Abstract
Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 28.2-56% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bahl, P., Padmanabhan, V.N.: RADAR: An in-building RF-based user location and tracking system. IEEE INFOCOM, 775–784 (March 2000)
ITRI, http://www.itri.org.tw
Bahl, P., Balachandran, A., Padmanabhan, V.N.: Enhancements to the RADAR user location and tracking system. Technical report of Microsoft Research (February 2000)
Ekahau, http://www.ekahau.com
Seshadri, V., Zaruba, G.V., Huber, M.: A Bayesian Sampling Approach to In-door Local-ization of Wireless Devices Using Received Signal Strength Indication. In: IEEE Conference on Pervasive Computing and Communications (PerCom) (March 2005)
Hightower, J., Borriello, G.: Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study. In: International Conference on Ubiquitous Computing (September 2005)
Schulz, D., Fox, D., Hightower, J.: People Tracking with Anonymous and ID-Sensors using Rao-Blackwellised Particle Filters. In: International Joint Conference on Artificial Intelligence, August 2003, pp. 921–926 (2003)
Fox, D., Hightower, J., Liao, L., Schulz, D., Borriello, G.: Bayesian Filtering for Location Estimation. IEEE Pervasive Computing 2(3), 24–33 (2003)
Graumann, D., Hightower, J., Lara, W., Borriello, G.: Real-world Implementation of the Location Stack: The Universal Location Framework. In: IEEE Workshop on Mobile Computing Systems & Applications (WMCSA) (October 2003)
Gwon, Y., Jain, R., Kawahara, T.: Robust Indoor Location Estimation of Stationary and Mobile Users. IEEE INFOCOM (March 2004)
Yie, J., Yang, Q., Ni, L.: Adaptive Temporal Radio Maps for Indoor Location Estimation. In: International Conference on Pervasive Computing (May 2005)
Chen, Y.C., Chiang, J.R., Chu, H.H., Huang, P., Tsui, A.W.: Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics. In: International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (October 2005)
Minami, M., Fukuju, Y., Hirasawa, K., Yokoyama, S., Mizumachi, M., Morikawa, H., Aoyama, T.: DOLPHIN: A practical approach for implementing a fully distributed indoor ultrasonic positioning system. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 347–365. Springer, Heidelberg (2004)
Hu, L., Evans, D.: Localization for Mobile Sensor Networks. In: International Conference on Mobile Computing and Networking (MobiCom) (2004)
Eugene Ng, T.S., Zhang, H.: Predicting Internet Network Distance with Coordinates-Based Approaches. IEEE INFOCOM, 170–179 (2002)
Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: A Decentralized Network Coordinate System. ACM SIGCOMM (2004)
He, T., Huang, C., Blum, B.M., Stankovic, J.A., Abdelzaher, T.: Range-Free Localization Schemes for Large Scale Sensor Networks. In: ACM International Conference on Mobile Computing and Networking (MOBICOM), pp. 81–95 (2003)
Priyantha, N.B., Balakrishnan, H., Demaine, E., Teller, S.: Anchor-Free Distributed Localization in Sensor Networks. In: ACM International Conference on Embedded Networked Sensor Systems (SenSys), pp. 340–341 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chan, Lw., Chiang, Jr., Chen, Yc., Ke, Cn., Hsu, J., Chu, Hh. (2006). Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds) Pervasive Computing. Pervasive 2006. Lecture Notes in Computer Science, vol 3968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11748625_4
Download citation
DOI: https://doi.org/10.1007/11748625_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33894-9
Online ISBN: 978-3-540-33895-6
eBook Packages: Computer ScienceComputer Science (R0)