PEOPLE’S PPRESENCE EFFECT ON WLAN-BASED IPS’ ACCURACY

In order to enable Location Based Service (LBS) closed environment, many technologies have been investigated to replace the Global Positioning System (GPS) in the localization process in indoor environments. WLAN is considered as the most suitable and powerful technology for Indoor Positioning System (IPS) due to its widespread coverage and low cost. Although WLAN Received Signal Strength Indicator (RSS) fingerprinting can be considered as the most accurate IPS method, this accuracy can be weakened due to WLAN RSS fluctuation. WLAN RSS fluctuates due to the multipath being influenced by obstacles presence. People presence under WLAN coverage can be considered as one of the main obstacles which can affect the WLAN-IPS accuracy. This research presents experimental results demonstrating that people’s presence between access point (AP) and mobile device (MD) reduces the received signal strength by -2dBm to -5dBm. This reduction in RSS can lead to distance error greater than or equal to 2m. Hence, any accurate IPS must consider the presence of people in the indoor environment.

[1]  Lin Ma,et al.  Dynamic Radio Map Construction for WLAN Indoor Location , 2011, 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics.

[2]  Hui Wang,et al.  Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization , 2006, 2006 IEEE/ION Position, Location, And Navigation Symposium.

[3]  Roger Pierre Fabris Hoefel,et al.  IEEE 802.11 WLANs: A comparison on indoor coverage models , 2010, CCECE 2010.

[4]  Ignas Niemegeers,et al.  A survey of indoor positioning systems for wireless personal networks , 2009, IEEE Communications Surveys & Tutorials.

[5]  Y. Ebihara.  Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[6]  Yunhao Liu,et al.  Location, Localization, and Localizability , 2010, Journal of Computer Science and Technology.

[7]  Chin-Liang Wang,et al.  Design of an adaptive positioning system based on WiFi radio signals , 2009, Comput. Commun..

[8]  Rosdiadee Nordin,et al.  Recent Advances in Wireless Indoor Localization Techniques and System , 2013, J. Comput. Networks Commun..

[9]  Shih-Hau Fang,et al.  A Novel Algorithm for Multipath Fingerprinting in Indoor WLAN Environments , 2008, IEEE Transactions on Wireless Communications.

[10]  Chen Liang,et al.  Survey of WLAN fingerprinting positioning system , 2012, 2012 First National Conference for Engineering Sciences (FNCES 2012).

[11]  Noor Azurati Ahmad,et al.  Adapted Indoor Positioning Model Based on Dynamic WLAN Fingerprinting RadioMap , 2014, SoMeT.

[12]  Sinan Gezici,et al.  A Survey on Wireless Position Estimation , 2008, Wirel. Pers. Commun..

[13]  R. Mardeni,et al.  Path loss model development for indoor signal loss prediction at 2.4 GHz 802.11n network , 2012, 2012 International Conference on Microwave and Millimeter Wave Technology (ICMMT).

[14]  Elyes Ben Hamida,et al.  Investigating the impact of human activity on the performance of wireless networks — An experimental approach , 2010, 2010 IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).

[15]  I. Forkel,et al.  A multi-wall-and-floor model for indoor radio propagation , 2001, IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202).

[16]  Pedro José Marrón,et al.  A model for WLAN signal attenuation of the human body , 2013, UbiComp.

[17]  L. M. Kamarudin,et al.  The study of human movement effect on Signal Strength for indoor WSN deployment , 2013, 2013 IEEE Conference on Wireless Sensor (ICWISE).

[18]  Kevin Curran,et al.  A survey of active and passive indoor localisation systems , 2012, Comput. Commun..

[19]  S. Seidel,et al.  914 MHz path loss prediction models for indoor wireless communications in multifloored buildings , 1992 .

[20]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[21]  Jing Liu,et al.  Survey of Wireless Indoor Positioning Techniques and Systems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Franco Mazzenga,et al.  Channel models for IEEE 802.11b indoor system design , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[23]  Petros Karadimas,et al.  Human Body Shadowing Characterization for 60-GHz Indoor Short-Range Wireless Links , 2013, IEEE Antennas and Wireless Propagation Letters.

[24]  Prashant Krishnamurthy,et al.  Analysis of WLAN's received signal strength indication for indoor location fingerprinting , 2012, Pervasive Mob. Comput..

[25]  David R. Frelinger,et al.  Global Positioning System: Assessing National Policies , 1996 .