Abstract
Aiming at the problem that the multi-hop range-free wireless localization algorithm is sensitive to the influence of the anisotropic sensor network factors, we propose a new approach for localization in wireless sensor networks based on regularization algorithm. We first construct the mapping model using the hop-counts and the distance between anchors, and regularization algorithm is used to describe the optimal linear transformations between the hop-counts and the distance. We then use the hop-counts of no-anchors to anchors and this mapping model to the locations of the non-anchors. We evaluate our algorithm under irregular distribution of nodes and the uneven deployment of nodes, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate our proposed algorithm can effectively avoid the network anisotropy.
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References
Akyildiz, I.F., Vuran, M.C.: Wireless Sensor Networks. Wiley, New York (2010)
Yang, Z., Wu, C., Liu, Y.: Location-Based Computing: Localization and Localizability of Wireless Networks, pp. 48–57. Tsinghua University Press, Beijing (2014)
Liu, Y., Yang, Z.: Location, Localization, and Localizability Location-Awareness Technology for Wireless Networks. Springer, New York (2011)
Xiao, F., Sha, C., Chen, L., et al.: Noise-tolerant localization from incomplete range measurements for wireless sensor networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), Hong Kong, pp. 2794–2802 (2015)
Shekhar, S., Feiner, S.K., Aref, W.G.: Spatial computing. Commun. ACM 59(1), 72–81 (2016)
Radiation U S E O O a A: The Inside story: a guide to indoor air quality. Technical report EPA 402-K-93-007.U.S. Environmental Protection Agency, Washington, D.C. http://goo.gl/RkZUBU
Tao, H., Gérard, L., Richard, K.: Controlled GPS signal simulation for the indoors. J. Navig. 60(2), 265–280 (2007)
Yan, X., Song, A., Yang, Z., et al.: An improved multihop-based localization algorithm for wireless sensor network using learning approach. Comput. Electr. Eng. 48, 247–257 (2015)
Yan, X., Yang, Z., Song, A., et al.: A novel multihop range-free localization based on kernel learning approach for the internet of things. Wireless Pers. Commun. 87(1), 269–292 (2016)
Yan, X., Yang, Z., Liu, Y., et al.: Incremental localization algorithm based on regularized iteratively reweighted least square. Found. Comput. Decis. Sci. 41(3), 183–196 (2016)
Niculescu, D., Nath, B.: DV based positioning in ad hoc networks. Telecommun. Syst. 22(1–4), 267–280 (2003)
Nagpal, R., Shrobe, H., Bachrach, J.: Organizing a global coordinate system from local information on an ad hoc sensor network. In: Zhao, F., Guibas, L. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 333–348. Springer, Heidelberg (2003). doi:10.1007/3-540-36978-3_22
Lim, H., Hou, J.C.: Distributed localization for anisotropic sensor networks. ACM Trans. Sensor Netw. (TOSN) 5(2), 1–26 (2009)
Lee, J., Chung, W., Kim, E.: A new kernelized approach to wireless sensor network localization. Inf. Sci. 243(2013), 20–38 (2013)
Gu, B., Sheng, V.S.: A robust regularization path algorithm for ν-support vector classification. IEEE Trans. Neural Netw. Learn. Syst. 28(5), 1241–1248 (2017)
Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K.: A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. 11(11), 2594–2608 (2016)
Fu, Z., Ren, K., Shu, J., Sun, X., Huang, F.: Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans. Parallel Distrib. Syst. 27(9), 2546–2559 (2016)
Bickel, P.J., Li, B.: Regularization in statistics. Test 15(2), 271–344 (2006)
Hansen, P.C.: Regularization tools: a matlab package for analysis and solution of discrete ill-posed problems. Numer. Algorithms 6(1), 1–35 (1994)
Zhang, L., Hua, C., Tang, Y., et al.: Ill-posed echo state network based on L-curve method for prediction of blast furnace gas flow. Neural Process. Lett. 43(1), 97–113 (2016)
Acknowledgements
The paper is sponsored by the NSF of China (61403080, 61572261, 61373139), National Natural Science Foundation of Jiangsu Province (BK20140641, BK20150868), China Postdoctoral Science Foundation (2014M551635, 2016M601861), Postdoctoral Fund of Jiangsu Province (1302085B), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (15KJB520009) and the Open Project Program of Jiangsu Key Laboratory of Remote Measurement and Control (YCCK201603).
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Yan, X., Yang, Z., Liu, Y., Su, Z., Li, H. (2017). An Improved Localization Algorithm for Anisotropic Sensor Networks. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_43
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DOI: https://doi.org/10.1007/978-3-319-68505-2_43
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