We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants

by Satvir Singh, Shivangna, Shelja Tayal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 22
Year of Publication: 2013
Authors: Satvir Singh, Shivangna, Shelja Tayal
10.5120/10768-5772

Satvir Singh, Shivangna, Shelja Tayal . Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants. International Journal of Computer Applications. 63, 22 ( February 2013), 31-37. DOI=10.5120/10768-5772

@article{ 10.5120/10768-5772,
author = { Satvir Singh, Shivangna, Shelja Tayal },
title = { Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 22 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number22/10768-5772/ },
doi = { 10.5120/10768-5772 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:11.331243+05:30
%A Satvir Singh
%A Shivangna
%A Shelja Tayal
%T Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 22
%P 31-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In a Wireless Sensor Network (WSN) accurate location of target node is highly desirable as it has strong impact on overall performance of the WSN. This paper proposes the application of different migration variants of Biogeography-Based Optimization (BBO) algorithm and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors for different ranges. Biogeography is collective learning of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i. e. , problem solution. PSO models have only fast convergence but less mature. An investigation on distributed iterative localization is presented in this paper that shows how time consumption and error varies for different ranges. Here the nodes that get localized in iteration act as anchor node. A comparison of the performance of PSO and different migration variants of BBO in terms of number of nodes localized, localization accuracy and computation time is presented.

References
  1. I. Akyildiz,W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," vol. 40, no. 8. IEEE, 2002, pp. 102–114.
  2. D. Estrin, D. Culler, K. Pister, and G. Sukhatme, "Connecting the Physical World with Pervasive Networks," vol. 1, no. 1. IEEE, 2002, pp. 59–69.
  3. G. Pottie and W. Kaiser, "Wireless integrated network sensors," vol. 43, no. 5. ACM, 2000, pp. 51–58.
  4. L. Doherty, L. El Ghaoui et al. , "Convex position estimation in wireless sensor networks," in INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 3. IEEE, 2001, pp. 1655–1663.
  5. R. Kulkarni, G. Venayagamoorthy, and M. Cheng, "Bioinspired node localization in wireless sensor networks," in Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on. IEEE, 2009, pp. 205–210.
  6. A. Pal, "Localization algorithms in wireless sensor networks: Current approaches and future challenges," vol. 2, no. 1, 2010, pp. 45–73.
  7. G. Mao and B. Fidan, "Introduction to wireless sensor network localization," 2009.
  8. N. Patwari, J. Ash, S. Kyperountas, A. Hero III, R. Moses, and N. Correal, "Locating the nodes: Cooperative localization in wireless sensor networks," vol. 22, no. 4. IEEE, 2005, pp. 54–69.
  9. A. Boukerche, H. Oliveira, E. Nakamura, and A. Loureiro, "Localization systems for wireless sensor networks," vol. 14, no. 6. IEEE, 2007, pp. 6–12.
  10. D. Niculescu and B. Nath, "Ad hoc positioning system (aps)," in Global Telecommunications Conference, 2001. GLOBECOM'01. IEEE, vol. 5. IEEE, 2001, pp. 2926– 2931.
  11. C. Rabaey and K. Langendoen, "Robust positioning algorithms for distributed ad-hoc wireless sensor networks," in USENIX technical annual conference, 2002.
  12. A. Savvides, H. Park, and M. Srivastava, "The bits and flops of the n-hop multilateration primitive for node localization problems," in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. ACM, 2002, pp. 112–121.
  13. M. Di Rocco and F. Pascucci, "Sensor network localisation using distributed extended kalman filter," in Advanced intelligent mechatronics, 2007 IEEE/ASME international conference on. IEEE, 2007, pp. 1–6.
  14. R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960.
  15. P. Biswas, T. Lian, T. Wang, and Y. Ye, "Semidefinite programming based algorithms for sensor network localization," vol. 2, no. 2. ACM, 2006, pp. 188–220.
  16. T. Liang, T. Wang, and Y. Ye, "A gradient search method to round the semidefinite programming relaxation solution for ad hoc wireless sensor network localization," vol. 5, 2004.
  17. A. Gopakumar and L. Jacob, "Localization in wireless sensor networks using particle swarm optimization," in Wireless, Mobile and Multimedia Networks, 2008. IET International Conference on. IET, 2008, pp. 227–230.
  18. A. Kannan, G. Mao, and B. Vucetic, "Simulated annealing based localization in wireless sensor network," in Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on. IEEE, 2005, pp. 2–pp.
  19. A. Kumar, A. Khosla, J. Saini, and S. Singh, "Computational intelligence based algorithm for node localization in wireless sensor networks," in Intelligent Systems (IS), 2012 6th IEEE International Conference. IEEE, 2012, pp. 431– 438.
  20. G. Nan, M. Li, and J. Li, "Estimation of node localization with a real-coded genetic algorithm in wsns," in Machine Learning and Cybernetics, 2007 International Conference on, vol. 2. IEEE, 2007, pp. 873–878.
  21. S. Yun, J. Lee, W. Chung, E. Kim, and S. Kim, "A soft computing approach to localization in wireless sensor networks," vol. 36, no. 4. Elsevier, 2009, pp. 7552–7561.
  22. Q. Zhang, J. Wang, C. Jin, and Q. Zeng, "Localization algorithm for wireless sensor network based on genetic simulated annealing algorithm," in Wireless Communications, Networking and Mobile Computing, 2008. WiCOM'08. 4th International Conference on. IEEE, 2008, pp. 1–5.
  23. Q. Zhang, J. Huang, J. Wang, C. Jin, J. Ye, and W. Zhang, "A new centralized localization algorithm for wireless sensor network," in Communications and Networking in China, 2008. ChinaCom 2008. Third International Conference on. IEEE, 2008, pp. 625–629.
  24. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Neural Networks, 1995. Proceedings. , IEEE International Conference on, vol. 4. IEEE, 1995, pp. 1942–1948.
  25. D. Simon, "Biogeography-based optimization," vol. 12, no. 6. IEEE, 2008, pp. 702–713.
  26. X. Hu, Y. Shi, and R. Eberhart, "Recent advances in particle swarm," in Evolutionary Computation, 2004. CEC2004. Congress on, vol. 1. IEEE, 2004, pp. 90–97.
  27. Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J. Hernandez, and R. Harley, "Particle swarm optimization: Basic concepts, variants and applications in power systems," vol. 12, no. 2. IEEE, 2008, pp. 171–195.
  28. A. Wallace, "The geographical distribution of animals," 1876.
  29. C. Darwin and G. Beer, The Origin of Species. Books, Incorporated, 1869.
  30. R. MacArthur and E. Wilson, The Theory of Island Biogeography. Princeton University Press, 2001, vol. 1.
  31. D. Du, D. Simon, and M. Ergezer, "Biogeography-based optimization combined with evolutionary strategy and immigration refusal," in Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on. IEEE, 2009, pp. 997–1002.
Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization Biogeography Based Optimization Enhanced BBO Immigration Refusal Blended BBO Localization Wireless Sensor Networks

Powered by PhDFocusTM