Ngewi Fet
ABSTRACT
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).
- P. Bahl and V. Padmanabhan. Radar: an in-building rf-based user location and tracking system. In INFOCOM 2000, volume 2, pages 775--784 vol.2. IEEE, 2000.Google Scholar
Cross Ref
- K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. Proc. MobiCom 2010, page 173, 2010. Google ScholarDigital Library
- D. Faria. Modeling signal attenuation in ieee 802.11 wireless lans-vol. 1. Computer Science Department, Stanford University, 1, 2005.Google Scholar
- C. Feng, W. S. A. Au, S. Valaee, and Z. Tan. Orientation-aware indoor localization using affinity propagation and compressive sensing. CAMSAP 2009, pages 261--264, 2009.Google ScholarCross Ref
- U. Frese. Treemap: An o(log n) algorithm for indoor simultaneous localization and mapping. Autonomous Robots, 21(2):103--122, 2006. Google ScholarDigital Library
- Y. Gu, A. Lo, and I. Niemegeers. A survey of indoor positioning systems for wireless personal networks. Communications Surveys Tutorials, 11(1):13--32, 2009. Google ScholarDigital Library
- D. Han, D. G. Andersen, M. Kaminsky, K. Papagiannaki, and S. Seshan. Access point localization using local signal strength gradient. In Proc. PAM 2009, pages 99--108. Springer-Verlag, 2009. Google ScholarDigital Library
- V. Honkavirta, T. Perala, S. Ali-Loytty, and R. Piche. A comparative survey of wlan location fingerprinting methods. In WPNC 2009, pages 243--251, 2009.Google ScholarCross Ref
- Y. Ji, S. Biaz, S. Pandey, and P. Agrawal. Ariadne: a dynamic indoor signal map construction and localization system. In Proc. MobiSys 2006, pages 151--164. ACM, 2006. Google ScholarDigital Library
- Y. Jiang, X. Pan, K. Li, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. Ariel: automatic wi-fi based room fingerprinting for indoor localization. In Proc. UbiComp 2012, pages 441--450. ACM, 2012. Google ScholarDigital Library
- K. Kaemarungsi. Distribution of wlan received signal strength indication for indoor location determination. In ISWPC 2006, pages 1--6. IEEE, 2006.Google ScholarCross Ref
- K. Kaemarungsi and P. Krishnamurthy. Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM 2004, volume 2, pages 1012--1022. IEEE, 2004.Google ScholarCross Ref
- K. Kaemarungsi and P. Krishnamurthy. Properties of indoor received signal strength for WLAN location fingerprinting. MOBIQUITOUS 2004., pages 14--23, 2004.Google ScholarCross Ref
- T. King, S. Kopf, T. Haenselmann, C. Lubberger, and W. Effelsberg. Compass: A probabilistic indoor positioning system based on 802.11 and digital compasses. In Proc. WiNTECH 2006, pages 34--40. ACM, 2006. Google ScholarDigital Library
- A. M. Ladd, K. E. Bekris, A. Rudys, G. Marceau, L. E. Kavraki, and D. S. Wallach. Robotics-based location sensing using wireless ethernet. In Proc. MobiCom 2002, pages 227--238. ACM, 2002. Google ScholarDigital Library
- H. Lee, H. Kim, and W. Choi. Modeling heterogeneous signal strength characteristics for flexible wlan indoor localization. ICCAS-SICE 2009, pages 1765--1768, 2009.Google Scholar
- M. Lihan, T. Tsuchiya, and K. Koyanagi. Orientation-aware indoor localization path loss prediction model for wireless sensor networks. In Proc. NBiS 2008, pages 169--178. Springer-Verlag, 2008. Google ScholarDigital Library
- H. Liu, H. Darabi, P. Banerjee, and J. Liu. Survey of wireless indoor positioning techniques and systems. SMC 2007, Part C: Applications and Reviews, 37(6):1067--1080, 2007. Google ScholarDigital Library
- H. Lukaski. Methods for the assessment of human body composition: traditional and new. The American journal of clinical nutrition, 1987.Google Scholar
- K. Nasr, F. Costen, and S. Barton. Average signal level prediction in an indoor wlan using wall imperfection model. In PIMRC 2005, volume 1, pages 674--678. IEEE, 2005.Google ScholarCross Ref
- S. Seidel and T. Rappaport. 914 mhz path loss prediction models for indoor wireless communications in multifloored buildings. APS, 1992, 40(2):207--217, 1992.Google Scholar
- S. Sen, R. R. Choudhury, and S. Nelakuditi. Spinloc: Spin once to know your location. ACM HotMobile, 2012. Google ScholarDigital Library
- A. Varshavsky, D. Pankratov, J. Krumm, and E. D. Lara. Calibree: Calibration-free localization using relative distance estimations. Pervasive Computing, pages 146--161, 2008. Google ScholarDigital Library
- H. Velayos and G. Karlsson. Techniques to reduce the ieee 802.11b handoff time. In ICC 2004, volume 7, pages 3844--3848, 2004.Google ScholarCross Ref
- M. Youssef and A. Agrawala. The horus location determination system. Wireless Networks, 14(3):357--374, 2008. Google ScholarDigital Library
- M. Zhang and S. Zhang. An accurate and fast wlan user location estimation method based on received signal strength. In Proc. ICCS 2007, pages 58--65. Springer-Verlag, 2007. Google ScholarDigital Library
Index Terms
- A model for WLAN signal attenuation of the human body
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