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An improved DV-Hop localization algorithm based on evolutionary algorithms

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Abstract

Localization is one of the most important issues in wireless sensor networks and designing accurate localization algorithms is a common challenge in recent researches. Among all localization algorithms, DV-Hop attracts more attention due to its simplicity; so, we use it as a basis for our localization algorithm in order to improve accuracy. The various evolutionary algorithms such as Genetic, Shuffled Frog Leaping and Particle Swarm Optimization are employed in different phases of the main DV-Hop localization algorithm. Simulation results prove that our proposed method decreases the localization error efficiently without additional hardware.

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References

  1. Tomic, S., & Mezei, I. (2016). Improvements of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 61(1), 93–106.

    Article  Google Scholar 

  2. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  3. Mesmoudi, A., Feham, M., & Labraoui, N. (2013). Wireless sensor networks localization algorithms: A comprehensive survey. International Journal of Computer Networks and Communications (IJCNC), 5(6), 45–64.

    Article  Google Scholar 

  4. Yang, X., Zhang, W., & Song, Q. (2015). An improved DV-Hop algorithm based on shuffled frog leaping algorithm. International Journal of Online Engineering, 11.

  5. Lee, S.-M., Cha, H., & Ha, R. (2007). Energy-aware location error handling for object tracking applications in wireless sensor networks. Computer Communications, 30(7), 1443–1450.

    Article  Google Scholar 

  6. Pensas, H., Raula, H., & Vanhala, J. (2009). Energy efficient sensor network with service discovery for smart home environments. In Third international conference on sensor technologies and applications, 2009 (SENSORCOMM’09). IEEE.

  7. Chen, Y., et al. (2010). A smart gateway for health care system using wireless sensor network. In 2010 fourth international conference on sensor technologies and applications (SENSORCOMM). IEEE.

  8. Girod, L., et al. (2002). Locating tiny sensors in time and space: A case study. In Proceedings of the 2002 IEEE international conference on computer design: VLSI in computers and processors. IEEE.

  9. Rong, P., & Sichitiu, M.L. (2006). Angle of arrival localization for wireless sensor networks. In 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks, 2006 (SECON’06) (Vol. 1). IEEE.

  10. Harter, A., et al. (2002). The anatomy of a context-aware application. Wireless Networks, 8(2/3), 187–197.

    Article  Google Scholar 

  11. Niculescu, D., & Nath, B. (2003). DV based positioning in ad hoc networks. Telecommunication Systems, 22(1–4), 267–280.

    Article  Google Scholar 

  12. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 7(5), 28–34.

    Article  Google Scholar 

  13. He, T., et al. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of 9th annual international conference on mobile computing and networking, San Diego, USA, ACM (pp. 81–95).

  14. Pal, A. (2010). Localization algorithms in wireless sensor networks: Current approaches and future challenges. Network Protocols and Algorithms, 2(1), 45–73.

    Article  Google Scholar 

  15. Niewiadomska-Szynkiewicz, E. (2012). Localization in wireless sensor networks: Classification and evaluation of techniques. International Journal of Applied Mathematics and Computer Science, 22(2), 281–297.

    Article  Google Scholar 

  16. Hu, Y., & Li, X. (2013). An improvement of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 53(1), 13–18.

    Article  Google Scholar 

  17. Kumar, S., & Lobiyal, D. K. (2014). Power efficient range-free localization algorithm for wireless sensor networks. Wireless Networks, 20(4), 681–694.

    Article  Google Scholar 

  18. Ren, W., & Zhao, C. (2013). A localization algorithm based on SFLA and PSO for wireless sensor network. Information Technology Journal, 12(3), 502.

    Article  Google Scholar 

  19. Peng, Bo, & Li, Lei. (2015). An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cognitive Neurodynamics, 9(2), 249–256.

    Article  Google Scholar 

  20. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In IEEE international of first conference on neural networks (pp. 167–171).

  21. Holland, J.H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press.

  22. Eusuff, Muzaffar, Lansey, Kevin, & Pasha, Fayzul. (2006). Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Engineering Optimization, 38(2), 129–154.

    Article  Google Scholar 

  23. Chandirasekaran, D., & Jayabarathi, T. (2015). Wireless sensor networks node localization-a performance comparison of shuffled frog leaping and firefly algorithm in LabVIEW. Telkomnika Indonesian Journal of Electrical Engineering, 14(3), 516–524.

    Google Scholar 

  24. Zhang, Wanli, Yang, Xiaoying, & Song, Qixiang. (2015). Improvement of DV-Hop localization based on evolutionary programming resample. Journal of Software Engineering, 9, 631–640.

    Article  Google Scholar 

  25. Lakshmanan, L., & Tomar, D. C. (2014). Optimizing localization route using particle swarm-a genetic approach. American Journal of Applied Sciences, 11(3), 520.

    Article  Google Scholar 

  26. Lu, C.-F., & Juang, C.-F. (2005). Evolutionary fuzzy control of flexible AC transmission system. In IEEE proceedings of generation, transmission and distribution (Vol. 152. No. 4). IET.

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Authors and Affiliations

  1. Switching and Network Lab, Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Mahshid Mehrabi & Hassan Taheri

  2. Photonics Engineering Group, Amirkabir University of Technology, Tehran, Iran

    Pooria Taghdiri

Authors
  1. Mahshid Mehrabi
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  2. Hassan Taheri
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  3. Pooria Taghdiri
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Correspondence to Hassan Taheri.

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Mehrabi, M., Taheri, H. & Taghdiri, P. An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommun Syst 64, 639–647 (2017). https://doi.org/10.1007/s11235-016-0196-9

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  • DOI: https://doi.org/10.1007/s11235-016-0196-9

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