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Wireless Personal Communications

Erschienen in:

13.08.2016

Wireless Sensor Networks Localization Using Progressive Isomap

verfasst von: Jyoti Kashniyal, Shekhar Verma, K. P. Singh

Erschienen in: Wireless Personal Communications | Ausgabe 3/2017

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Abstract

This paper proposes a progressive Isomap algorithm for node localization. The algorithm is an extension of centralized Isomap and is capable of effectively localizing new nodes progressively, without neglecting preceding computational results. In the initial startup phase, sensor nodes within two hop range of three non-collinear anchor nodes are localized using range (\(\epsilon\))-Isomap. Afterwards, in the progressive phase, the remaining nodes are localized in a sequential manner, using recently localized nodes. The localization process initiated at different nodes in the network give rise to the overlapping substructures of localized nodes. The overlapped substructures are stitched to form a single coordinate system. Finally, Helmert Transformation is used to obtain the global coordinates of the nodes. The effects of varying various parameters on the accuracy and scalability of the proposed algorithm are studied through the simulation. Results indicate that the proposed algorithm has good positioning accuracy and noise-robustness.

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Mao, G., Fidan, B., & Anderson, B. D. (2007). Localization. In N. P. Mahalik (Ed.), Sensor networks and configuration: Fundamentals, standards, platforms and applications (pp. 281–315). Berlin: Springer. doi:10.1007/3-540-37366-7.

Savvides, A., Han, C. C., & Strivastava, M. B. (2001). Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th ACM international conference on mobile computing and networking (MOBICOM’01) (pp. 166–179). doi:10.1145/381677.381693.

Chan, H., Luk, M., & Perrig, A. (2005). Using clustering information for sensor network localization. In Proceedings of the 1st IEEE international conference on distributed computing in sensor systems (DCOSS’05) (pp. 109–125). doi:10.1007/11502593_11.

Rashid, H., & Turuk, A. K. (2013). Localization of wireless sensor networks using a single anchor node. Wireless Personal Communications, 72(2), 975–986. doi:10.1007/s11277-013-1050-y.CrossRef

Zhang, Y., Chen, Y., & Liu, Y. (2012). Towards unique and anchor-free localization for wireless sensor networks. Wireless Personal Communications, 63(1), 261–278. doi:10.1007/s11277-011-0337-0.CrossRef

Shen, S., Yang, B., Qian, K., & Jiang, X. (2015). An efficient localization algorithm in wireless sensor networks. In Proceedings of the 3rd IEEE international symposium on computing and networking (CANDAR) (pp. 291–294). doi:10.1109/CANDAR.2015.99.

Priyantha, N. B., Balakrishnan, H., Demaine, E., & Teller, S. (2003). Anchor-free distributed localization in sensor networks. In Proceedings of the 1st ACM international conference on embedded networked sensor systems (pp. 340–341). doi:10.1145/958491.958550.

Wang, D., Yang, L., & Cheng, X. (2016). A low-complexity cooperative algorithm for robust localization in wireless sensor networks. In International conference on computing, networking and communications (ICNC) (pp. 1–5). doi:10.1109/ICCNC.2016.7440715.

Schlupkothen, S., Dartmann, G., & Ascheid, G. (2015). A novel low-complexity numerical localization method for dynamic wireless sensor networks. IEEE Transactions on Signal Processing, 63(15), 4102–4114. doi:10.1109/TSP.2015.2422685.MathSciNetCrossRef

10.

Rabaey, C. S. J., & Langendoen, K. (2002). Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In Proceedings of the general track of the annual conference on USENIX annual technical conference (ATEC ‘02) (pp. 317–327).

11.

Zhang, Y., Wu, W., & Chen, Y. (2005). A range-based localization algorithm for wireless sensor networks. Journal of Communications and Networks, 7(4), 429–437. doi:10.1109/JCN.2005.6387985.CrossRef

12.

Tomic, S., & Mezei, I. (2016). Improvements of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 61(1), 93–106. doi:10.1007/s11235-015-0014-9.CrossRef

13.

He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on mobile computing and networking (MobiCom’03) (pp. 81–95). doi:10.1145/938985.938995.

14.

Liang, C., & Wen, F. (2016). Received signal strength based robust cooperative localization with dynamic path loss model. IEEE Sensors Journal, 16(5), 1265–1270. doi:10.1109/JSEN.2015.2500270.CrossRef

15.

Rencheng, J., Hongbin, W., Bo, P., & Ning, G. (2008). Research on RSSI-based localization in wireless sensor networks. In Proceedings of the 4th IEEE international conference on wireless communications, networking and mobile computing (WiCOM’08) (pp. 1–4). doi:10.1109/WiCom.2008.963.

16.

Kalpana, R., & Baskaran, M. (2016). TAR: TOA–AOA based random transmission directed localization. Wireless Personal Communications. doi:10.1007/s11277-016-3237-5.

17.

Niculescu, D., & Nath, B. (2003). Ad hoc positioning system (APS) using AOA. In Proceedings of the 22nd annual joint conference of the ieee computer and communications societies (Vol. 3, pp. 1734–1743).

18.

Xu, B., Sun, G., Yu, R., & Yang, Z. (2013). High-accuracy TDOA-based localization without time synchronization. IEEE Transactions on Parallel and Distributed Systems, 24(8), 1567–1576. doi:10.1109/TPDS.2012.248.CrossRef

19.

Shang, Y., Ruml, W., Zhang, Y., & Fromherz, M. P. (2003). Localization from mere connectivity. In Proceedings of the 4th ACM international symposium on mobile ad hoc networking & computing (MobiHoc’03) (pp. 201–212). doi:10.1145/778415.778439.

20.

Franceschini, F., Galetto, M., Maisano, D., & Mastrogiacomo, L. (2009). A review of localization algorithms for distributed wireless sensor networks in manufacturing. International Journal of Computer Integrated Manufacturing, 22(7), 698–716. doi:10.1080/09511920601182217.CrossRef

21.

Gezici, S. (2008). A survey on wireless position estimation. Wireless Personal Communications, 44(3), 263–282. doi:10.1007/s11277-007-9375-z.CrossRef

22.

Ji, X., & Zha, H. (2004). Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling. In Proceedings of the 23rd annual joint conference of the IEEE computer and communications societies (INFOCOM’04) (Vol. 4, pp. 2652–2661). doi:10.1109/infcom.2004.1354684.

23.

Patwari, N., & Hero III, A. O. (2004). Manifold learning algorithms for localization in wireless sensor networks. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP’04) (Vol. 3, pp. iii-857). doi:10.1109/ICASSP.2004.1326680.

24.

Shi, L. K., He, P. L., Liu, B., Fu, K., & Wu, Q. (2005). A robust generalization of Isomap for new data. In Proceedings of IEEE international conference on machine learning and cybernetics (ICMLC’05) (Vol. 3, pp. 1707–1712). doi:10.1109/ICMLC.2005.1527219.

25.

Zhang, Z., Wang, J., & Zha, H. (2012). Adaptive manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(2), 253–265. doi:10.1109/TPAMI.2011.115.CrossRef

26.

Saul, L. K., & Roweis, S. T. (2003). Think globally, fit locally: Unsupervised learning of low dimensional manifolds. The Journal of Machine Learning Research, 4, 119–155. doi:10.1162/153244304322972667.MathSciNetMATH

27.

Jolliffe, I. T. (2002). Principal component analysis. New York: Springer. doi:10.1007/b98835.MATH

28.

Cox, T. F., & Cox, M. A. (Eds.). (2000). Multidimensional scaling (2nd ed.). London/Boca Raton: Chapman & Hall/CRC. doi:10.1201/9781420036121.MATH

29.

Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. doi:10.1126/science.290.5500.2319.CrossRef

30.

Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326. doi:10.1126/science.290.5500.2323.CrossRef

31.

Wang, C., Chen, J., Sun, Y., & Shen, X. S. (2009). Wireless sensor networks localization with Isomap. In Proceedings of the IEEE international conference on communications (ICC’09) (pp. 1–5). doi:10.1109/icc.2009.5199576.

32.

Bengio, Y., Paiement, J. F., Vincent, P., Delalleau, O., Le Roux, N., & Ouimet, M. (2004). Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. Advances in Neural Information Processing Systems, 16, 177–184.

33.

Law, M. H., & Jain, A. K. (2006). Incremental nonlinear dimensionality reduction by manifold learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(3), 377–391. doi:10.1109/TPAMI.2006.56.CrossRef

34.

Zhao, D., & Yang, L. (2009). Incremental isometric embedding of high-dimensional data using connected neighborhood graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 86–98. doi:10.1109/TPAMI.2008.34.CrossRef

35.

Zhang, Y., Wang, Y., Li, C., & Wang, K. (2008). Kernel based incremental learning Isomap algorithm. In IEEE international conference on information and automation (ICIA’08) (pp. 184–189). doi:10.1109/icinfa.2008.4607993.

36.

Mao, G., Fidan, B., & Anderson, B. D. (2007). Wireless sensor network localization techniques. Computer Networks, 51(10), 2529–2553. doi:10.1016/j.comnet.2006.11.018.CrossRefMATH

37.

Shang, Y., Shi, H., & Ahmed, A. (2004). Performance study of localization methods for ad-hoc sensor networks. In Proceedings of the 1st IEEE international conference on mobile ad hoc and sensor systems (MASS’04) (pp. 184–193). doi:10.1109/MAHSS.2004.1392106.

38.

Li, B., He, Y., Guo, F., & Zuo, L. (2013). A novel localization algorithm based on Isomap and partial least squares for wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 62(2), 304–314. doi:10.1109/TIM.2012.2216476.CrossRef

39.

Meertens, L., & Fitzpatrick, S. (2004). The distributed construction of a global coordinate system in a network of static computational nodes from inter-node distances. Kestrel Institute Technical Report KES.U.04.04. Retrieved from https://www.kestrel.edu/home/people/fitzpatrick/pub/KES.U.04.04.pdf.

40.

Horn, B. K. (1987). Closed-form solution of absolute orientation using unit quaternions. JOSA A, 4(4), 629–642. doi:10.1364/JOSAA.4.000629.CrossRef

41.

Bachrach, J., & Taylor, C. (2005). Localization in sensor networks. In I. Stojmenovic (Ed.), Handbook of sensor networks: Algorithms and architectures (pp. 277–310). New Jersey: Wiley. doi:10.1002/047174414x.ch9.CrossRef

42.

Alikhani, S., Kunz, T., & St-Hilaire, M. (2010). iCCA-MAP versus MCL and dual MCL: Comparison of mobile node localization algorithms. In Ad-Hoc, mobile and wireless networks (pp. 163–176). Berlin: Springer. doi:10.1007/978-3-642-14785-2_13.

Titel: Wireless Sensor Networks Localization Using Progressive Isomap
verfasst von: Jyoti Kashniyal
Shekhar Verma
K. P. Singh
Publikationsdatum: 13.08.2016
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 3/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-016-3606-0

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