SpringerLink
Log in

Design and development of mobile anchor assisted node localization strategy using a Hybrid Electric-Coyote Optimization

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

As Wireless Sensor Networks (WSNs) offer essential support for several location-aware applications and protocols, localization is one of the principal methodologies in WSN. In the literature, the mobile anchor node is preferred over the static anchor node to handle the high overhead issue caused by the larger number of anchor nodes. In recent years, the Mobile Anchor Node Assisted Localization (MANAL) problem has received significant attention in various research works, and path-planning techniques for localization using fewer anchor nodes have been implemented. However, recent MANAL breakthroughs in WSNs have provided ample research opportunities. The mobile anchor node is used in this paper to solve the node localization problem in WSN. The locations of the target nodes are determined using a Hybrid Electric-Coyote Optimization Algorithm (HE-COA), the mobile anchor node, and the virtual anchor nodes. The objective of the proposed localization is to minimize the distance error between actual node coordinates and estimated node coordinates. Various path trajectories are investigated in simulation to demonstrate the efficacy of the suggested work. The simulation results show that the proposed algorithm can reduce the localization error compared to the conventional models.

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
EUR 32.99 /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 (United Kingdom)

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

Similar content being viewed by others

Availability of data and material (data transparency)

Not applicable.

Code availability (software application or custom code)

Not applicable.

References

  1. Ding Y, Wang C, **ao L (2010) Using mobile beacons to locate sensors in obstructed environments. J Parallel Distrib Comput 70(6):644–656

    Article  Google Scholar 

  2. Koutsonikolas D, Das SM, Hu YC (2007) Path planning of mobile landmarks for localization in wireless sensor networks. Comput Commun 30(13):2577–2592

    Article  Google Scholar 

  3. Pandey OJ, Gautam V, Jha S, Shukla MK, Hegde RM (2020) Time synchronized node localization using optimal H-node allocation in a small world WSN. IEEE Commun Lett 24(11):2579–2583

    Article  Google Scholar 

  4. Pandey OJ, Mahajan A, Hegde RM (2018) Joint localization and data gathering over a small-world WSN with optimal data MULE allocation. IEEE Trans Veh Technol 67(7):6518–6532

    Article  Google Scholar 

  5. Caceres Najarro LA, Song I, Kim K (2022) Fundamental limitations and state-of-the-art solutions for target node localization in WSNs: a review. IEEE Sens J 22(24):23661–23682

    Article  Google Scholar 

  6. Cui H, Wang Y, Lv J (2012) Path planning of mobile anchor in three-dimensional wireless sensor networks for localization. J Inf Comput Sci 9(8):2203–2210

    Google Scholar 

  7. Ssu K, Ou C, Jiau HC (2005) Localization with mobile anchor points in wireless sensor networks. IEEE Trans Veh Technol 54(3):1187–1197

    Article  Google Scholar 

  8. Lee S, Kim E, Kim C, Kim K (2009) Localization with a mobile beacon based on geometric constraints in wireless sensor networks. IEEE Trans Wirel Commun 8(12):5801–5805

    Article  Google Scholar 

  9. Caceres Najarro LA, Song I, Tomic S, Kim K (2020) Fast localization with unknown transmit power and path-loss exponent in WSNs based on RSS measurements. IEEE Commun Lett 24(12):2756–2760

    Article  Google Scholar 

  10. Yan X, Sun L, Sun Z, Zhou J, Song A (2018) Improved hop-based localisation algorithm for irregular networks. IET Commun 13:520527

    Google Scholar 

  11. Cui H, Wang Y (2012) Four-mobile-beacon assisted localization in three-dimensional wireless sensor networks. Comput Electr Eng 38(3):652–661

    Article  Google Scholar 

  12. Ou C, He W (2013) Path planning algorithm for mobile anchor based localization in wireless sensor networks. IEEE Sens J 13(2):466–475

    Article  Google Scholar 

  13. Amundson I, Koutsoukos XD (2009) A survey on localization for mobile wireless sensor networks. In: Mobile entity localization and tracking in GPS-less environnments, In: Lecture notes in computer science, vol 5801, pp 235–254

  14. Akcan H, Kriakov V, Bronnimann H, Delis A (2010) Managing cohort movement of mobile sensors via GPS-free and compass free node localization. J Parallel Distrib Comput 70(7):743–757

    Article  Google Scholar 

  15. Han G, Xu H, Jiang J, Shu L, Hara T, Nishio S (2013) Path planning using a mobile anchor node based on trilateration in wireless sensor networks. Wirel Commun Mob Comput 13(14):1324–1336

    Article  Google Scholar 

  16. Kim E, Lee S, Kim C, Kim K (2010) Mobile beacon-based 3D-localization with multidimensional scaling in large sensor networks. IEEE Commun Lett 17(7):647–649

    Article  Google Scholar 

  17. Huiya L, Jianbo X (2010) Path planning for mobile beacon nodes in localization for wireless sensor networks. Chin J Sens Actuators 23:873–877

    Google Scholar 

  18. Han G, Yang X, Liu L, Guizani M, Zhang W (2017) A disaster management-oriented path planning for mobile anchor node based localization in wireless sensor networks. IEEE Trans Emerg Top Comput 99:1–1

    Google Scholar 

  19. Han G, Chao J, Zhang C, Shu L, Li Q (2014) The impacts of mobility models on dv-hop based localization in mobile wireless sensor networks. J Netw Comput Appl 42:70–79

    Article  Google Scholar 

  20. Rezazadeh J, Moradi M, Ismail AS, Dutkiewicz E (2014) Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks. IEEE Sens J 14(9):3052–3064

    Article  Google Scholar 

  21. Han G, Zhang C, Lloret J, Shu L, Rodrigues JJPC (2014) A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks. Sci World J. https://doi.org/10.1155/2014/219371

    Article  Google Scholar 

  22. Erdemira E, Engin Tuncer T (2018) Path planning for mobile-anchor based wireless sensor network localization: static and dynamic schemes. Ad Hoc Netw. https://doi.org/10.1016/j.adhoc.2018.04.005

    Article  Google Scholar 

  23. Halder S, Ghosal A (2016) A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wirel Netw 22:2317–2336. https://doi.org/10.1007/s11276-015-1101-2

    Article  Google Scholar 

  24. Song L, Zhu JR, Zhang P (2017) Node localization algorithm based on mobile anchor in wireless sensor networks. December

  25. Tingting XU, **g**g WANG, Wei SHI, Jianfeng WANG, Zhe CHEN (2019) A localization algorithm using a mobile anchor node based on region determination in underwater wireless sensor networks. J Ocean Univ China (Oceanic and Coastal Sea Research) 18(2):394–402

    Google Scholar 

  26. 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 Int J Electron C 82:543–552

    Article  Google Scholar 

  27. Sun S, Zhao J, Tian X, Zhang J (2019) Path planning for multiple mobile anchor nodes assisted localization in wireless sensor networks. Measurement 141:124–136

    Article  Google Scholar 

  28. Singh P, Khosla A, Kumar A, Khosla M (2018) Optimized localization of target nodes using single mobile anchor node in wireless sensor network. AEU Int J Electron C 91:55–65

    Article  Google Scholar 

  29. Kaushik A, Lobiyal D (2021) Localization in mobile wireless sensor networks using drones. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4213

    Article  Google Scholar 

  30. Kaushik A, Lobiyal DK (2021). Localization in wireless sensor networks using a mobile anchor and subordinate nodes. In: Applied soft computing and communication networks. Lecture notes in networks and systems, vol 187, pp 177–188. Springer, Singapore

  31. Han G, Jiang J, Zhang C, Duong TQ, Guizani M, Karagiannidis G (2015) A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Access

  32. Han G, Jiang J, Zhang C, Duong TQ, Guizani M, Karagiannidis GK (2016) A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Commun Surv Tutor 18(3):2220–2243

    Article  Google Scholar 

  33. Sagan H (1994) “Hilberts space-filling curve,’’ Space-filling curves. Springer, Berlin, pp 9–30

    Book  Google Scholar 

  34. Joshi AA, Bhatt MC, Sinha A (2019) Modification of Hilbert’s space-filling curve to avoid obstacles: a robotic path-planning strategy. In: 2019 sixth Indian control conference (ICC), Hyderabad, India, pp 338–343

  35. Bader M, Bungartz HJ, Mehl M (2011) Space-filling curves. In: Padua D (ed) Encyclopedia of parallel computing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09766-4_145

    Chapter  Google Scholar 

  36. Ren X, Gao C, ** Y (2013) A node localization algorithm based on simple particle swarm optimization in wireless sensor networks. J Comput Inf Syst 9(22):9203–9210

    Google Scholar 

  37. Raja S, Amudhavel J, Dhavachelvan P, Vengattaraman T (2017) GWO-LPWSN: Grey Wolf optimization algorithm for node localization problem in wireless sensor networks. J Comput Netw Commun 2017(2):1–10

    Google Scholar 

  38. Yuan Z, Wang W, Wang H, Yildizbasi A (2020) Developed Coyote Optimization Algorithm and its application to optimal parameters estimation of PEMFC model. Energy Rep 6:1106–1117

    Article  Google Scholar 

  39. Yilmaz S, Sen S (2020) Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Comput Appl 32:11543–11578

    Article  Google Scholar 

  40. Han G, Jiang J, Chao J, Yang X (2017) Path planning for a group of mobile anchor nodes based on regular triangles in wireless sensor networks. Neurocomputing 270(C):198208

    Google Scholar 

  41. Xue W, Qiu W, Hua X, Yu K (2017) Improved Wi-Fi RSSI measurement for indoor localization. IEEE Sens J 17(7):2224–2230

    Article  Google Scholar 

  42. http://db.csail.mit.edu/labdata/labdata.html

  43. Pandey OJ, Hegde RM (2017) Node localization over small world WSNs using constrained average path length reduction. Ad Hoc Netw 67:87–102

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. VSSUT, Burla, India

    V. Ch Sekhar Rao Rayavarapu & Arunanshu Mahapatro

  2. LENDI Institute of Engineering and Technology, Vizianagaram, India

    R. Divya Kanti

Authors
  1. V. Ch Sekhar Rao Rayavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arunanshu Mahapatro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Divya Kanti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Ch Sekhar Rao Rayavarapu.

Ethics declarations

Conflicts of interest

No conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sekhar Rao Rayavarapu, V.C., Mahapatro, A. & Kanti, R.D. Design and development of mobile anchor assisted node localization strategy using a Hybrid Electric-Coyote Optimization. Evol. Intel. 17, 1405–1423 (2024). https://doi.org/10.1007/s12065-023-00834-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-023-00834-2

Keywords

Search

Navigation