Abstract
Fingerprint indoor positioning technology is one of the most attractive and promising techniques for mobile devices positioning. However, it is also time consuming for building the radio map offline and cannot provide reliable accuracy due to the changing environment. In response to this compelling problem, a client-assisted (CA) approach is proposed for radio map construction based on multi-user collaboration. In this method, multi-dimensional scaling (MDS) approach is used to transform the distance to two dimensional data. MDS as an set of analytical technique has been used for many years in fields like economics and marketing research. It is a suitable for reducing the data dimensionality to points in two or three dimensional space. In CA, this can be used where only distances between users are known which are used as an input data. All the client data is collected at one point because of the centralized nature of the MDS. It is advantageous in that, MDS can reconstruct the relative map of the network even when there are no anchor clients (clients with a priori known location). Given a sufficient number of known client locations, MDS generates accurate position estimation enabling local map to be transformed into an absolute map.
Based on gradient features of users’ walking speed, solve-stuck (SS) method is adopted to improve the efficiency by reducing calculation complexity and solving “data drift” problem. Radio map with a small number of labeled fingerprints can be self-updated by iterating the distance between users. Kalman filter (KF) method is used to remove the noise to make the trajectory closer to the ideal trajectory. We further demonstrate the influence of density distribution and time-cost of different number of clients. The experimental results show that CA approach can improve positioning accuracy with acceptable time-cost.
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References
Roy, S., Sarkar, S., Tah, A.: A Bluetooth-based autonomous mining system. In: Mohapatra, D.P., Patnaik, S. (eds.) Intelligent Computing, Networking, and Informatics. AISC, vol. 243, pp. 57–65. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1665-0_6
Bahld, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the IEEE Conference on Computer and Communications Societies, vol. 2, pp. 775–784 (2000)
Eleni, B., Demosthenes, V., Nikalaos, N.: Localization error modeling of hybrid fingerprint-based techniques for indoor ultra-wide band systems. Telecommun. Syst. 63(2), 223–241 (2016)
Sorour, S., Lostanlen, Y., Valaee, S., Majeed, K.: Joint indoor localization and radio map construction with limited deployment load. IEEE Trans. Mob. Comput. 14(5), 1031–1043 (2015)
Tran, D.A., Zhang, T.: Fingerprint-based location tracking with hodrick-prescott filtering. In: Wireless and Mobile Networking Conference (WMNC), pp. 1–8 (2014)
Le, T.D., Le, H.M., Nguyen, N.Q., Tran, D., Nguyen, N.T.: Convert Wi-Fi signals for fingerprint localization algorithm. In: 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–5, 23–25 September 2011
Zhou, M., Tang, Y.: GrassMA: graph-based semi-supervised manifold alignment for indoor WLAN localization. IEEE Sens. J. https://doi.org/10.1109/jsen.2017.2752844
Zhang, L., Ma, L., Xu, Y.: A semi-supervised indoor localization method based on l1-graph algorithm. J. Harbin Inst. Technol. (New Series) 22(4), 55–61 (2015)
Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., Choudhury, R.R.: No need to war-drive: Unsupervised indoor localization. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, pp. 197–210 (2012)
Unsupervised learning for crowdsourced indoor localization in wireless networks. IEEE Trans. Mob. Comput. 15(11) (2016)
Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications, 2nd edn., pp. 207–212. Springer, New York (2005). ISBN 0-387-94845-7
Yang, C., Shao, H.-R.: WiFi-based indoor positioning. IEEE Commun. Mag. 53, 150–157 (2015)
Ji, Z., Li, B.-H., Wang, H.-X.: A new indoor ray-tracing propagation prediction model. In: Computational Electromagnetics and Its Applications
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Zhang, Y., Tang, J., Elimu, M., Bian, N. (2018). A Client-Assisted Approach Based on User Collaboration for Indoor Positioning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_14
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DOI: https://doi.org/10.1007/978-3-319-97310-4_14
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