Skip to main content

Advertisement

SpringerLink
Log in

An Efficient Algorithm for Wireless Sensor Network Localization Based on Hierarchical Structure Poly-Particle Swarm Optimization

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Localization accuracy and low operating costs are substantial and key issues in operating and managing wireless sensor networks. The literature has many algorithms which used optimization techniques to promote the localization accuracy. This paper proposes a novel and an efficient algorithm based on state of art of black box optimization techniques, in order to promote the localization accuracy. This optimization technique is called hierarchical structure poly-particle swarm optimization. The proposed algorithm considers that the ranging technique is based on the cheapest distance estimation method which is receive signal strength indicator (RSSI). The proposed algorithm is more charming to promote the localization accuracy, where it has special characteristics such as the easy implementation of HSPPSO. In addition, RSSI is the cost efficiency measurement method. Simulation results demonstrate the effectiveness of the proposed algorithm comparing to other algorithms which based on optimization, e.g., ant colony, genetic algorithm, and basic PSO. This is clearly evident in some of the evaluation metrics such as localization accuracy, localization rate, and complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  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).

  2. Vikrant, S., Patel, R. B., Bhadauria, H. S., & Prasad, D. (2016). Policy for planned placement of sensor nodes in large scale wireless sensor network. KSII Transactions on Internet and Information Systems, 10(7), 3213–3230.

    Google Scholar 

  3. Zhang, S., Yan, S., Weitao, H., Wang, J., & Guo, K. (2015). A component-based localization algorithm for sparse sensor networks combining angle and distance information. KSII Transactions on Internet and Information Systems, 9(3), 1014–1034.

    Google Scholar 

  4. 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).

  5. Xiao, F., Mingtan, W., 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, 1–6, 2012.

    Google Scholar 

  6. 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, 2005 (pp. 719–724).

  7. Amundson, I., & Koutsoukos, XD. (2009). A survey on localization for mobile wireless sensor networks. In Mobile entity localization and tracking in GPS-less environnments (pp. 235–254).

  8. Mao, G., Fidan, B., & Anderson, B. (2007). Wireless sensor network localization techniques. Computer Networks, 51(10), 2529–2553.

    Article  MATH  Google Scholar 

  9. Perillo, M., Heinzelman, W., Yick, J., Mukherjee, B., & Ghosal, D. (2004). Wireless sensor network protocols. Computer Networks, 52(12), 2292–2330.

    Google Scholar 

  10. Rawat, P., Singh, K. D., Chaouchi, H., & Marie Bonnin, J. (2014). Wireless sensor networks: A survey on recent developments and potential synergies. The Journal of Supercomputing, 68(1), 1–48.

    Article  Google Scholar 

  11. Ramadurai, V., & Sichitiu, M. L. (2003). Localization in wireless sensor networks: A probabilistic approach (pp. 275–281).

  12. Alhmiedat, T., & Samara, G. (2013). An indoor fingerprinting localization approach for ZigBee wireless sensor. Networks, 105(2), 190–202.

    Google Scholar 

  13. Mahtab Hossain, K. M., Jin, Y., Soh, W. S., & Van, H. N. (2013). SSD: A robust RF location fingerprint addressing mobile devices’ heterogeneity. IEEE Transactions on Mobile Computing, 12(1), 65–77.

    Article  Google Scholar 

  14. Wang, J. Y., Chen, C. P., Lin, T. S., Chuang, C. L., Lai, T. Y., & Jiang, J. A. (2012). High-precision RSSI-based indoor localization using a transmission power adjustment strategy for wireless sensor networks. In 2012 IEEE 14th international conference on high performance computing and communication & 2012 IEEE 9th international conference on embedded software and systems (pp. 1634–1638).

  15. Lau, E.-E.-L., Lee, B.-G., Lee, S.-C., & Chung, W.-Y. (2008). Enhanced RSSI-based high accuracy real-time user location tracking system for indoor and outdoor environments. International Journal on Smart Sensing and Intelligent Systems, 1(2), 534–548.

    Article  Google Scholar 

  16. Blumrosen, G., Hod, B., Anker, T., Dolev, D., & Rubinsky, B. (2013). Enhanced calibration technique for RSSI-based ranging in body area networks. Ad Hoc Networks, 11(1), 555–569.

    Article  Google Scholar 

  17. Kumar, A., Khosla, A., Saini, J. S., & Sidhu, S. S. (2015). Range-free 3D node localization in anisotropic wireless sensor networks. Applied Soft Computing, 34, 438–448.

    Article  Google Scholar 

  18. 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.

    MathSciNet  Google Scholar 

  19. Uraiya, K., & 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 

  20. Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET conference proceedings (Vol. 3, pp. 227–230).

  21. Zhang, F. (2013). Positioning research for wireless sensor networks based on pso algorithm. Elektronika Ir Elektrotechnika, 19(9), 7–10.

    Google Scholar 

  22. Chuang, P. (2011). Employing PSO to enhance RSS range-based node local-ization for wireless sensor networks. Journal of Information Science, 1611, 1597–1611.

    Google Scholar 

  23. 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).

  24. 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).

  25. 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.

    Article  Google Scholar 

  26. Alawi, R. A. (2011). RSSI based location estimation in wireless sensors networks. In IEEE international conference on networks (pp. 118–122). IEEE.

  27. Oguejiofor, O. S., Okorogu, V. N., Adewale, A., & Osuesu, B. O. (2013). Outdoor localization system using RSSI measurement of wireless sensor network. International Journal of Innovative Technology and Exploring, Engineering, 2(2), 1–6.

    Google Scholar 

  28. Lu, L., Luo, Q., Liu, J. Y., & Long, C. (2008). An improved particle swarm optimization algorithm. In IEEE international conference on granular computing (Vol. 38, pp. 486–490). IEEE.

  29. Gopakumar, A., & Jacob, L. (2008). Localization in wireless sensor networks using particle swarm optimization. In IET international conference on wireless, mobile and multimedia networks (pp. 227–230). IET.

  30. Rini, D. P. (2011). Particle swarm optimization: Technique system and challenges. 14(1), 19–27.

Download references

Acknowledgements

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

Author information

Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

    Bassam Faiz Gumaida & Juan Luo

Authors
  1. Bassam Faiz Gumaida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Luo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gumaida, B.F., Luo, J. An Efficient Algorithm for Wireless Sensor Network Localization Based on Hierarchical Structure Poly-Particle Swarm Optimization. Wireless Pers Commun 97, 125–151 (2017). https://doi.org/10.1007/s11277-017-4497-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4497-4

Keywords

Search

Navigation