The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2012 vol.11)
pp: 1970-1982
Keyong Li , Boston University, Boston
Dong Guo , Boston University, Boston
Yingwei Lin , Boston University, Boston
Ioannis Ch. Paschalidis , Boston University, Boston
We present a novel probabilistic framework for reliable indoor positioning of mobile sensor network devices. Compared to existing approaches, ours adopts complex computations in exchange for high localization accuracy while needing low hardware investment and moderate set-up cost. To that end, we use full distributional information on signal measurements at a set of discrete locations, termed landmarks. Positioning of a mobile device is done relative to the resulting landmark graph and the device can be found near a landmark or in the area between two landmarks. Key elements of our approach include profiling the signal measurement distributions over the coverage area using a special interpolation technique; a two-tier statistical positioning scheme that improves efficiency by adding movement detection; and joint clusterhead placement optimization for both localization and movement detection. The proposed system is practical and has been implemented using standard wireless sensor network hardware. Experimentally, our system achieved an accuracy equivalent to less than 5 meters with 95 percent success probability and less than 3 meters with an 87 percent success probability. This performance is superior to well-known contemporary systems that use similar low-cost hardware.
Accuracy, Interpolation, Wireless sensor networks, Probabilistic logic, Position measurement, optimal deployment, Wireless sensor networks, localization, probabilistic profiling, hypothesis testing
Keyong Li, Dong Guo, Yingwei Lin, Ioannis Ch. Paschalidis, "Position and Movement Detection of Wireless Sensor Network Devices Relative to a Landmark Graph", IEEE Transactions on Mobile Computing, vol.11, no. 12, pp. 1970-1982, Dec. 2012, doi:10.1109/TMC.2011.214
[1] J. Caffery and G. Stuber, “Subscriber Location in CDMA Cellular Networks,” IEEE Trans. Vehicular Technology, vol. 47, no. 2, pp. 406-416, May 1998.
[2] A. Weiss, “On the Accuracy of a Cellular Location System Based on RSS Measurements,” IEEE Trans. Vehicular Technology, vol. 52, no. 6, pp. 1508-1518, Nov. 2003.
[3] R. Want, A. Hopper, V. Falcao, and J. Gibbons, “The Active Badge Location System,” ACM Trans. Information Systems, vol. 10, pp. 91-102, Jan. 1992.
[4] N.B. Priyantha, A. Chakraborty, and H. Balakrishnan, “The Cricket Location-Support System,” Proc. Sixth Ann. Int'l Mobile Computing and Networking, pp. 32-43, , 2000.
[5] I. Guvenc, C.-C. Chong, and F. Watanabe, “NLOS Identification and Mitigation for UWB Localization Systems,” Proc. Wireless Comm. and Networking Conf. (WCNC '07), pp. 1571-1576, Mar. 2007.
[6] N. Patwari and S. Kasera, “Robust Location Distinction Using Temporal Link Signatures,” Proc. ACM MobiCom, pp. 111-122, 2007.
[7] P. Bahl and V. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proc. IEEE INFOCOM, Mar. 2000.
[8] K. Lorincz and M. Welsh, “MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking,” Personal and Ubiquitous Computing, vol. 16, pp. 1617-4909, 2006.
[9] K. Kaemarungsi and P. Krishnamurthy, “Modeling of Indoor Positioning Systems Based on Location Fingerprinting,” Proc. IEEE INFOCOM, 2004.
[10] J. Hightower, R. Want, and G. Borriello, “SpotON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength,” Technical Report UW CSE 00-02-02, Dept. of Computer Science and Eng., Univ. of Washington, Feb. 2000.
[11] P. Castro, P. Chiu, T. Kremenek, and R. Muntz, “A Probabilistic Location Service for Wireless Network Environments,” Proc. Third Int'l Conf. Ubiquitous Computing (Ubicomp), Sept. 2001.
[12] N. Patwari, A.O. Hero, M. Perkins, N.S. Correal, and R.J. O'Dea, “Relative Location Estimation in Wireless Sensor Networks,” IEEE Trans. Signal Processing, vol. 51, no. 8, pp. 2137-2148, Aug. 2003.
[13] K. Yedavalli, B. Krishnamachari, S. Ravula, and B. Srinivasan, “Ecolocation: A Sequence Based Technique for RF-Only Localization in Wireless Sensor Networks,” Proc. Fourth Int'l Conf. Information Processing in Sensor Networks, Apr. 2005.
[14] I.C. Paschalidis and D. Guo, “Robust and Distributed Localization in Sensor Networks,” Proc. IEEE 46th Conf. Decision and Control, pp. 933-938, Dec. 2007.
[15] I.C. Paschalidis and D. Guo, “Robust and Distributed Stochastic Localization in Sensor Networks: Theory and Experimental Results,” ACM Trans. Sensor Networks, vol. 5, no. 4, pp. 34:1-34:22, 2009.
[16] M.A. Youssef, “Collection About Location Determination Papers Available Online,” location_papers.htm , 2008.
[17] C. Chang and A. Sahai, “Cramer-Rao-Type Bounds for Localization,” J. Applied Signal Processing, vol. 2006, p. 113, 2006.
[18] S. Gezici, “A Survey on Wireless Position Estimation,” J. Wireless Personal Comm., vol. 44, no. 3, pp. 263-282, 2008.
[19] F.H. Bursal, “On Interpolating between Probability Distributions,” Applied Math. and Computation, vol. 77, pp. 213-244, 1996.
[20] S. Ray, W. Lai, and I.C. Paschalidis, “Statistical Location Detection with Sensor Networks,” IEEE Trans. Information Theory, vol. 52, no. 6, pp. 2670-2683, June 2006.
[21] T. Cover and J.A. Thomas, Elements of Information Theory. John Wiley and Sons, 1991.
[22] W. Hoeffding, “Asymptotically Optimal Tests for Multinomial Distributions,” Annals of Math. Statistics, vol. 36, pp. 369-401, 1965.
[23] A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications, second ed. Springer-Verlag, 1998.
[24] M. Daskin, Network and Discrete Location. Wiley, 1995.
[25] F. Özsoy and M. Pınar, “An Exact Algorithm for the Capacitated Vertex P-Center Problem,” Computers and Operations Research, vol. 33, no. 5, pp. 1420-1436, 2006.
567 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool