A New Indoor Positioning Approach based on Weighted K-Nearest Algorithm

Abdurrhaman Ademola Isa, Jimoh Akanni, Amuda Yusuf Abdulrahman, Rasaq Atanda Alao, Olalekan Ogunbiyi

Abstract


Many contemporary technological services rely heavily on precise location data within smartphone applications, making accuracy a crucial aspect of indoor positioning systems. However, the variability in received signal strength (RSS) poses a challenge for achieving exact locations in Wi-Fi indoor positioning algorithms. Traditional weighted k-nearest neighbor (WkNN) techniques typically utilize RSS spatial distance for selecting reference points (RPs) to estimate locations. To enhance position accuracy, this study introduces a novel indoor positioning method based on WkNN. By incorporating three geometrical distances of RSS (physical, spatial, and Canberra), this approach selects RPs and conducts position estimation using a fusion weighted strategy based on these distances. Experimental findings indicate that the newly proposed method outperforms the nearest neighbor (NN) technique. Moreover, comparative investigations demonstrate its superiority over k-nearest neighbor (kNN) and weighted k-nearest neighbor (WkNN) algorithms. Compared to NN, kNN, and WkNN algorithms, this novel technique improves positioning accuracy by approximately 49.9%, 32%, and 25%, respectively.


Keywords


Wi-Fi; Access points; Nearest neighbor; Indoor positioning; RSS

References


X. Lin, J. Gan, C. Jiang, S. Xue, and Y. Liang, “Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach,” Sensors, vol. 23, no. 14, p. 6320, Jul. 2023.

S.-H. Lee, C.-H. Cheng, C.-C. Lin, and Y.-F. Huang, “PSO-Based Target Localization and Tracking in Wireless Sensor Networks,” Electronics, vol. 12, no. 4, p. 905, Feb. 2023.

Y. Fen Yong, C. Keong Tan, I. Kim Teck Tan, and S. Wei Tan, “Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization,” Computers, Materials & Continua, vol. 74, no. 1, pp. 1801–1818, 2023.

L. Botler, K. Diwold, and K. Roemer, “Improving Signal-Strength-based Distance Estimation in UWB Transceivers,” in Proceedings of Cyber-Physical Systems and Internet of Things Week 2023, San Antonio TX USA: ACM, pp. 61–66, May 2023.

M. Liu, H. Wang, Y. Yang, Y. Zhang, L. Ma, and N. Wang, “RFID 3-D indoor localization for tag and tag-free target based on interference,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3718–3732, 2018.

S. Bai, Y. Luo, and Q. Wan, “Transfer Learning for Wireless Fingerprinting Localization Based on Optimal Transport,” Sensors, vol. 20, no. 23, p. 6994, Dec. 2020.

S. He and S.-H. G. Chan, “Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 466–490, 2016.

N. Singh, S. Choe, and R. Punmiya, “Machine learning based indoor localization using Wi-Fi RSSI fingerprints: An overview,” IEEE Access, vol. 9, pp. 127150–127174, 2021.

S. Urwan, D. R. Wysocka, A. Pietrzak, and K. K. Cwalina, “Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach,” International Journal of Electronics and Telecommunications, vol. 63, no. 3, pp. 594–607, Nov. 2023.

S. A. Zibaei and R. Ali Abbaspour, “Evaluation of Improved K-Nearest Neighbors for Indoor Positioning System in Real Complex Buildings,” in 2023 9th International Conference on Web Research (ICWR), Tehran, Iran, Islamic Republic of: IEEE, pp. 12–19, May 2023.

K. Pahlavan and P. Krishnamurthy, “Evolution and Impact of Wi-Fi Technology and Applications: A Historical Perspective,” International Journal of Wireless Information Networks, vol. 28, no. 1, pp. 3–19, Mar. 2021.

S. Zhang, J. Guo, N. Luo, L. Wang, W. Wang, and K. Wen, “Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm,” Remote Sensing, vol. 11, no. 6, p. 652, Mar. 2019.

W. Yanhua, W. DongH, and Z. Yan, “Axial decoupled LS-SVMs for indoor positioning using RSS fingerprints,” in 2015 34th Chinese Control Conference (CCC), Hangzhou, China: IEEE, pp. 3920–3925, Jul. 2015.

E. L. Gomes, M. Fonseca, A. E. Lazzaretti, A. Munaretto, and C. Guerber, “Clustering and Hierarchical Classification for High-Precision RFID Indoor Location Systems,” IEEE Sensors J., vol. 22, no. 6, pp. 5141–5149, Mar. 2022.

M. Mallik, A. K. Panja, and C. Chowdhury, “Paving the way with machine learning for seamless indoor–outdoor positioning: A survey,” Information Fusion, vol. 94, pp. 126–151, Jun. 2023.

M. Zhang, J. Jia, J. Chen, L. Yang, L. Guo, and X. Wang, “Real-time indoor localization using smartphone magnetic with LSTM networks,” Neural Computer & Application, vol. 33, no. 16, pp. 10093–10110, Aug. 2021.

V. F. Mirama, L. E. Diez, A. Bahillo, and V. Quintero, “A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges,” IEEE Access, vol. 9, pp. 120138–120157, 2021.

J. Ni et al., “Experience: pushing indoor localization from laboratory to the wild,” in Proceedings of the 28th Annual International Conference on Mobile Computing and Networking, Sydney NSW Australia: ACM, pp. 147–157, Oct. 2022.

C.-H. Chen and M.-C. Chen, “A novel position estimation method using accelerometer based error correction,” Engineering Computations, vol. 33, no. 6, pp. 1784–1799, Aug. 2016.

A. Papapostolou and H. Chaouchi, “WIFE: Wireless Indoor Positioning Based on Fingerprint Evaluation,” in NETWORKING 2009, L. Fratta, H. Schulzrinne, Y. Takahashi, and O. Spaniol, Eds., in Lecture Notes in Computer Science, vol. 5550. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 234–247, 2009.

T. Ishihara, K. M. Kitani, C. Asakawa, and M. Hirose, “Deep Learning and Geometry-based Image Localization Enhanced by Bluetooth Signals,” Journal of Information Processing, vol. 26, no. 0, pp. 707–717, 2018.

T. Manabe and K. Saba, “Performance Evaluation of Wi-Fi RTT Lateration without Pre-Constructing a Database,” IEICE Trans. Fundamentals, vol. E106.A, no. 5, pp. 765–774, May 2023.

G. M. Mendoza-Silva, J. Torres-Sospedra, J. Huerta, R. Montoliu, F. Benítez, and O. Belmonte, “Situation Goodness Method for Weighted Centroid-Based Wi-Fi APs Localization,” in Progress in Location-Based Services 2016, G. Gartner and H. Huang, Eds., in Lecture Notes in Geoinformation and Cartography. Cham: Springer International Publishing, pp. 27–47, 2017.

D. Deliyska, N. Yanev, and M. Trifonova, “Methods for developing an indoor navigation system,” E3S Web Conf., vol. 280, p. 04001, 2021.

N. Kim and Y. Kim, “A Novel RSS-Ratio Position Estimation Scheme for Wi-Fi Networks,” in Proceedings of the 2015 International Conference on Electrical, Electronics and Mechatronics, Phuket, Thailand: Atlantis Press, 2015.

A. Raza, L. Lolic, S. Akhter, and M. Liut, “Comparing and Evaluating Indoor Positioning Techniques,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain: IEEE, pp. 1–8, Nov. 2021.

M. N. Husen and S. Lee, “Indoor human localization with orientation using WiFi fingerprinting,” in Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, Siem Reap Cambodia: ACM, pp. 1–6, Jan. 2014.

D. Almeida, E. Pedrosa, and F. Curado, “Magnetic Mapping for Robot Navigation in Indoor Environments,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, Spain: IEEE, pp. 1–8, Nov. 2021.

M. Zhang, S. Zhang, and J. Cao, “Fusing received signal strength from multiple access points for WLAN user location estimation,” in 2008 International conference on internet computing in science and engineering, pp. 173–180, IEEE, 2008.

J. F. Tsai, M. H. Lin, and P. C. Wang, “An efficient deterministic approach to optimal design of reliable networks,” IEEE Transactions on Reliability, vol. 67, no. 2, pp. 598–608, 2018.

J. Machaj, P. Brida, and J. Benikovsky, “Impact of APs removal on accuracy of fingerprinting localization algorithms,” in 2015 38th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic: IEEE, pp. 1–5, Jul. 2015.

R. Ma, Q. Guo, C. Hu, and J. Xue, “An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion,” Sensors, vol. 15, no. 9, pp. 21824–21843, Aug. 2015.

C. Pei, Y. Cai, and Z. Ma, “An Indoor Positioning Algorithm Based on Received Signal Strength of WLAN,” in 2009 Pacific-Asia Conference on Circuits, Communications and Systems, Chengdu, China: IEEE, pp. 516–519, May 2009.

W. Meng, J. Wang, L. Peng, and Y. Xu, “ANFIS-Based Wireless LAN Indoor Positioning Algorithm,” in 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, China: IEEE, pp. 1–4. Sep. 2009.

V. Truong-Quang and T. Ho-Sy, “Maximum convergence algorithm for WiFi based indoor positioning system,” IJECE, vol. 11, no. 5, p. 4027, Oct. 2021.

A. Aboodi and T.-C. Wan, “Evaluation of WiFi-Based Indoor (WBI) Positioning Algorithm,” in 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, Vancouver, Canada: IEEE, pp. 260–264, Jun. 2012.

H. X. Jian and W. Hao, “WIFI Indoor Location Optimization Method Based on Position Fingerprint Algorithm,” in 2017 International Conference on Smart Grid and Electrical Automation (ICSGEA), Changsha: IEEE, pp. 585–588, May 2017.

M. E. Rusli, M. Ali, N. Jamil, and M. M. Din, “An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT),” in 2016 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia: IEEE, pp. 72–77, Jul. 2016.

H. L. Li, W. Quan, G. Ji, and Z. H. Qian, “Wireless Indoor Positioning Algorithm Based on PCA:” presented at the 2015 International Conference on Artificial Intelligence and Industrial Engineering, Phuket, Thailand, Phuket, Thailand, 2015.

S. B. Keser, A. Yazici, and S. Gunal, “An F-Score-Weighted Indoor Positioning Algorithm Integrating WiFi and Magnetic Field Fingerprints,” Mobile Information Systems, vol. 2018, pp. 1–8, 2018.

W. Zhang, X. Hua, K. Yu, W. Qiu, X. Chang, B. Wu, and X. Chen, “Radius based domain clustering for WiFi indoor positioning,” SR, vol. 37, no. 1, pp. 54–60, Jan. 2017.

H. Wang, X. Zhang, Y. Gu, L. Zhang, and J. Li, “Indoor Wi-Fi RSS-fingerprint location algorithm based on sample points clustering and AP reduction,” in 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP), Wuhan, China: IEEE, pp. 264–267, Nov. 2015.

M. Chai, C. Li, and H. Huang, “A New Indoor Positioning Algorithm of Cellular and Wi-Fi Networks,” Journal of Navigation, vol. 73, no. 3, pp. 509–529, May 2020.

A. A. Isa, J. Akanni, Y. A. Abdulrahman, R. A. Alao and K. Salaudeen, “Optimal Positioning of Wi-Fi Access Points towards Effective Indoor Positioning Systems,” Advances in Engineering Design and Technology, vol. 5, no 3, pp. 22-29, Sep. 2023.




DOI: http://dx.doi.org/10.12962/j20882033.v35i2.20249

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