Wireless Sensor Network Localization Based On A Mobile Beacon And Tsvm
By: Jiang Xu, Huanyan Qian, Huan Dai and Jianxin Zhu
References
- 1. Girod, L., D. Estrin. Robust Range Estimation Using Acoustic and Multimodal Sensing. - In: Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2001, 1312-1320.
- 2. Niculescu, D., B. Nath. Ad Hoc Positioning System (APS) Using AOA. - In: Proc. of 22nd Annual Joint Conference of the IEEE Computer and Communications Societies, Rutgers University, Piscataway, 30 March-3 April 2003, 1734-1743.
- 3. Krumm, J., G. Cermak, E. Horvitz. Right SPOT, a Novel Sense of Location for a Smart Personal Object. - In: Proc. of Ubicomp, 2003, 36-43.
- 4. Niculescu, D., B. Nath. DV Based Positioning in Ad Hoc Networks. - J. Telecommun. Syst., Vol. 22, 2003, 267-280.
- 5. Shang, Y., W. Ruml, Y. Zhang, M. P. J. Fromherz. Localization from Mere Connectivity. - In: Proc. of 4th ACM Int. Symp. on Mobile Ad Hoc Networking & Computing, 2003, 201-212.
- 6. Lee, H., S. Lee, Y. Kim, H. Chong. Grouping Multi-Duolateration Localization Using Partial Space Information for Indoor Wireless Sensor Networks. - IEEE Trans. on Consumer Electronics, Vol. 55, 2009, No 4, 1950-1958.
- 7. Brunato, M., R. Battiti. Statistical Learning Theory for Location Fingerprinting in Wireless LANs. - Comput. Networks, Vol. 47, 2005, 825-845.
- 8. Nguyen, X., M. I. Jordan, B. Sinopoli. A Kernel-Based Learning Approach to Ad Hoc Sensor Network Localization. - ACM Trans. Sensor Networks, Vol. 1, 2005, 134-152.
- 9. Pan, J. J., J. T. Kwok, Y. Chen. Multidimensional Vector Regression for Accurate and Low- Cost Location Estimation in Pervasive Computing. - IEEE Trans. on Knowledge and Data Engineering, Vol. 18, 2006, 1181-1193.
- 10. Tran, D. A., T. Nguyen. Localization in Wireless Sensor Networks Based on Support Vector Machines. - IEEE Trans. Parallel Distrib. Syst., Vol. 19, 2008, 981-994.
- 11. Joachims, T. Transductive Inference for Text Classification Using Support Vector Machines. - In: Proc. of 16th Int. Conf. on Machine Learning (ICML), San Francisco, 1999, 200-209.
- 12. Chen, Y., G. Wang, S. Dong. A Progressive Transductive Inference Algorithm Based on Support Vector Machine. - Journal of Software, Vol. 14, 2003, No 3, 451-460.
- 13. Cortes, C., V. Vapnik. Support-Vector Networks. - Maching Learning, Vol. 20, 1995, 273-297.
- 14. Scholkopf, B., A. J. Smola. Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MIT Press, 2002.
- 15. Cauwenberghs, G., T. Poggio. Incremental and Decremental Support Vector Machine Learning, Advances Neural Information Processing Systems. Cambridge, MIT Press, 2001, 409-415.
DOI: https://doi.org/10.2478/cait-2014-0047 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702 (formerly 1314-4081)
Language: English
Page range: 98 - 107
Published on: Dec 30, 2014
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Keywords:
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© 2014 Jiang Xu, Huanyan Qian, Huan Dai, Jianxin Zhu, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.