An advanced fingerprint-based indoor localization scheme for WSNs

The purpose of this paper is to propose an advanced fingerprint-based indoor localization scheme for wireless sensor networks (WSN) to improve the accuracy. Many localization methods have been introduced for WSN systems using wireless signals mainly divided into two categories, which are range-based and range-free. As wireless ranging is not reliable in indoor environment due to multipath fading and other attenuations, range-free solutions are preferable indoors. A popular solution is RSSI fingerprint-based algorithm or its variances within KNN, R-KNN, and WKNN, which can achieve accurate localization without interference. However, random and unpredictable human presence and movement cause certain level of interference to reduce the accuracy of indoor localization. In this paper, LWMA scheme is introduced to filter interfered RSSI values and contribute to algorithm recover back to no interference condition. The experiments show that the scheme can improve the average positioning accuracy by 50% with standard deviation decrease 40% in interfered condition.

[1]  Kaveh Pahlavan,et al.  The Performance of Simulated Annealing Algorithms for Wi-Fi Localization Using Google Indoor Map , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[2]  Dario Petri,et al.  Accuracy of RSS-Based Centroid Localization Algorithms in an Indoor Environment , 2011, IEEE Transactions on Instrumentation and Measurement.

[3]  Fabrice Valois,et al.  Is RSSI a Good Choice for Localization in Wireless Sensor Network? , 2012, 2012 IEEE 26th International Conference on Advanced Information Networking and Applications.

[4]  Prathima Agrawal,et al.  ARIADNE: a dynamic indoor signal map construction and localization system , 2006, MobiSys '06.

[5]  Xianbin Wang,et al.  A New Positioning System Using DVB-T2 Transmitter Signature Waveforms in Single Frequency Networks , 2012, IEEE Transactions on Broadcasting.

[6]  Yunhao Liu,et al.  WILL: Wireless indoor localization without site survey , 2012, 2012 Proceedings IEEE INFOCOM.

[7]  Joe-Air Jiang,et al.  A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism , 2013, IEEE Sensors Journal.

[8]  Min Gao,et al.  FILA: Fine-grained indoor localization , 2012, 2012 Proceedings IEEE INFOCOM.

[9]  Yin Chen,et al.  FM-based indoor localization , 2012, MobiSys '12.

[10]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[11]  W. J. Klepczynski,et al.  GPS: primary tool for time transfer , 1999, Proc. IEEE.

[12]  Venkata N. Padmanabhan,et al.  Indoor localization without the pain , 2010, MobiCom.

[13]  John Krumm,et al.  Accuracy characterization for metropolitan-scale Wi-Fi localization , 2005, MobiSys '05.

[14]  Guoliang Xing,et al.  ZiFind: Exploiting cross-technology interference signatures for energy-efficient indoor localization , 2013, 2013 Proceedings IEEE INFOCOM.

[15]  Henry Tirri,et al.  A Statistical Modeling Approach to Location Estimation , 2002, IEEE Trans. Mob. Comput..

[16]  Chahé Nerguizian,et al.  Accuracy enhancement of an indoor ANN-based fingerprinting location system using Kalman filtering , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[17]  Brian D. Noble,et al.  Mobile network estimation , 2001, MobiCom '01.

[18]  Feng Yang,et al.  Weight adjust algorithm in indoor fingerprint localization , 2012, 2012 6th International Conference on Signal Processing and Communication Systems.

[19]  Peter Steenkiste,et al.  Efficient channel-aware rate adaptation in dynamic environments , 2008, MobiSys '08.

[20]  Timea Bagosi,et al.  Indoor localization by WiFi , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.