Quantitative and Spatial Evaluation of Distance-Based Localization Algorithms

Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Indoor localization, especially in wireless networks (WN) has become an important research focus in computer science during the past ten years. Several approaches exist to estimate a node’s position relative to other devices. Most approaches are based on distance measurements and localization algorithms. In this chapter we provide an overview of common and new localization algorithms. A detailed investigation on the error distribution and the real world behaviour of these algorithms is presented. We also provide a discussion of the evaluation results that leads to open questions and future research approaches.

Keywords

Wireless networks Localization Spatial error simulation 

References

  1. Baar M, Will H, Blywis B, Hillebrandt T, Liers A, Wittenburg G, Schiller J (2008) The ScatterWeb MSB-A2 platform for wireless sensor networks, technical report B-08-15, Freie Universität Berlin, Institute of Computer ScienceGoogle Scholar
  2. Biaz S, Ji Y (2005) Precise distributed localization algorithms for wireless networks. In: Sixth IEEE international symposium on a world of wireless mobile and multimedia networksGoogle Scholar
  3. Dennis J, Schnabel R (1996) Numerical methods for unconstrained optimization and nonlinear equations (Classics in Applied Mathematics). Society for Industrial Mathematics, Englewood CliffsGoogle Scholar
  4. Dulman S, Havinga P, Baggio A, Langendoen K (2008) Revisiting the Cramer-Rao bound for localization algorithms. In: 4th IEEE/ACM DCOSS Work-in-progress paper, 2008Google Scholar
  5. Haiyong L, Hui L, Fang Z, Jinghua P (2011) An iterative clustering-based localization algorithm for wireless sensor networks. China Communications, pp 58–64Google Scholar
  6. Kuruoglu GS, Erol M, Oktug S (2009) Localization in wireless sensor networks with range measurement errors. In: Advanced international conference on telecommunications, pp 261–266Google Scholar
  7. Langendoen K, Reijers N (2003) Distributed localization in wireless sensor networks: a quantitative comparison. Int J Comput Telecommun Networking 43:499–518Google Scholar
  8. Li Z, Trappe W, Zhang Y, Nath B (2005) Robust statistical methods for securing wireless localization in sensor networks. Information processing in sensor networks, pp 91–98Google Scholar
  9. Mao G, Fidan B, Anderson B (2007) Wireless sensor network localization techniques. Computer networks, pp 2529–2553Google Scholar
  10. Nanotron Technologies Gmbh (2009) nanoPAN 5375 RF Module datasheet, BerlinGoogle Scholar
  11. Navidi W, Murphy W, Hereman W (1998) Statistical methods in surveying by trilateration. Computational statistics and data analysis, pp 209–227Google Scholar
  12. Rousseeuw P, Leroy A (1987) Robust regression and outlier detection. Wiley, New YorkGoogle Scholar
  13. Savvides A, Park H, Srivastava M (2002) 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 (WSNA), pp 112–121Google Scholar
  14. Torres-Solis J, Falk T, Chau T (2010) A review of indoor localization technologies: towards navigational assistance for topographical disorientation. In: Ambient intelligence.InTech, Vukovar, CroatiaGoogle Scholar
  15. Vaghefi R, Buehrer M (2012) Cooperative sensor localization with NLOS mitigation using semidefinite programming. 9th workshop on positioning, navigation and communicationGoogle Scholar
  16. Will H, Hillebrandt T, Kyas M (2012) The FU Berlin parallel lateration-algorithm simulation and visualization engine. 9th workshop on positioning, navigation and communicationGoogle Scholar
  17. Yang Z, Liu Y (2010) Quality of trilateration: confidence based iterative localization. IEEE Trans Parallel Distrib Syst 21(5):631–640CrossRefGoogle Scholar
  18. Yang B, Scheuing J (2005) Cramer-Rao bound and optimum sensor array for source localization from time differences of arrival. IEEE International conference on acoustics, speech, and signal processing, pp 961–964Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceFreie Universität BerlinBerlinGermany

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