Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN
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Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.
KeywordsLocation estimation Localization Distributed systems Wireless Local Area Network (WLAN) Indoor localization Subtract on Negative Add on Positive (SNAP)
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- 2.Ahmad, U., Gavrilov, A., Lee, S., & Lee, Y. (2008). A modular classification model for received signal strength based location systems. Neuro Computing, 71(13–15), 2669.Google Scholar
- 3.Bahl, P., & Padmanabhan, V. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of 19th IEEE annual joint conference of computer and communications societies, INFOCOM 2000 (Vol. 2, pp. 775–784).Google Scholar
- 4.Krishnan, P., Krishnakumar, A., Ju, W.H., Mallows, C., & Gamt, S. (2004). A system for lease: location estimation assisted by stationary emitters for indoor rf wireless networks. In Proceedings of 23rd IEEE annual joint conference of computer and communications societies, INFOCOM 2004 (Vol. 2, pp. 1001–1011). doi: 10.1109/INFCOM.2004.1356987.
- 5.Youssef, M., Agrawala, A., & Udaya Shankar, A. (2003). Wlan location determination via clustering and probability distributions. In Proceedings of the first IEEE international conference on pervasive computing and communications, (PerCom 2003) (pp. 143–150). IEEE.Google Scholar
- 7.Bahl, P., Padmanabhan, V., & Balachandran, A. (2000). Enhancements to the RADAR user location and tracking system. Microsoft Research, (MSR-TR-2000-12) pp. 13.Google Scholar
- 8.Battiti, R., Nhat, T., & Villani, A. (2002). Location-aware computing: a neural network model for determining location in wireless LANs. Technical Report DIT-02-083, Ingegneria e Scienza dell’Informazione, University of Trento.Google Scholar
- 11.Ahmad U., Gavrilov A., Lee Y., Lee S. (2008) Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems. Soft Computing-A Fusion of Foundations, Methodologies and Applications 12(7): 699–713Google Scholar
- 12.Chen, C. (2005). Hybrid location estimation and tracking system for mobile devices. In Proceedings of 61st IEEE vehicular technology conference, VTC 2005 (Vol. 4, pp. 2648–2652).Google Scholar
- 13.Ding, X., Li, H., Li, F., & Wu, J. (2008). A novel infrastructure WLAN locating method based on neural network. In Proceedings of 4th Asian conference on internet engineering (pp. 47–55).Google Scholar
- 14.Gupta, A., Tapaswi, & S., Jain, V. (2009). Recurrent grid based voting approach for location estimation in wireless sensor networks. In Proceedings of symposia and workshops on ubiquitous, autonomic and trusted computing, UIC-ATC’09 (pp. 263–267).Google Scholar
- 16.Jain, V., Tapaswi, S., & Shukla, A. (2010). Distributed growing radial basis function neural networks for location estimation in indoor wireless networks. In 6th International conference on wireless communications networking and mobile computing (WiCOM) (pp. 1–6). doi: 10.1109/WICOM.2010.5601059.
- 18.Laoudias, C., Michaelides, M., & Panayiotou, C. (2011). Fault tolerant fingerprint-based positioning. In IEEE international conference on communications (ICC) (pp. 1–5). doi: 10.1109/icc.2011.5963136.
- 19.Qiang Yang Sinno Jialin Pan, V.W.Z. (2007). IEEE ICDM Data Mining Contest 2007. http://www.cse.ust.hk/~qyang/ICDMDMC07/. Accessed 20 April 2011.