Advertisement

Hybrid Genetic Algorithm–Differential Evolution Approach for Localization in WSN

  • P. Srideviponmalar
  • V. Jawahar Senthil Kumar
  • R. Harikrishnan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Nature-inspired algorithms have the characteristics to learn and decide and to be adaptable, intelligent, and robust, and so they can be used for solving complex problems. This paper deals with one such algorithm named hybrid genetic algorithm–differential evolution for localization in wireless sensor network. This algorithm is used to estimate the position of sensor node. A novel hybrid algorithm is analyzed, designed, and implemented. This algorithm provides better accuracy and is simple to implement.

Keywords

Genetic algorithm Wireless sensor network Sensor node Anchor node Differential evolution 

References

  1. 1.
    D’Ore, S., Galluccio, L., Morabito, G., Palazzo, S.: Exploiting object group localization in the internet of things: performance analysis. IEEE Trans. Veh. Technol. 64(8), 3645–3656 (2015)CrossRefGoogle Scholar
  2. 2.
    Singh, S.P., Sharma, S.C.: Range free localization techniques in wireless sensor network: a review. Procedia Comput. Sci. 57, 7–16 (2015). Science DirectCrossRefGoogle Scholar
  3. 3.
    Mao, G., Fidan, B., Anderson, B.D.: Wireless sensor network localization techniques. Comput. Netw. 51, 2529–2553 (2007). Science DirectCrossRefGoogle Scholar
  4. 4.
    Raghavendra Kulkarni, V., Förster, A., Venayagamoorthy, G.K.: Computational intelligence in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 13(1), 68–96 (2011)CrossRefGoogle Scholar
  5. 5.
    Halders, S., Ghosal, A.: A survey on mobility-assisted localization techniques in wireless sensor networks. J. Netw. Comput. Appl. 82–94 (2016)CrossRefGoogle Scholar
  6. 6.
    Shi, Q., He, C., Chen, H., Jiang, L.: Distributed wireless sensor network localization via sequential greedy optimization algorithm. IEEE Trans. Signal Process. 58(6), 3328–3340 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Salman, N., Ghogho, M., Kemp, A.H.: Optimized low complexity sensor node positioning in wireless sensor networks. IEEE Sens. J. 14(1), 39–46 (2014)CrossRefGoogle Scholar
  8. 8.
    Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16(1), 122–128 (1986)CrossRefGoogle Scholar
  9. 9.
    Vaisakh, K., Srinivas, L.R.: Differential evolution approach for optimal power flow solution. J. Theor. Appl. Inform. Technol. 261–268 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • P. Srideviponmalar
    • 1
  • V. Jawahar Senthil Kumar
    • 2
  • R. Harikrishnan
    • 3
  1. 1.Faculty of Information and Communication EngineeringAnna UniversityChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringAnna UniversityChennaiIndia
  3. 3.School of Electrical and Electronics EngineeringSathyabama UniversityChennaiIndia

Personalised recommendations