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NRSSD: Normalizing Received Signal Strength to Address Device Diversity Problem in Fingerprinting Positioning

Volume 15, Number 3, March 2019, pp. 1033-1044
DOI: 10.23940/ijpe.19.03.p33.10331044

Chunxiu Lia, Jianli Zhaoa, Qiuxia Sunb, Xiang Gaoa, Guoqiang Suna, and Chendi Zhua

aSchool of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266000, China
bSchool of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266000, China.


(Submitted on November 10, 2018; Revised on December 13, 2018; Accepted on January 14, 2019)

Abstract:

The WiFi-based fingerprinting technique is widely adopted for indoor positioning due to its cost-effectiveness compared to other infrastructure-based positioning methods. However, the WiFi-based technique still faces the problem of device diversity in the application of an indoor positioning system. Previous studies have faced two main challenges. One is the curse of computational dimensionality in online positioning, while the other is the issue of low positioning accuracy in real applications. In this paper, we propose to normalize the observable Access Point (AP) signal strength to eliminate the influence of device diversity and avoid a dimension disaster. Experimental results show that our algorithm based on the normalization Received Signals Strength (RSS) not only solves the problem of device diversity but also outperforms three other baseline methods.

 

References: 17

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B. Buchli, “GPS-Equipped Wireless Sensor Network Node for High-Accuracy Positioning Applications,in Proceedings of the 9th European Conference on Wireless Sensor Networks, pp. 179-195, Trento, Italy, 2012

S. N. He and S. –H. G. Chan, Wi-Fi Fingerprint-based Indoor Positioning: Recent Advances and Comparisons, IEEE Communications Surveys & Tutorials, Vol. 18, No. 1, pp. 466-490, 2017

A. K. M. M. Hossain and W. S. Soh, A Survey of Calibration-Free Indoor Positioning Systems,” Computer Communications, Vol. 66, No. 15, pp. 1-13, July 2015

L. Z. Qiu and C. Liu, An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning, Wireless Personal Communications, Vol. 96, No. 2, pp. 1-13, September 2017

A. W. Tsui, Y. H. Chuang, and H. H. Chu, “Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization,” Mobile Networks & Applications, Vol. 14, No. 5, pp. 677-691, October 2009

H. Cheng, F. Wang, R. Tao, H. Luo, and F. Zhao, Clustering Algorithms Research for Device-Clustering Localization, in Proceedings of International Conference on Indoor Positioning and Indoor Navigation IEEE, pp. 1-7, Sydney, NSW, Australia, 2012

J. G. Park, D. Curtis, S. Teller, and J. Ledlie, “Implications of Device Diversity for Organic Localization,” in Proceedings of 2011 IEEE INFOCOM, pp. 3182-3190, Shanghai, China. April 2011

L. H. Chen, H. K. Wu, M. H. Jin, and G. H. Chen, “Homogeneous Features Utilization to Address the Device Heterogeneity Problem in Fingerprint Localization,” IEEE Sensors Journal, Vol. 14, No. 4, pp. 998-1005, November 2014

C. Laoudias, D. Zeinalipour-Yazti, and C. G. Panayiotou, “Crowdsourced Indoor Localization for Diverse Devices Through Radiomap Fusion,” in Proceedings of International Conference on Indoor Positioning and Indoor Navigation, pp. 1-7, Montbeliard-Belfort, France, 2013

C. Laoudias and C. G. Panayiotou, “Device Self-Calibration in Location Systems using Signal Strength Histograms,” Journal of Location Based Services, Vol. 7, No. 3, pp. 165-181, September 2013

C. Figuera, “Time-Space Sampling and Mobile Device Calibration for WiFi Indoor Location Systems,” IEEE Transactions on Mobile Computing, Vol. 10, No. 7, pp. 913-926, May 2011

A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki, “Practical Robust Localization over Large-Scale 802.11 Wireless Networks,” in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, pp. 70-84, 2004

F. Dong, Y. Chen, J. Liu, Q. Ning, and S. Piao, “A Calibration Free Localization Solution for Handling Signal Strength Variance,” Mobile Entity Localization and Tracking in GPS less Environments, Springer Berlin Heidelberg, pp. 79-90, 2009

M. B. Kjærgaard, “Indoor Location Fingerprinting with Heterogeneous Clients,” Pervasive and Mobile Computing, Vol. 7, No. 1, pp. 31-43, February 2010
 
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