Swarm Intelligence Based Localization in Wireless Sensor Networks

  • Dama Lavanya
  • Siba K. Udgata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


In wireless sensor networks, sensor node localization is an important problem because sensor nodes are randomly scattered in the region of interest and they get connected into network on their own. Finding location without the aid of Global Positioning System (GPS) in each node of a sensor network is important in cases where GPS is either not accessible, or not practical to use due to power, cost, or line of sight conditions. The objective of this paper is to find the locations of nodes by using Particle Swarm Optimization and Artificial Bee Colony algorithm and compare the performance of these two algorithms. The term swarm is used in a general manner to refer to a collection of interacting agents or individuals. We also propose multi stage localization and compared multi stage localization performance with single stage localization.


Wireless Sensor Networks Localization Beacon Particle Swarm Optimization Artificial Bee Colony Algorithm Multi stage localization 


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  1. 1.
    Yu, K., Oppermannr, I.: Performance of UWB position estimation based on TOA measurements. In: Proc. Joint UWBST and IWUWBS, Kyoto, Japan, pp. 400–404 (2004)Google Scholar
  2. 2.
    Chan, Y.T., Ho, K.C.: A Simple and Efficient Estimator for Hyperbolic Location. IEEE Transactions on Signal Processing 42(8), 1905–1915 (1994)CrossRefGoogle Scholar
  3. 3.
    Doherty, L., Pister, K., El Ghaoui, L.: Convex Position estimation in wireless sensor networks. In: IEEE INFOCOM, vol. 3, pp. 1655–1663 (2001)Google Scholar
  4. 4.
    Biswas, P., Ye, Y.: Semidefinite programming for ad hoc wireless sensor network localization. In: Third International Symposium on Information Processing in Sensor Networks, pp. 46–54 (2004)Google Scholar
  5. 5.
    Kannan, A.A., Mao, G., Vucetic, B.: Simulated annealing based wireless sensor network localization. Journal of Computers (2), 15–22 (2006)Google Scholar
  6. 6.
    Vossiek, M., Wiebking, L., Gulden, P., Wieghardt, J., Hoffmann, C., Heide, P.: Wireless local positioning. IEEE Microwave Magazine 4(4), 77–86 (2003)CrossRefGoogle Scholar
  7. 7.
    Niculescu, D., Nath, B.: Ad hoc positioning system (aps). In: IEEE GLOBECOM 2001, vol. 5, pp. 2926–2931 (2001)Google Scholar
  8. 8.
    Savvides, A., Park, H., Srivastava, M.B.: The bits and flops of the n-hop multilateration primitive for node localization problems. In: International Workshop on Sensor Networks Application, pp. 112–121 (2002)Google Scholar
  9. 9.
    Bulusu, N., Heidemann, J., Estrin, D.: GPS-less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications Magazine 7(5), 28–34 (2000)CrossRefGoogle Scholar
  10. 10.
    Patil, M.M., Shaha, U., Desai, U.B., Merchant, S.N.: Localization in Wireless Sensor Networks using Three Masters. In: ICPWC 2005, pp. 384–388 (2005)Google Scholar
  11. 11.
    Doherty, L., Pister, K., Ghaoui, L.E.: Convex position estimation in wireless sensor networks. In: IEEE INFOCOM 2001, vol. 3, pp. 1655–1663 (2001)Google Scholar
  12. 12.
    Liang, T.C., Wang, T.C., Ye, Y.: A gradient search method to round the semi definite programming relaxation solution for ad hoc wireless sensor network localization. Stanford University, formal report 5 (2004),
  13. 13.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  14. 14.
    Noel, M.M., Joshi, P.P., Jannett, T.C.: Improved Maximum Likelihood Estimation of Target Position in Wireless Sensor Networks using Particle Swarm Optimization. In: Third International Conference on Information Technology: New Generations, ITNG 2006, pp. 274–279 (2006)Google Scholar
  15. 15.
    Chen, Y., Dubey, V.K.: Ultra wideband source localization using a particle-swarm-optimized Capon estimator. In: IEEE International Conference on Communications, vol. 4, pp. 2825–2829 (2005)Google Scholar
  16. 16.
    Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), pp. 81–86 (2001)Google Scholar
  17. 17.
    Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), pp. 94–97 (2001)Google Scholar
  18. 18.
    Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dama Lavanya
    • 1
  • Siba K. Udgata
    • 1
  1. 1.Department of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

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