (2017). ISAB: integrated indoor navigation system for the blind. Interacting with Computers, 29(2), 181-202. google scholar
2. Akleylek, S., Kiliç, E., Söylemez, B., Aruk, T. E., & Çavuş, A. (2020). Kapali mekan konumlandirma üzerine bir çalişma. Mühendislik Bilimleri ve Tasarim Dergisi, 8(5), 90-105. google scholar
3. Arslantaş, H., & Ökdem, S. (2019). İç Mekân Konumlandırma Yöntemleri. Paper presented at the 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences. google scholar
4. Barsocchi, P., Cimino, M. G., Ferro, E., Lazzeri, A., Palumbo, F., ve Vaglini, G. (2015). Monitoring elderly behavior via indoor position-based stigmergy. Pervasive and Mobile Computing, 23, 26-42. google scholar
5. Bozkurt, S., Elibol, G., Gunal, S., ve Yayan, U. (2015). A comparative study on machine learning algorithms for indoor positioning. Paper presented at the 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). google scholar
6. Breima, L. (2010). Random Forests. Machine Learning. google scholar
7. Breiman, L., Friedman, J., Olshen, R., ve Stone, C. (1984). Classification and regression trees-crc press. BocaRaton, Florida. google scholar
8. Chen, R.-C., ve Huang, S.-L. (2009). A new method for indoor location base on radio frequency identification. Paper presented at the WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering. google scholar

9. Correa, A., Llado, M. B., Morell, A., ve Vicario, J. L. (2016). Indoor pedestrian tracking by on-body multiple receivers. IEEE Sensors Journal, 16(8), 2545-2553. google scholar
10. Çalık, S. H., & Gülgen, F. (2021). Artırılmış gerçeklik teknolojisi ile iç mekân navigasyonu. Türkiye Coğrafi Bilgi Sistemleri Dergisi, 3(1), 48-52. google scholar
11. Çubukçu, A., Kuncan, M., Kaplan, K., & Ertunc, H. M. (2015). Development of a voice-controlled home automation using Zigbee module. Paper presented at the 2015 23nd Signal Processing and Communications Applications Conference (SIU). google scholar
12. Dardan, D., Closas, P., ve Djuric, P. M. (2015). Indoor tracking: Theory, methods, and technologies. IEEE Transactions on Vehicular Technology, 64(4), 1263-1278. google scholar Frank, A. (2010). UCI machine learning repository. http://archive.ics.uci.edu/ml. google scholar
13. Gama, J. (2004). Functional trees. Machine learning, 55(3), 219-250. google scholar
14. Geurts, P., Ernst, D., ve Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3-42. google scholar
15. Gu, Y., Lo, A., ve Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications surveys ve tutorials, 11(1), 13-32. google scholar
16. Homayounvala, E., Nabati, M., Shahbazian, R., Ghorashi, S. A., & Moghtadaiee, V. (2019). A novel smartphone application for indoor positioning of users based on machine learning. Paper presented at the Adjunct proceedings of the 2019 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2019 ACM international symposium on wearable computers. google scholar
17. Huang, X., Wang, F., Zhang, J., Hu, Z., ve Jin, J. (2019). A posture recognition method based on indoor positioning technology. Sensors, 19(6), 1464. google scholar
18. Hulten, G., Spencer, L., ve Domingos, P. (2001). Mining time-changing data streams. Paper presented at the Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. google scholar
19. Joshi, R. (2016). Accuracy, precision, recall ve f1 score: Interpretation of performance measures. Retrieved April, 1(2018), 2016. google scholar
20. Kohavi, R. (1996). Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. Paper presented at the Kdd. google scholar
21. Kuncan, M., & Ömer, Ç. (2019). Akıllı Ev Teknolojisi için Kablosuz Akıllı Kit. Avrupa Bilim ve Teknoloji Dergisi(17), 271-282. google scholar
22. Landwehr, N., Hall, M., ve Frank, E. (2005). Logistic model trees. Machine learning, 59(1-2), 161-205. google scholar
23. Lin, C.-J., Lee, T.-L., Syu, S.-L., ve Chen, B.-W. (2010). Application of intelligent agent and RFID technology fo indoor position: Safety of kindergarten as example. Paper presented at the 2010 International Conference on Machine Learning and Cybernetics. google scholar
24. Mert, T., Ferdi, K., & Hakan, K. (2020). Dinamik Yapay Sinir Ağı ile İç Mekân Konum Kestirimi. El-Cezeri Journal of Science and Engineering, 7(2), 858-870. google scholar
25. Quinlan, J. R. (1987). Simplifying decision trees. International journal of man-machine studies, 27(3), 221-234. google scholar
26. Quinlan, R. (1993). 4.5: Programs for machine learning morgan kaufmann publishers inc. San Francisco, USA. google scholar
27. Randell, C., ve Muller, H. (2001). Low cost indoor positioning system. Paper presented at the International Conference on Ubiquitous Computing. google scholar
28. Rida, M. E., Liu, F., Jadi, Y., Algawhari, A. A. A., ve Askourih, A. (2015). Indoor location position based on bluetooth signal strength. Paper presented at the 2015 2nd International Conference on Information Science and Control Engineering. google scholar
29. Rohra, J. G., Perumal, B., Narayanan, S. J., Thakur, P., ve Bhatt, R. B. (2017). User localization in an indoor environment using fuzzy hybrid of particle swarm optimization ve gravitational search algorithm with neural networks. Paper presented at the Proceedings of Sixth International Conference on Soft Computing for Problem Solving. google scholar
30. Roy, P., & Chowdhury, C. (2021). A survey of machine learning techniques for indoor localization and navigation systems. Journal of Intelligent & Robotic Systems, 101(3), 1-34. google scholar
31. Sabanci, K., Yigit, E., Ustun, D., Toktas, A., ve Aslan, M. F. (2018). Wifi based indoor localization: application and comparison of machine learning algorithms. Paper presented at the 2018 XXIIIrd International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED). google scholar
32. Saenz, M., ve Sanchez, J. (2009). Indoor position and orientation for the blind. Paper presented at the International Conference on Universal Access in Human-Computer Interaction. google scholar
33. Seco, F., Jimenez, A. R., ve Zampella, F. (2013). Joint estimation of indoorposition and orientation from RF signal strength measurements. Paper presented at the International Conference on Indoor Positioning and Indoor Navigation. google scholar
34. Shi, H. (2007). Best-first decision tree learning. The University of Waikato, google scholar
35. Shi, J. (2013). The Challenges of Indoor Positioning. National University of Singapore: Singapore. google scholar
36. Srinivasan, D. B., ve Mekala, P. (2014). Mining social networking data for classification using REPTree. International Journal ofAdvance Research in Computer Science and Management Studies, 2(10). google scholar
37. Su, H.-K., Lıao, Z.-X., Lin, C.-H., ve Lin, T.-M. (2015). A hybrid indoor-position mechanism based on bluetooth and WiFi communications for smart mobile devices. Paper presented at the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB). google scholar
38. Subhan, F., Hasbullah, H., ve Ashraf, K. (2013). Kalman filter-based hybrid indoor position estimation technique in bluetooth networks. International Journal ofNavigation and Observation, 2013. google scholar
39. Taşer, P. Y., & Akram, V. (2021). Kapalı ortamlarda gerçek zamanlı kişi tespitinde makine öğrenmesi algoritmalarının karşılaştırmalı başarım analizi. Academic Platform Journal of Engineering and Science, 9(1), 182-193. google scholar
40. Van Diggelen, F., ve Abraham, C. (2001). Indoor GPS technology. CTIA Wireless-Agenda, Dallas, 89. google scholar
41. Wang, Z., Yang, Z., ve Dong, T. (2017). A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors, 17(2), 341. google scholar
42. Yasir, M., Ho, S.-W., ve Vellambi, B. N. (2015). Indoor position tracking using multiple optical receivers. Journal of Lightwave Technology, 34(4), 1166-1176. google scholar
43. Zhang, H., ve Ye, C. (2020). A visual positioning system for indoor blind navigation. Paper presented at the 2020 IEEE International Conference on Robotics and Automation (ICRA). google scholar