Skip to main content
SpringerLink
Log in

Fuzzy logic based 3D localization in wireless sensor networks using invasive weed and bacterial foraging optimization

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The purpose of this paper is to improve the performance of node localization in 3D space for wireless sensor network. To achieve this objective, we propose two range free localization algorithms for 3D space in anisotropic environment using the application of bacterial foraging optimization (BFO) and invasive weed optimization (IWO). In proposed methods, only received signal strength (RSS) information between nodes is sufficient for estimating target nodes locations. The RSS information gives clue to find out the distances between target nodes and anchor nodes. To overcome the non-linearity between RSS and distance, edge weights between target nodes and their neighbouring anchor nodes are considered to estimate the positions of target nodes. To further reduce the computational complexity and to model the edge weights, we use fuzzy logic system in this paper. BFO and IWO techniques are used to further optimize the edge weights separately to achieve the better localization accuracy. The simulation results show the superiority of the proposed algorithms as compared to centroid method, weighted centroid and existing 3D localization algorithms in terms of localization accuracy, stability, positioning coverage and scalability.

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
Fig. 15

Similar content being viewed by others

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Hofmann-Wellenfof, B., Lichtenegger, H., & Collins, J. (1993). Global positioning system: Theory and practice. Berlin: Springer.

    Google Scholar 

  3. Djuknic, G. M., & Richton, R. E. (2001). Geolocation and assisted GPS. Computer, 34(2), 123–125.

    Article  Google Scholar 

  4. Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (APS). In IEEE global telecommunications conference (GLOBECOM’0) (Vol. 5, pp. 2926–2931).

  5. Kumar, S., & Lobiyal, D. K. (2017). Novel DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 64(3), 509–524.

    Article  Google Scholar 

  6. Doherty, L., & El Ghaoui, L. (2001). Convex position estimation in wireless sensor networks. In Proceedings of twentieth annual joint conference of the IEEE computer and communications societies (INFOCOM 2001) (Vol. 3, pp. 1655–1663).

  7. Zadeh, L. A. (1996). Fuzzy logic—Computing with words. IEEE Transactions on Fuzzy Systems, 4(2), 103–111.

    Article  Google Scholar 

  8. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52–67.

    Article  Google Scholar 

  9. Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1(4), 355–366.

    Article  Google Scholar 

  10. 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 

  11. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2005). Range-free localization schemes for large scale sensor networks. ACM Transactions on Embedded Computing System, 4(4), 877–906.

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Bachrach, J., & Taylor, C. (2005). Localization in sensor networks. Handbook of sensor networks: Algorithms and Architectures (Vol. 1).

  14. Zhang, B., Fan, J., Dai, G., & Luan, T. H. (2015). A hybrid localization approach in 3D wireless sensor network. International Journal of Distributed Sensor Networks., 692345, 1–11.

    Google Scholar 

  15. Yun, S., Lee, J., Chung, W., Kim, E., & Kim, S. (2009). A soft computing approach to localization in wireless sensor networks. Expert Systems with Applications, 36(4), 7552–7561.

    Article  Google Scholar 

  16. Lee, S., Park, C., Lee, M. J., & Kim, S. (2014). Multihop range-free localization with approximate shortest path in anisotropic wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2014(1), 1–12.

    Article  Google Scholar 

  17. Chen, X., & Zhang, B. (2014). 3D DV-hop localisation scheme based on particle swarm optimisation in wireless sensor networks. International Journal of Sensor Networks, 16(2), 100–105.

    Article  Google Scholar 

  18. 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 

  19. Chaurasiya, V. K., Jain, N., & Nandi, G. C. (2014). A novel distance estimation approach for 3D localization in wireless sensor network using multi-dimensional scaling. Information Fusion, 15, 5–18.

    Article  Google Scholar 

  20. Chuang, P. J., & Jiang, Y. J. (2014). Effective neural network-based node localisation scheme for wireless sensor networks. IET Wireless Sensor Systems, 4(2), 97–103.

    Article  Google Scholar 

  21. Kim, D. H., Abraham, A., & Cho, J. H. (2007). A hybrid genetic algorithm and bacterial foraging approach for global optimization. Information Sciences, 177(18), 3918–3937.

    Article  Google Scholar 

  22. Gopakumar, A., & Jacob, L. (2009). Performance of some metaheuristic algorithms for localization in wireless sensor networks. International Journal of Network Management, 19(5), 355–373.

    Article  Google Scholar 

  23. Kulkarni, R. V., Venayagamoorthy, G. K., & Cheng, M. X. (2009). Bio-inspired node localization in wireless sensor networks. In IEEE international conference on systems, man and cybernetics (SMC 2009) (pp. 205–210).

  24. Mehrabi, M., Taheri, H., & Taghdiri, P. (2016). An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommunication Systems, 1–9.

  25. Noel, M. M., Joshi, P. P., & Jannett, T. C. (2006). Improved maximum likelihood estimation of target position in wireless sensor networks using particle swarm optimization. In Third IEEE international conference on information technology: New generations (ITNG 2006) (pp. 274–279).

  26. Li, J., Dang, J., Bu, F., & Wang, J. (2014). Analysis and improvement of the bacterial foraging optimization algorithm. Journal of Computing Science and Engineering, 8(1), 1–10.

    Article  Google Scholar 

  27. Liu, X., Zhang, S., & Bu, K. (2016). A locality-based range-free localization algorithm for anisotropic wireless sensor networks. Telecommunication Systems, 62(1), 3–13.

    Article  Google Scholar 

  28. Zhou, G., He, T., Krishnamurthy, S., & Stankovic, J. A. (2006). Models and solutions for radio irregularity in wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(2), 221–262.

    Article  Google Scholar 

  29. Kim, S. Y., & Kwon, O. H. (2005). Location estimation based on edge weights in wireless sensor networks. The Journal of Korean Institute of Communications and Information Sciences, 30(10A), 938–948.

    Google Scholar 

  30. Angelov, P. P., & Buswell, R. A. (2003). Automatic generation of fuzzy rule-based models from data by genetic algorithms. Information Sciences, 150(1), 17–31.

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Institute of Technology, Hamirpur, Himachal Pradesh of India (No. B-198) and Ministry of Human Resource Developments (MHRD) of India with Fundamental Research Funds (No. 2K13-Ph.D-ECE-227).

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India

    Gaurav Sharma & Ashok Kumar

Authors
  1. Gaurav Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashok Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gaurav Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, G., Kumar, A. Fuzzy logic based 3D localization in wireless sensor networks using invasive weed and bacterial foraging optimization. Telecommun Syst 67, 149–162 (2018). https://doi.org/10.1007/s11235-017-0333-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-017-0333-0

Keywords

Search

Navigation