A model for WLAN signal attenuation of the human body

Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).

[1]  Udo Frese,et al.  Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping , 2006, Auton. Robots.

[2]  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).

[3]  Srihari Nelakuditi,et al.  SpinLoc: spin once to know your location , 2012, HotMobile '12.

[4]  Wolfgang Effelsberg,et al.  COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses , 2006, WINTECH.

[5]  Shahrokh Valaee,et al.  Orientation-aware indoor localization using affinity propagation and compressive sensing , 2009, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

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

[7]  Simo Ali-Löytty,et al.  A comparative survey of WLAN location fingerprinting methods , 2009, 2009 6th Workshop on Positioning, Navigation and Communication.

[8]  Shensheng Zhang,et al.  An Accurate and Fast WLAN User Location Estimation Method Based on Received Signal Strength , 2007, International Conference on Computational Science.

[9]  Eyal de Lara,et al.  Calibree: Calibration-Free Localization Using Relative Distance Estimations , 2009, Pervasive.

[10]  Gunnar Karlsson,et al.  Techniques to reduce the IEEE 802.11b handoff time , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[11]  Daniel B. Faria.  Modeling Signal Attenuation in IEEE 802.11 Wireless LANs-Vol. 1 , 2022 .

[12]  Kostas E. Bekris,et al.  Robotics-Based Location Sensing Using Wireless Ethernet , 2005, Wirel. Networks.

[13]  Srinivasan Seshan,et al.  Access Point Localization Using Local Signal Strength Gradient , 2009, PAM.

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

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

[16]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[17]  H C Lukaski,et al.  Methods for the assessment of human body composition: traditional and new. , 1987, The American journal of clinical nutrition.

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

[19]  Moustafa Youssef,et al.  The Horus location determination system , 2008 .

[20]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[21]  Hyung Keun Lee,et al.  Modeling heterogeneous signal strength characteristics for flexible WLAN indoor localization , 2009, 2009 ICCAS-SICE.

[22]  Xin Pan,et al.  ARIEL: automatic wi-fi based room fingerprinting for indoor localization , 2012, UbiComp.

[23]  K. Kaemarungsi,et al.  Distribution of WLAN received signal strength indication for indoor location determination , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[24]  Fumie Costen,et al.  Average Signal Level Prediction in an Indoor WLAN Using Wall Imperfection Model , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[25]  Prashant Krishnamurthy,et al.  Properties of indoor received signal strength for WLAN location fingerprinting , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[26]  Takeshi Tsuchiya,et al.  Orientation-Aware Indoor Localization Path Loss Prediction Model for Wireless Sensor Networks , 2008, NBiS.