Two manifold learning techniques for sensor localization

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2007, Pages 2114-2118

  Hui, Liu ^a,b   Xun, Luo ^b   Yingwei, Yao ^a,b  

^a IEEE

^b University of Illinois   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

LOCAL TANGENT SPACE ALIGNMENT (LTSA); PAIRWISE DISTANCE; RANGE MEASUREMENT; SENSOR LOCALIZATION;

WIRELESS SENSOR NETWORKS;

EID: 40949120979     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2007.4413668     Document Type: Conference Paper

References (17)

1. I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless sensor networks: A survey," Computer Networks, vol.38, pp.393-422, March 2002.
2. Patwari, N., Ash, J.N., Kyperountas, S., Hero, A.O., III, Moses, R.L. and Correal, N.S. "Locating the nodes: cooperative localization in wireless sensor networks", IEEE Signal Processing Magazine, Volume 221, Issue 4, July 2005.
3. He, T., Huang, C., Blum, B.M., Stankovic, J.A., Abdelzaher., T., "Range-free Localization Schemes for Large Scale Sensor Networks ". Technical Report CS-2003-06, University of Virginia, Computer Science Department (2003).
4. Bulusu, N., Heidemann, J., Estrin, D., "Gps-less Low cost Outdoor Localization for Very Small Devices". IEEE Personal Communications Magazine. 1 (2000) 28-34.
5. Niculescu, D., Nath, B., "DV Based Positioning in Ad hoc Networks". Journal of Telecommunication Systems, (2003).
6. Cox, T. and Cox, M. 1994. Multidimensional Scaling. Chapman & Hall, London.
7. Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz. "Localization from Mere Connectivity", In Proc. of Intl. Symp. on Mobile Ad Hoc Networking and Computing (MobiHoc), 2003.
8. Y. Shang and W. Ruml, "Improved MDS-based localization," in IEEE Proc. Infocom'04, pp. 2640-2651, Mar. 2004.
9. X. Ji and H. Zha, "Sensor Positioning in Wireless Ad-Hoc Sensor Networks Using Multidimensional Scaling", Proc. of IEEE Infocom, Hong Kong, China, pp. 2652-2661, March 2004.
10. J. A. Costa, N. Patwari, A. O. Hero, "Distributed Weighted Multidimensional Scaling for Node Localization in Sensor Networks", IEEE/ACM Transactions on Sensor Networks, Feb. 2006, vol. 2, no. 1, pp. 39-64.
11. Joshua B. Tenenbaum, Vin de Silva, and John C. Langford, "A global geometric framework for nonlinear dimensionality reduction". Science. Vol. 290, pp. 2319-2323, Dec 2000.
12. Roweis, S. T. and Saul, L. K., "Nonlineaar dimensionality reduction by locally linear embedding". Science, 290:2323-2326, Dec. 2000.
13. David L. Donoho and Carrie Grimes, "Hessian eigenmaps: new locally linear embedding techniques fro high-dimensional data", Tech. Rep. TR2003-08, Dept. of Statistics, Stanford University, March 2003.
14. Patwari, N. and Hero III, A. O., "Manifold learning algorithms for localization in wireless sensor networks". In Proceedings of IEEE International Conference on Acoustic Speech and Signal Processing, 2004.
15. M. Belkin, P. Niyogi, "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation", Neural Computation, 15 (6):13731396, Jun 2003.
16. N. Patwari and A. O. Hero, "Adaptive Neighborhoods for Manifold Learning based Sensor Localization", in Proceedings of the 2005 Signal Processing and Wireless Communications Conference (SPA WC), New York City, 58 June 2005.
17. Zhang, Z. and Zha, H., "Principal manifolds and nonlinear dimension reduction via local tangent space alignment". SIAM Scientific Computing, 26(1): 313-338.