1. Home
  2. Vol 7, No 5 (2023)
  3. Yaro

A Two-Nearest Wireless Access Point-Based Fingerprint Clustering Algorithm for Improved Indoor Wireless Localization

Abdulmalik Shehu Yaro, Filip Malý, Karel Malý

Abstract


Fingerprint database clustering is one of the methods used to reduce localization time and improve localization accuracy in a fingerprint-based localization system. However, optimal selection of initial hyperparameters, higher computation complexity, and interpretation difficulty are among the performance-limiting factors of these clustering algorithms. This paper aims to improve localization time and accuracy by proposing a clustering algorithm that is extremely efficient and accurate at clustering fingerprint databases without requiring the selection of optimal initial hyperparameters, is computationally light, and is easily interpreted. The two closest wireless access points (APs) to the reference location where the fingerprint is generated, as well as the labels of the two APs in vector form, are used by the proposed algorithm to cluster fingerprints. The simulation result shows that the proposed clustering algorithm has a localization time that is at least 45% faster and a localization accuracy that is at least 25% higher than the k-means, fuzzy c-means, and lightweight maximum received signal strength clustering algorithms. The findings of this paper further demonstrate the real-time applicability of the proposed clustering algorithm in the context of indoor wireless localization, as low localization time and higher localization accuracy are the main objectives of any localization system.

 

Doi: 10.28991/ESJ-2023-07-05-019

Full Text: PDF


Keywords


Clustering; C-means; K-means; RSS; Fingerprinting; Localization; Position Error.

References


Yaro, A. S., Maly, F., & Prazak, P. (2023). A Survey of the Performance-Limiting Factors of a 2-Dimensional RSS Fingerprinting-Based Indoor Wireless Localization System. Sensors, 23(5), 2545. doi:10.3390/s23052545.

Yang, T., Cabani, A., & Chafouk, H. (2021). A survey of recent indoor localization scenarios and methodologies. Sensors, 21(23), 8086. doi:10.3390/s21238086.

Shang, S., & Wang, L. (2022). Overview of WiFi fingerprinting-based indoor positioning. IET Communications, 16(7), 725–733. doi:10.1049/cmu2.12386.

Kriz, P., Maly, F., & Kozel, T. (2016). Improving Indoor Localization Using Bluetooth Low Energy Beacons. Mobile Information Systems, 2016, 1–11. doi:10.1155/2016/2083094.

Fronckova, K., & Prazak, P. (2020). Possibilities of using Kalman filters in indoor localization. Mathematics, 8(9), 1564. doi:10.3390/math8091564.

Alhomayani, F., & Mahoor, M. H. (2020). Deep learning methods for fingerprint-based indoor positioning: a review. Journal of Location Based Services, 14(3), 129–200. doi:10.1080/17489725.2020.1817582.

Xu, D., & Tian, Y. (2015). A Comprehensive Survey of Clustering Algorithms. Annals of Data Science, 2(2), 165–193. doi:10.1007/s40745-015-0040-1.

Quezada-Gaibor, D., Torres-Sospedra, J., Nurmi, J., Koucheryavy, Y., & Huerta, J. (2021). Lightweight Wi-Fi Fingerprinting with a Novel RSS Clustering Algorithm. 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). doi:10.1109/ipin51156.2021.9662612.

Gomes, E. L., Fonseca, M., Lazzaretti, A. E., Munaretto, A., & Guerber, C. (2022). Clustering and Hierarchical Classification for High-Precision RFID Indoor Location Systems. IEEE Sensors Journal, 22(6), 5141–5149. doi:10.1109/JSEN.2021.3103043.

Marakkalage, S. H., Lau, B. P. L., Zhou, Y., Liu, R., Yuen, C., Yow, W. Q., & Chong, K. H. (2021). WiFi Fingerprint Clustering for Urban Mobility Analysis. IEEE Access, 9, 69527–69538. doi:10.1109/ACCESS.2021.3077583.

Ezhumalai, B., Song, M., & Park, K. (2021). An efficient indoor positioning method based on wi-fi RSS fingerprint and classification algorithm. Sensors, 21(10), 3418. doi:10.3390/s21103418.

Liu, S., De Lacerda, R., & Fiorina, J. (2021). WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). doi:10.1109/vtc2021-spring51267.2021.9448961.

Zhao, H. (2022). Design and Implementation of an Improved K-Means Clustering Algorithm. Mobile Information Systems, 2022, 1–10. doi:10.1155/2022/6041484.

Sun, Y., Xu, Y., Ma, L., & Deng, Z. (2009). KNN-FCM hybrid algorithm for indoor location in WLAN. 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS). doi:10.1109/PEITS.2009.5406793.

Subedi, S., Gang, H. S., Ko, N. Y., Hwang, S. S., & Pyun, J. Y. (2019). Improving indoor fingerprinting positioning with affinity propagation clustering and weighted centroid fingerprint. IEEE Access, 7, 31738–31750. doi:10.1109/ACCESS.2019.2902564.

Wu, Y., Jia, Z., Dai, Y., & Wang, W. (2022). Research on WiFi Location Fingerprint Positioning Algorithm Based on DPC-FCM Clustering. Wireless Communications and Mobile Computing, 2022, 1–7. doi:10.1155/2022/8347084.

Nurhidayat, I., Pimpunchat, B., Noeiaghdam, S., & Fernández-Gámiz, U. (2022). Comparisons of SVM kernels for insurance data clustering. Emerging Science Journal, 6(4), 866-880. doi:10.28991/ESJ-2022-06-04-014.

Yaro, A. S., Maly, F., & Maly, K. (2023). Improved Indoor Localization Performance Using a Modified Affinity Propagation Clustering Algorithm with Context Similarity Coefficient. IEEE Access, 11, 57341–57348. doi:10.1109/ACCESS.2023.3283592.

Hung, S. L., Kao, C. Y., & Huang, J. W. (2022). Constrained K-means and Genetic Algorithm-based Approaches for Optimal Placement of Wireless Structural Health Monitoring Sensors. Civil Engineering Journal, 8(12), 2675-2692. doi:10.28991/CEJ-2022-08-12-01.

Nagaraj, P., Birunda, S. S., Venkatesh, R., Muneeswaran, V., Narayanan, S. K., Shree, U. D., & Sunethra, B. (2022). Automatic and Adaptive Segmentation of Customer in R framework using K-means Clustering Technique. 2022 International Conference on Computer Communication and Informatics (ICCCI). doi:10.1109/iccci54379.2022.9741067.

Klus, L., Quezada-Gaibor, D., Torres-Sospedra, J., Lohan, E. S., Granell, C., & Nurmi, J. (2020). RSS Fingerprinting Dataset Size Reduction Using Feature-Wise Adaptive k-Means Clustering. 2020 12th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT). doi:10.1109/icumt51630.2020.9222458.

Caso, G., de Nardis, L., & di Benedetto, M. G. (2015). A Mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning. Sensors (Switzerland), 15(11), 27692–27720. doi:10.3390/s151127692.

Sadowski, S., Spachos, P., & Plataniotis, K. N. (2020). Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things. IEEE Internet of Things Journal, 7(11), 10996–11005. doi:10.1109/JIOT.2020.2992651.

Alhmiedat, T. (2023). Fingerprint-Based Localization Approach for WSN Using Machine Learning Models. Applied Sciences (Switzerland), 13(5), 3037. doi:10.3390/app13053037.


Full Text: PDF

DOI: 10.28991/ESJ-2023-07-05-019

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Abdulmalik Shehu Yaro, Filip Maly, Karel Malý