ABSTRACT
Accurate location of people in indoor environments is a key aspect of many applications such as resource management or security. In this paper, we explore the use of short-range radio technologies to track people indoors. The network consists of two kind of radio nodes: static nodes (anchors) and mobile nodes (people). From a set of sparse connectivity matrices (people vs. people and people vs. anchors) at each time instant and people's dynamics, we infer people's trajectories. To combine connectivity and dynamic information, we propose an extension of Multidimensional Scaling(MDS), Dynamic Weighted MDS (DWMDS), that finds an embedding of people's trajectories (x and y coordinates of people through time). DWMDS has proven to be more accurate and effective, especially for low connectivity degree networks (i.e. sparse networks), compared to existing location algorithms. Extensive simulations show the effectiveness and robustness of the proposed algorithm.
- G. Anastasi, R. Bandelloni, M. Conti, F. Delmastro, E. Gregori, and G. Mainetto. Experimenting an indoor bluetooth-based positioning service. In ICDCSW '03: Proceedings of the 23rd International Conference on Distributed Computing Systems, page 480, Washington, DC, USA, 2003. IEEE Computer Society. Google ScholarDigital Library
- D. Ashbrook and T. Starner. Using gps to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput., 7 (5):275--286, 2003. Google ScholarDigital Library
- P. Bahl and V. N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In INFOCOM, pages 775--784, 2000.Google ScholarCross Ref
- T. F. Cox, M. A. A. Cox, and T. F. Cox. Multidimensional Scaling, Second Edition. Chapman & Hall/CRC, September 2000.Google Scholar
- E. Elnahrawy, X. Li, and R. P. Martin. The limits of localization using signal strength: a comparative study. Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004, pages 406--414, 2004.Google ScholarCross Ref
- R. Fletcher. Practical methods of optimization; (2nd ed.). Wiley-Interscience, New York, NY, USA, 1987. Google ScholarDigital Library
- X. Ji. Sensor positioning in wireless ad-hoc sensor networks with multidimensional scaling, In Infocom 2004.Google Scholar
- K. Lorincz and M. Welsh. A robust, decentralized approach to rf-based location tracking. Technical Report TR-04-04, Harvard University, 2004.Google Scholar
- N. Marmasse and C. Schmandt. Location-aware information delivery with commotion. In HUC, pages 157--171, 2000. Google ScholarDigital Library
- D. Niculescu and B. Nath. Ad hoc positioning system (aps) using aoa, in Proceedings of INFOCOM 2003, San Francisco, CA.Google ScholarCross Ref
- Y. Ohta, M. Sugano, and M. Murata. Autonomous localization method in wireless sensor networks. In PERCOMW '05: Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops, pages 379--384, Washington, DC, USA, 2005. IEEE Computer Society. Google ScholarDigital Library
- N. Patwari, A. III, M. Perkins, N. Correal, and R. O'Dea. Relative location estimation in wireless sensor networks. In IEEE Transactions on Signal Processing, pages 2137--2148, Piscataway, NJ, USA, 2003. IEEE Signal Processing Society.Google ScholarDigital Library
- N. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In MobiCom '00: Proceedings of the 6th Annual ACM International Conference on Mobile Computing and Networking, August 2000. Google ScholarDigital Library
- T. S. Rappaport. Wireless Communications: Principles and Practice. IEEE Press, Piscataway, NJ, USA, 1996. Google ScholarDigital Library
- A. Savvides, C.-C. Han, and M. B. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In Mobile Computing and Networking, pages 166--179, 2001. Google ScholarDigital Library
- Y. Shang and W. Ruml. Improved mds-based localization, In Infocom 2004.Google Scholar
- Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. Localization from mere connectivity, MobiHoc'03, Annapolis, Maryland. June 2003. Google ScholarDigital Library
- V. Vivekanandan and V. W. Wong. Ordinal mds-based localization for wireless sensor networks. In VTC-2006: IEEE 64th Vehicular Technology Conference, pages 1--5, 2006.Google Scholar
- K. Whitehouse, C. Karlof, A. Woo, F. Jiang, and D. Culler. The effects of ranging noise on multihop localization: an empirical study. In IPSN '05: Proceedings of the 4th international symposium on Information processing in sensor networks, page 10, Piscataway, NJ, USA, 2005. IEEE Press. Google ScholarDigital Library
Index Terms
- Indoor people tracking based on dynamic weighted multidimensional scaling
Recommendations
Indoor Tracking and Navigation Using Received Signal Strength and Compressive Sensing on a Mobile Device
An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to ...
A Survey on Device-free Indoor Localization and Tracking in the Multi-resident Environment
Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared ...
Indoor human tracking mechanism using integrated onboard smartphones Wi-Fi device and inertial sensors
In indoor/outdoor environments, special cares need to be given to locate smartphones which are used by most of the people. Locating or tracking is valuable for those people who are in dangerous falling-situations or they are used for shopping and ...
Comments