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Range-Based Localization in Mobile Sensor Networks

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Wireless Sensor Networks (EWSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 3868))

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Abstract

Localization schemes for wireless sensor networks can be classified as range-based or range-free. They differ in the information used for localization. Range-based methods use range measurements, while range-free techniques only use the content of the messages. None of the existing algorithms evaluate both types of information. Most of the localization schemes do not consider mobility. In this paper, a Sequential Monte Carlo Localization Method is introduced that uses both types of information as well as mobility to obtain accurate position estimations, even when high range measurement errors are present in the network and unpredictable movements of the nodes occur. We test our algorithm in various environmental settings and compare it to other known localization algorithms. The simulations show that our algorithm outperforms these known range-oriented and range-free algorithms for both static and dynamic networks. Localization improvements range from 12% to 49% in a wide range of conditions.

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References

  1. Savvides, A., Park, H., Srivastava, M.: The Bits and Flops of the N-Hop Multilateration Primitive for Node Localization Problems. In: First ACM International Workshop on Wireless Sensor Networks and Application, Atlanta, GA (September 2002)

    Google Scholar 

  2. Savarese, C., Rabay, J., Langendoen, K.: Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks. In: USENIX Technical Annual Conference, Monterey, CA (June 2002)

    Google Scholar 

  3. Langendoen, K., Reijers, N.: Distributed localization in wireless sensor networks: A quantitative comparison. Computer Networks, special issue on Wireless Sensor Networks (2003)

    Google Scholar 

  4. Shang, Y., Ruml, W., Zhang, Y., Fromherz, M.: Localization From Mere Connectivity. In: MobiHoc 2003, June 2003, Annapolis, Maryland (2003)

    Google Scholar 

  5. Shang, Y., Ruml, W.: Improved MDS-based localization. In: Infocom 2004 (2004)

    Google Scholar 

  6. Evers, L., Bach, W., Dam, D., Jonker, M., Scholten, H., Havinga, P.: An iterative quality based localization algorithm for adhoc networks, Department of Computer Science, University of Twente (2002)

    Google Scholar 

  7. Evers, L., Dulman, S., Havinga, P.: A distributed precision based localization algorithm for ad-hoc networks. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 269–286. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Beutel, J.: Geolocation in a picoradio environment. MS Thesis, ETH Zurich, Electronics Lab. (1999)

    Google Scholar 

  9. Handschin, J.E.: Monte Carlo Techniques for Prediction and Filtering of Non-Linear Stochastic Processes. Automatica 6, 555–563 (1970)

    Article  MATH  MathSciNet  Google Scholar 

  10. Zaritskii, V.S., Svetnik, V.S., Shimelevich, L.I.: Monte Carlo technique in problems of optimal data processing. Automation and Remote Control 12, 95–103 (1974)

    MathSciNet  Google Scholar 

  11. Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo Localization for Mobile Robots. In: IEEE International Conference on Robotics and Automation (ICRA) (May 1999)

    Google Scholar 

  12. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence Journal (2001)

    Google Scholar 

  13. Hu, L., Evans, D.: Localization for Mobile Sensor Networks. In: Tenth Annual International Conference on Mobile Computing and Networking (MobiCom 2004), USA (2004)

    Google Scholar 

  14. Kong, A., Liu, J.S., Wong, W.H.: Sequential Imputations and Bayesian Missing Data Problems. Journal of the American Statistical Association 89, 278–288 (1994)

    Article  MATH  Google Scholar 

  15. Doucet, A., Godsill, S., Andrieu, C.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)

    Article  Google Scholar 

  16. Camp, T., Boleng, J., Davies, V.: A survey of Mobility Models for Ad Hoc Networks Research. Wireless Communications and Mobile Computing 2(5) (2002)

    Google Scholar 

  17. Tanizaki, H., Mariano, R.S.: Nonlinear and non-Gaussian statespace modeling with Monte-Carlo simulations. Journal of Econometrics 83, 263–290 (1998)

    Article  MATH  Google Scholar 

  18. Niculescu, D., Nath, B.: Ad hoc positioning systems. In: IEEE Globecom 2001, San Antonio (2001)

    Google Scholar 

  19. Dulman, S., Havinga, P.: Statistically enhanced localization schemes for randomly deployed wireless sensor networks. In: DEST International Workshop on Signal Processing for Sensor Networks, Australia (2004)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Dil, B., Dulman, S., Havinga, P. (2006). Range-Based Localization in Mobile Sensor Networks. In: Römer, K., Karl, H., Mattern, F. (eds) Wireless Sensor Networks. EWSN 2006. Lecture Notes in Computer Science, vol 3868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11669463_14

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  • DOI: https://doi.org/10.1007/11669463_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32158-3

  • Online ISBN: 978-3-540-32159-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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