An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm | Wireless Networks Skip to main content

Advertisement

Springer Nature Link
Log in

An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Localization in wireless sensor networks (WSNs) is required to examine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. The anchor nodes positions are known as the nodes that have GPS unit incorporated with them. All sensor nodes are generally not configured with GPS as it is not suitable for indoor environments and/or underwater areas. A network becomes more expensive and utilizes more energy if all nodes are equipped with GPS that is a major drawback of WSNs. Various localization schemes have been proposed in literature, while most research proposals deal with the study of 2D applications. However, in the 3D applications, the area under observation may have a complexity in the sensing environment. An optimized algorithm is required for the determination of node location in 3D environment. In this paper, we propose an adaptive flower pollination algorithm (AFPA) with enhanced exploration and exploitation capabilities of conventional FPA for the localization of sensor nodes in WSN. To test the performance of AFPA, benchmark functions (CEC 2019) are used to compare it with other meta-heuristics. The results show that proposed AFPA outperforms in terms of convergence speed and provides better results for most of the benchmark functions. Also, the proposed AFPA is tested on WSN Localization problem, it provides least localization error in comparison to other techniques applied in 3D WSN environments.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

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
Fig.16

Similar content being viewed by others

References

  1. Karl, H., & Willig, A. (2007). Protocols and architectures for wireless sensor networks. New York: Wiley.

    Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  3. Zhou, G., He, T., Krishnamurthy, S., & Stankovic, J. A. (2006). Models and solutions for radio ir- regularity in wireless sensor networks. ACM Transaction Sensor Networks (TOSN), 2(2), 221–262.

    Article  Google Scholar 

  4. Robles, J. J. (2014). Indoor localization based on wireless sensor networks. AEU-International Journal of Electronics and Communication, 68(7), 578–580.

    Article  Google Scholar 

  5. Boukerche, A., Oliveira, H. A., Nakamura, E. F., & Loureiro, A. A. (2007). Localization systems for wireless sensor networks. IEEE Wireless Communication, 14(6), 6–12.

    Article  Google Scholar 

  6. Cho, H., & Kwon, Y. (2016). Rss-based indoor localization with pdr location tracking for wireless sensor networks. AEU- International Journal of Electronics and Communication, 70(3), 250–256.

    Article  Google Scholar 

  7. Awad, A., Frunzke, T., & Dressler, F. (2007). Adaptive distance estimation and localization in wsn using rssi measures. In 10th euromicro conference on digital system design ar- chitectures, methods and tools, DSD 2007. IEEE; 2007. pp. 471–8.

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

    Article  Google Scholar 

  9. Graefenstein, J., Albert, A., Biber, P., & Schilling, A. (2009). Wireless node localization based on rssi using a rotating antenna on a mobile robot. In 6th workshop on positioning, navigation and communication, 2009. WPNC 2009. IEEE; 2009. pp. 253–9.

  10. Sumathi, R., Srinivasan, R. (2011) RSS-based location estimation in mobility assisted wireless sensor networks. In IEEE 6th international conference on intelligent data acquisi- tion and advanced computing systems (IDAACS), 2011 (vol. 2, pp. 848–52). IEEE.

  11. Guo, Z., Guo, Y., Hong, F., Jin, Z., He, Y., Feng, Y., et al. (2010). Perpendicular intersection: lo- cating wireless sensors with mobile beacon. IEEE Transactions on Vehicular Technology, 59(7), 3501–3509.

    Article  Google Scholar 

  12. Shi, Q., Huo, H., Fang, T., & Li, D. (2009). A 3d node localization scheme for wireless sensor networks 6(3), 167–172.

    Article  Google Scholar 

  13. Wang, L., Zhang, J., & Cao, D. (2012). A new 3-dimensional dv-hop localization algorithm. Journal of Computer Information Systems, 8(6), 2463–2475.

    Google Scholar 

  14. Yan, X., Zhuang, Y., & Jing-jing. , G. (2015). An improved 3D localization algorithm for the wireless sensor network. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2015/315714.

    Article  Google Scholar 

  15. Li, J., Zhong, X., & Lu, I.-T. (2014). Three-dimensional node localization algorithm for wsn based on differential rss irregular transmission model. Journal of Communication, 9(5), 391–397.

    Article  Google Scholar 

  16. Ahmad, T., Li, X. J., & Seet, B.-C. (2017). Parametric loop division for 3d localization in wireless sensor networks. Sensors, 17(7), 1697.

    Article  Google Scholar 

  17. Gopakumar, A., & Jacob, L. Localization in wireless sensor networks using particle swarm optimization.

  18. Chuang, P.-J., & Wu, C.-P. An effective pso-based node localization scheme for wireless sensor networks. In Ninth international conference on parallel and distributed computing, applications and technologies, 2008. PDCAT 2008. IEEE; 2008. pp. 187–94.

  19. Kumar, A., Khosla, A., Saini, J.S., & Singh, S. (2012). Meta-heuristic range-based node localization algorithm for wireless sensor networks. In International conference on localization and GNSS (ICL-GNSS). IEEE; 2012. p. 1–7.

  20. Sujatha, S., & Siddappa, M. (2017). Node localization method for wireless sensor networks based on hybrid optimization of particle swarm optimization and differential evo-lution. IOSR Journal of Computing Engineering, 19(2), 07–12.

    Article  Google Scholar 

  21. Arora, S., & Singh, S. Node localization in wireless sensor networks using butterfly optimization algorithm. Arabian Journal for Science and Engineering 2017:1–11.

  22. Mautz, R., Ochieng, W., Brodin, G., & Kemp, A. H. (2007). 3d wireless network localization from inconsistent distance observations. Ad Hoc Sensor Wireless Networks, 3(2–3), 141–170.

    Google Scholar 

  23. Liu, W., Dong, E., & Song, Y. (2016). Analysis of flip ambiguity for robust three-dimensional node localization in wireless sensor networks. Journal of Parallel Distribution Computing, 97, 57–68.

    Article  Google Scholar 

  24. Bai, S., & Qi, H. (2016). Tackling the flip ambiguity in wireless sensor network localization and beyond. Digitial Signal Processing, 55, 85–97.

    Article  Google Scholar 

  25. Han, S., Yue, J., Meng, W., & Li, C. (2015). A novel flip ambiguities detection algorithm for wsn localization. In IEEE globecom workshops. IEEE; 2015. pp. 1–6.

  26. Singh, P., Khosla, A., Kumar, A., & Khosla, M. (2017). A novel approach for localization of moving target nodes in wireless sensor networks. International Journal of Grid and Distributed Computing, 10(10), 33–44.

    Article  Google Scholar 

  27. Singh, P., Khosla, A., Kumar, A., & Khosla, M. (2018). Computational intelligence-based localization of moving target nodes using single anchor node in wireless sensor networks. Telecommunication Systems, 69(3), 397–411.

    Article  Google Scholar 

  28. Singh, P., Khosla, A., Kumar, A., & Khosla, M. (2017). 3D localization of moving target nodes using single anchor node in anisotropic wireless sensor networks. AEU-International Journal of Electronics and Communications, 82, 543–552.

    Article  Google Scholar 

  29. Singh, P., Khosla, A., Kumar, A., & Khosla, M. (2018). Optimized localization of target nodes using single mobile anchor node in wireless sensor network. AEU-International Journal of Electronics and Communications, 91, 55–65.

    Article  Google Scholar 

  30. Yang, B., Guo, L., Guo, R., Zhao, M., & Zhao, T. (2020). A novel trilateration algorithm for RSSI-based indoor localization. IEEE Sensors Journal, 20(14), 8164–8172.

    Article  Google Scholar 

  31. Goyat, R., Rai, M. K., Kumar, G., Kim, H.-J., & Lim, S.-J. (2020). Improved DV-Hop localization scheme for randomly deployed WSNs. International Journal of Sensors Wireless Communications and Control, 10(1), 94–109.

    Article  Google Scholar 

  32. Nguyen, T. L. N., Vy, T. D., & Shin, Y. (2019). An efficient hybrid RSS-AOA localization for 3d wireless sensor networks. Sensors, 19(9), 2121.

    Article  Google Scholar 

  33. Singh, S. P., & Sharma, S. C. (2019). Implementation of a PSO based improved localization algorithm for wireless sensor networks. IETE Journal of Research, 65(4), 502–514.

    Article  Google Scholar 

  34. Singh, P., & Mittal, N. (2020) An efficient localization approach for WSNs using hybrid DA-FA algorithm. IET-Communications.

  35. Singh, P., & Mittal, N. (2020) Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. AJSE.

  36. Yang, X. S. (2012). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240–249). Berlin: Springer.

  37. Xiuli, W., Yongquan, Z., & Yuting, L. (2017) Elite opposition-based water wave optimization algorithm for global optimization. In Mathematical problems in engineering, Article ID 3498363.

  38. Singh, U., & Salgotra, R. (2017). Pattern synthesis of linear antenna arrays using enhanced flower pollination algorithm. International Journal of Antennas and Propagation, pp. 1–11.

  39. Das, S., Abraham, A., Chakraborty, U. K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13(3), 526–553.

    Article  Google Scholar 

  40. Wu, J., Wang, Y. G., Burrage, K., Tian, Y. C., Lawson, B., & Ding, Z. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113340.

    Article  Google Scholar 

  41. Chuang, L. Y., Tsai, S. W., & Yang, C. H. (2011). Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Applied Mathematics and Computation, 217(16), 6900–6916.

    Article  MathSciNet  Google Scholar 

  42. Janez, B., Mirjam, M., Borko, S. B. (2019). The 100-digit challenge: algorithm. pp. 19–26.

  43. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  44. Mirjalili, S., Gandomi, A. H., Zahra, S., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.

    Article  Google Scholar 

  45. Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210–214). IEEE.

  46. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab, 140413, India

    Prabhjot Singh & Nitin Mittal

Authors
  1. Prabhjot Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nitin Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nitin Mittal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 5.

Table 5 The 100-digit challenge basic test functions [38]
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, P., Mittal, N. An efficient localization approach to locate sensor nodes in 3D wireless sensor networks using adaptive flower pollination algorithm. Wireless Netw 27, 1999–2014 (2021). https://doi.org/10.1007/s11276-021-02557-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-021-02557-7

Keywords

Search

Navigation