Abstract
Indoor localization of nodes in Wireless Sensor Network(WSN) is impeded by several factors: unreliability of wireless link quality, variety of interference factors, low accuracy of localization. To solve the problem of indoor localization, we propose an range-free algorithm for WSN-based systems. We build several classes of samples that have different similarity by dividing the k-nearest neighbor. Our algorithm are mainly composed of two parts. First, we filter the original samples to remove the noise data out of the original samples. Second, we reduce dimension to overcome the impact of lost packets. The simulation results show that our algorithm can improve the accuracy of localization and the anti-interference capability.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Torres-Solis, J., Falk, T.H., et al.: A review of indoor localization technologies: towards navigational assistance for topographical disorientation. Ambient Intelligence, 51–84 (2010)
Mitilineos, S.A., Kyriazanos, D.M., Segou, O.E.: Indoor Localization with Wireless Sensor Networks. Progress in Electromagnetics Research 109, 441–474 (2010)
Rohrig, C., Muller, M.: Indoor location tracking in non-line-of-sight environments using a IEEE 802.15. 4a wireless network. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009. IEEE (2009)
Nagpal, R., Shrobe, H., Bachrach, J.: Organizing a global coordinate system from local information on an ad hoc sensor network. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 333–348. Springer, Heidelberg (2003)
Kjærgaard, M.B.: A taxonomy for radio location fingerprinting. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 139–156. Springer, Heidelberg (2007)
Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. In: Proceedings of the IEEE Infocom, Israel (2000)
Zhou, G., He, T., Krishnamurthy, S., Stankovic, J.A.: Impact of Radio Irregularity on Wireless Sensor Networks. In: Proceedings of MobiSys 2004, Boston, MA (2004)
Elnahraway, E., Li, X., Martin, R.P.: The limits of localization using RSS. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems, Baltimore, MD, USA (2004)
Indoor Localization System using Wireless Sensor Networks for Stationary and Moving Target (2009)
Welch, G., Bishop, G.: An introduction to the Kalman filter (1995)
Paul, A.S., Wan, E.A.: RSSI-based indoor localization and tracking using sigma-point kalman smoothers. IEEE Journal of Selected Topics in Signal Processing 3(5), 860–873 (2009)
Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C., et al. (eds.) CoopIS/DOA/ODBASE 2003. LNCS, vol. 2888, pp. 986–996. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y., Liu, Z., Li, Y., Wang, Y. (2014). Indoor Localization Algorithm for Wireless Sensor Network Based on Range-Free. In: Sun, L., Ma, H., Hong, F. (eds) Advances in Wireless Sensor Networks. CWSN 2013. Communications in Computer and Information Science, vol 418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54522-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-54522-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54521-4
Online ISBN: 978-3-642-54522-1
eBook Packages: Computer ScienceComputer Science (R0)
