Weighted Localization for Underwater Sensor Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 295)

Abstract

ELSN (Efficient Localization for large-scale underwater Sensor Networks) gets the node location by solving equations. However, sometimes the unique solution (no solution or multiple solutions) cannot be obtained from equations because of measurement error. In this case, the node cannot be located. On the basis of ELSN algorithm, a new localization algorithm is proposed. It transforms the process of solving equations into looking for the best point of intersection of the three circles in the plane, and regards the point as the potential location of the node. First, in this algorithm , the node is projected into a two-dimensional plane to reduce the computational complexity of the algorithm. Second, every three reference nodes are selected as a combination at random. That is to say, each triplet of nodes which represent a triplet of equations forms three tangent or intersecting circles. Based on the positional relationship of three circles, an optimal point of intersection is served as a potential location of the target node, and the residuals of each triplet are served as a potential weight. Finally, the weighted results of all potential locations are considered as the final position of the node.

Keywords

Localization Potential location Reference node Residuals Weight 

Notes

Acknowledgments

The work is supported by the National Science foundation of China (41176082, 61073182, 40827003, 61073183), the Fundamental Research Funds for the Central Universities (HEUCF1006), and Young backbone teacher project of Heilongjiang province (1155G15).

References

  1. 1.
    Wang F, Shi L, Ren F (2005) Self-localization systems and algorithms for wireless sensor networks. J Softw 15(6):857–868 (in Chinese)Google Scholar
  2. 2.
    Shanshan W, Jianping Y, Zhiping C, Guomin Z (2008) A RSSI-based self-localization algorithm for wireless sensor networks [J]. J Comput Res Devel 45(1):385–388Google Scholar
  3. 3.
    Cui X, Liu I, Fan X (2009) A distributed anchor free localization algorithm in sensor networks. J Comput Res Dev 46(3):425–433 (in Chinese)Google Scholar
  4. 4.
    Langendoen K, Reijers N (2003) Distributed localization in wireless sensor networks: a quantitative comparison. Comput Netw 42(4):499–518CrossRefGoogle Scholar
  5. 5.
    Niculescu D, Nath B (2004) Position and orientation in ad hoc networks. Ad Hoc Netw 2(1):133–151CrossRefGoogle Scholar
  6. 6.
    Shaobin Cai, Xi Li, Ying Tian et al (2010) Alternating combination trilateration based on circle-selection. J Comput Res Dev 46(2):238–244 (in Chinese)Google Scholar
  7. 7.
    Zhou Z, Peng Z, Cui JH et al (2011) Scalable localization with mobility prediction for underwater sensor networks. IEEE Trans Mob Comput 10(3):335–348CrossRefGoogle Scholar
  8. 8.
    Niculescu D, Nathi B (2001) Ad-hoc localization system (APS). In: Proceeding of IEEE GLOBECOM01. Piscataway, pp 2926–2931Google Scholar
  9. 9.
    Albowitz J, Chen A, Zhang L (2001) Recursive position estimation in sensor networks. In: Proceedings of the IEEE international conference on network protocols (ICNP) 01. Los Angeles, pp. 35–41Google Scholar
  10. 10.
    Zhou Z, Cui J, Zhou S (2010) Efficient localization for large-scale underwater sensor networks. Ad Hoc Netw 8(3):267–279CrossRefGoogle Scholar
  11. 11.
    Caruso A, Paparella F, Vieira L et al (2008) Meandering current model and its application to underwater sensor networks. In: Proceeding of INFOCOM08. Phoenix, pp 221–225Google Scholar
  12. 12.
    Bower AS (1991) A simple kinematic mechanism for mixing fluid parcels across a meandering jet. J Phys Oceanogr 21(1):173–180CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

Personalised recommendations