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http://dx.doi.org/10.3745/KTSDE.2013.2.9.595

Design of a Crowd-Sourced Fingerprint Mapping and Localization System  

Choi, Eun-Mi (경기대학교 컴퓨터과학과)
Kim, In-Cheol (경기대학교 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.9, 2013 , pp. 595-602 More about this Journal
Abstract
WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.
Keywords
Indoor Localization; Crowd-sourced Fingerprint Mapping; WiFi SLAM; Error Filtering; Gaussian Interpolation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 V. Honkavirta, et al., "A Comparative Survey of WLAN Location Fingerprinting Methods," Proceedings of the 6th Workshop on Positioning, Navigation and Communication (WPNC), 2009.
2 B. Ferris, D. Hahnel, and D. Fox, "Gaussian Processes for Signal Strength-Based Location Estimation," Proceedings of Robotics Science and Systems, 2006.
3 B. Philipp, "Redpin-Adaptive, zero-configuration indoor localization through user collaboration," presented at the First ACM Int. Workshop Mobile Entity Localization Tracking GPS-less Environ, San Francisco, CA, 2008.
4 J. Ledlie, et al., "Mole: a Scalable, User-Generated WiFi Positioning Engine," Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2011.
5 J. Park et al., "Growing an Organic Indoor Location System", Proceedings of the 8th International Conference on MobiSys-2010, San Francisco, CA, 271-284, 2010
6 B. Ferris, et al., "WiFi-SLAM Using Gaussian Process Latent Variable Models," Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2007.
7 J.Huang, et al., "Efficient, Generalized Indoor WiFi GraphSLAM", Proc. of ICRA-2010, 2010.
8 L. Bruno, Patrick Robertson, "WiSLAM: Improving FootSLAM with WiFi", Proc. of IPIN-2011, 2011.
9 E. Choi, H. Oh, I. Kim, "Simultaneous Localization and WiFi Fingerprint Mapping Based on Particle Filters", Journal of KIISE: Software and Applications, Vol.40, No.4, 2013.   과학기술학회마을
10 P. Bahl and V.N. Padmanabhan, "RADAR: An In-Building RF-Based User Location and Tracking System," Proceedings of IEEE Conference on Computer Communications (INFOCOM), 2000.
11 A. LaMarca, et al., "Place Lab: Device Positioning Using Radio Beacons in the Wild," Proceedings of the International Conference on Pervasive Computing, 2005.