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Wireless Networks

, Volume 25, Issue 2, pp 597–609 | Cite as

Novel localization algorithm for wireless sensor network based on intelligent water drops

  • Bassam Faiz Gumaida
  • Juan LuoEmail author
Article

Abstract

High localization rigor and low development expense are the keys and pivotal issues in operation and management of wireless sensor network. This paper proposes a neoteric and high efficiency algorithm which is based on new optimization method for locating nodes in an outdoor environment. This new optimization method is non-linear optimization method and is called intelligent water drops (IWDs). It is proposed that the objective function which need to be optimized by using IWDs is the mean squared range error of all neighboring anchor nodes. This paper affirms that received signal strength indicator (RSSI) is used to determine the interior distances between WSNs nodes. IWDs is an elevated performance stochastic global optimization tool that affirms the minimization of objective function, without being trapped into local optima. The proposed algorithm based on IWDs is more attractive to promote elevated localization precision because of a special features that is an easy implementation of IWDs, in addition to non cost of RSSI. Simulation results have approved that the proposed algorithm able to perform better than that of other algorithms based on optimization techniques such as ant colony, genetic algorithm, and particle swarm optimization. This is distinctly appear in some of the evaluation metrics such as localization accuracy and localization rate.

Keywords

Wireless sensor networks Ranging model RSSI Optimization techniques Intelligent water drops Localization 

Notes

Acknowledgements

This work is partially supported by Program for the National Natural Science Foundation of China (61672220).

References

  1. 1.
    Singh, S., Shivangna, S., & Mittal, E. (2013). Range based wireless sensor node localization using PSO and BBO and its variants. In 2013 International conference on communication systems and network technologies (pp. 309–315).Google Scholar
  2. 2.
    Marks, M., & Niewiadomska-Szynkiewicz, E. (2011). Self-adaptive Localization using signal strength measurements. In SENSORCOMM 2011: The fifth international conference on sensor technologies and applications (pp. 73–78).Google Scholar
  3. 3.
    Jinyu, H., Luo, J., Zhang, Y., Wang, P., & Liu, Y. (2015). Location-based data aggregation in 6LoWPAN. International Journal of Distributed Sensor Networks, 4, 2015.Google Scholar
  4. 4.
    Xiao, F., Wu, M., Huang, H., Wang, R., & Wang, S. (2012). Novel node localization algorithm based on nonlinear weighting least square for wireless sensor networks. International Journal of Distributed Sensor Networks, 8(11), 1238–1241.Google Scholar
  5. 5.
    Luo, J., Jinyu, H., Di, W., & Li, R. (2015). Opportunistic routing algorithm for relay node selection in wireless sensor network. IEEE Transactions on Industrial Informatics, 11(1), 112–121.CrossRefGoogle Scholar
  6. 6.
    Luo, J., Di, W., Li, R., & Pan, C. (2015). Optimal energy strategy for node selection and data relay in WSN-based IoT. Mobile Networks and Applications, 20(2), 169–180.CrossRefGoogle Scholar
  7. 7.
    Cao, W., Wang, H., & Liu, L. (2014). An ant colony optimization algorithm for virtual network embedding. In International Conference on Algorithms and Architectures for Parallel Processing (pp. 299–309). Springer International Publishing.Google Scholar
  8. 8.
    Malhotra, R., Singh, N., & Singh, Y. (2011). Genetic algorithms: Concepts. Design for Optimization of Process Controllers, 4(2), 39–54.Google Scholar
  9. 9.
    Arampatzis, T., Lygeros J., & Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In Proceedings of the 2005 IEEE international symposium on, Mediterrean conference on control and automation intelligent control (pp. 719–724).Google Scholar
  10. 10.
    Amundson, I., & Koutsoukos, X. (2009). A survey on localization for mobile wireless sensor networks. Mobile entity localization and tracking in GPS-less environments (pp. 235–254).Google Scholar
  11. 11.
    Mao, G., Fidan, B., & Anderson, B. (2007). Wireless sensor network localization techniques. Computer Networks, 51(10), 2529–2553.CrossRefzbMATHGoogle Scholar
  12. 12.
    Perillo, M., & Heinzelman, W. (2004). Wireless sensor network protocols. Computer Networks, 52(12), 2292–2330.Google Scholar
  13. 13.
    Rawat, P., Kamal, S. D., Chaouchi, H., & Bonnin, J. M. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. The Journal of Supercomputing, 68(1), 1–48.CrossRefGoogle Scholar
  14. 14.
    Lu, Y. H., & Zhang, M. (2014). Adaptive mobile anchor localization algorithm based on ant colony optimization in wireless sensor networks. International Journal on Smart Sensing and Intelligent Systems, 7(4), 1943–1961.CrossRefGoogle Scholar
  15. 15.
    Kapil, U., & Gandhi, D. K. (2014). Genetic algorithm for wireless sensor network with localization based techniques. International Journal of Scientific and Research Publications, 4(9), 1–6.Google Scholar
  16. 16.
    Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET conference proceedings (pp. 227–230).Google Scholar
  17. 17.
    Zhang, F. (2013). Positioning research for wireless sensor networks based on PSO algorithm. Elektronika Ir Elektrotechnika, 19(9), 7–10.Google Scholar
  18. 18.
    Chuang, P. (2011). Employing PSO to enhance RSS range-based node localization for wireless sensor networks. Journal of Information Science, 1611, 1597–1611.MathSciNetGoogle Scholar
  19. 19.
    Low, K. S., Nguyen, H. A., & Guo, H. (2008). A particle swarm optimization approach for the localization of a wireless sensor network. In 2008 IEEE international symposium on industrial electronics (pp. 1820–1825).Google Scholar
  20. 20.
    Low, K. S., Nguyen, H. A., & Guo, H. (2008). Optimization of sensor node locations in a wireless sensor network. In 2008 Fourth international conference on natural computation (vol. 5, pp. 286–290).Google Scholar
  21. 21.
    Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2), 262–267.CrossRefGoogle Scholar
  22. 22.
    Al Alawi, R. (2011). RSSI based location estimation in wireless sensors networks (pp. 118–122).Google Scholar
  23. 23.
    Hosseini, H. S. (2007). Problem solving by intelligent water drops. In IEEE Congress on evolutionary computation, Singapore (pp. 3226–3231).Google Scholar
  24. 24.
    Hosseini, H. S. (2008). Intelligent water drops algorithm: A new optimization method for solving the multiple knapsack problem. International Journal of Intelligent Computing and Cybernetics, 1(2), 193–212.MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Shah-Hosseini, H. (2009). The intelligent water drops algorithm: A nature-inspired swarm-based optimization algorithm. International Journal of Bio-Inspired Computation, 1(2), 71–79.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Duan, H., Liu, S., & Lei, X. (2008). Air robot path planning based on intelligent water drops optimization. 2008 IEEE international joint conference on neural networks (IEEE World Congress on Computational Intelligence) (pp. 1397–1401).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina

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