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Weighted centroid localization based on compressive sensing

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Abstract

To solve the problem of estimating the locations of sensor nodes in wireless sensor networks where most nodes are without an effective positioning device, a novel range-free localization algorithm—weighted centroid localization based on compressive sensing (WCLCS) is proposed. WCLCS makes use of compressive sensing to get decomposition coefficients between each nonbeacon node and beacon nodes. According to these coefficients, WCLCS algorithm decides the weighted value of each beacon node for Centroid and estimates the locations of nonbeacon nodes. The simulation results show that WCLCS has better localization performance than LSVM.

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References

  1. Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of the IEEE INFOCOM (pp. 775–784).

  2. Mourad, F., Snoussi, H., & Richard, C. (2011). Interval-based localization using RSSI comparison in MANETs. IEEE Transactions on Aerospace and Electronic Systems, 47(4), 2897–2910.

    Article  Google Scholar 

  3. Wang, T. (2012). Novel sensor location scheme using time-of-arrival estimates. IET Signal Processing, 6(1), 8–13.

    Article  MathSciNet  Google Scholar 

  4. Savvides, A., Han, C., & Srivastava, M. B. (2001). Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on mobile computing and networking (pp. 166–179).

  5. Xu, E., Ding, Z., & Dasgupta, S. (2011). Reduced complexity semidefinite relaxation algorithms for source localization based on time difference of arrival. IEEE Transactions on Mobile Computing, 10(9), 1276–1282.

    Article  Google Scholar 

  6. Dragos, N., & Badri, N. (2003). Ad hoc positioning system (APS) using AoA. In Proceedings of the Info Com 2003 Francisco (pp. 1734–1743).

  7. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communications Magazine, 7(5), 28–34.

    Article  Google Scholar 

  8. Jun, W., Urriza, P., Yuxing, H., & Cabric, D. (2011). Weighted centroid localization algorithm: Theoretical analysis and distributed implementation. IEEE Transactions on Wireless Communications, 10(10), 3403–3413.

    Article  Google Scholar 

  9. Xinyu, Y., Qingru, K., & Xiangjun, D. (2010). A improved weighted centroid location algorithm. Journal of Xi’an Jiaotong University, 44(8), 1–4.

    Google Scholar 

  10. He, T., Huang, C., & Blum, B. M. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the Mobi Com ‘03 (pp. 81–95).

  11. Meertens, L., & Fitzpatrick, S. (2004). The distributed construction of a global coordinate system in a network of static computational nodes from inter-node distances. Technical report. Palo Alto, CA: Kestrel Institute.

  12. Bulusu, N., Byehkovskiy, V., & Estrin, D. (2002). Sealable ad hoc deployable RF-based localization. In Proceedings of the Grace Hopper celebration of women in computing conference. http://www.isi.edu/~johnh/PAPERS/Bulusu02a.pdf.

  13. Tran, D. A., & Nguyen, T. (2008). Localization in wireless sensor networks based on support vector machines. IEEE Transactions on Parallel and Distributed Systems, 19(7), 981–994.

    Article  Google Scholar 

  14. Goldenberg, D., Bihler P., Cao, M., Fang, J., Anderson, B., Morse, A. S., et al. (2006). Localization in sparse networks using sweeps. In Proceedings of the ACM Mobi Com (pp. 110–121).

  15. Yang, Z., Liu, Y., & Li, X. (2010). Beyond trilateration: On the localizability of wireless ad-hoc networks. IEEE/ACM Transactions on Networking, 18(6), 1806–1814.

    Article  Google Scholar 

  16. Chen, H., HongYang, C., & LingGe, J. (2010). Distributed wireless sensor network localization via sequential greedy optimization algorithm. IEEE Transactions on Signal Processing, 58(6), 3328–3340.

    Article  MathSciNet  Google Scholar 

  17. Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

    Article  MATH  MathSciNet  Google Scholar 

  18. Candes, E. (2006). Compressive sampling. International Congress of Mathematicians, 3, 1433–1452.

    MathSciNet  Google Scholar 

  19. Candes, E., & Plan, Y. (2011). A probabilistic and RIP less theory of compressed sensing. IEEE Transactions on Information Theory, 57(11), 7235–7254.

    Article  MathSciNet  Google Scholar 

  20. Chen, S. B., Donoho, D. L., & Saunders, M. A. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61.

    Article  MathSciNet  Google Scholar 

  21. Mallat, S., & Zhang, Z. (1993). Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Process, 41(12), 3397–3415.

    Article  MATH  Google Scholar 

  22. Tropp, J. A., & Gilbert, A. C. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53(12), 4655–4666.

    Article  MATH  MathSciNet  Google Scholar 

  23. Cands, E., & Tao, T. (2006). Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12), 5406–5425.

    Article  Google Scholar 

  24. IEEE standard online resource provided by IEEE 802.15 WPAN. http://www.ieee802.org/15/pub/TG4.html.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61077079; the Ph.D. Programs Foundation of Ministry of Education of China under Grant No. 20102304110013 and the Fundamental Research Funds for the Central Universities under Grant No. HEUCF1208.

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Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, People’s Republic of China

    Chunhui Zhao, Yunlong Xu & Hui Huang

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  1. Chunhui Zhao
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  2. Yunlong Xu
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  3. Hui Huang
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Correspondence to Yunlong Xu.

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Zhao, C., Xu, Y. & Huang, H. Weighted centroid localization based on compressive sensing. Wireless Netw 20, 1527–1540 (2014). https://doi.org/10.1007/s11276-014-0686-1

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