Skip to main content
Springer Nature Link
Log in

An IMOA DV-Hop localization algorithm in WSN based on hop count and hop distance correction

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Wireless sensor networks (WSNs) have been widely used in environmental monitoring due to their low cost advantages. In WSNs monitoring, the location information is significant, because data collected by sensor nodes is valuable only if the locations of nodes are known. DV-Hop algorithm is a popular localization algorithm in WSNs monitoring. However, DV-hop has low localization accuracy due to its imperfect hop count, hop distance and location calculation mechanism. Therefore, in order to improve its localization accuracy, we improve the three stages of DV-hop respectively: Firstly, the anchor node broadcasts in three types of communication radius to reduce hop count error. Secondly, we utilize local average hop distance to reduce the hop distance calculation error. Finally, we use the heuristic algorithm MOA to calculate node positions. Meanwhile, we utilize the good point set, t-distribution and Levy flight to improve the global optimization ability of MOA. In simulation experiments, we use Matlab2018a to verify algorithm performance. The simulation results show that the proposed algorithm outperforms the comparison algorithm in different communication radius, number of anchor nodes, and total number of nodes. It performs optimally in both localization efficiency and accuracy, and has better robustness.

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
$34.99 /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 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

Similar content being viewed by others

Data availability

The data that supporting the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Strumberger I, Minovic M, Tuba M, Bacanin N (2019) Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors 19(11):2515–2544

    Article  Google Scholar 

  2. Kim TH, Goyat R, Rai MK, Kumar G, Buchanan WJ, Saha R, Thomas R (2019) A novel trust evaluation process for secure localization using a decentralized blockchain in wireless sensor networks. IEEE Access 7:184133–184144

    Article  Google Scholar 

  3. Du R, Gkatzikis L, Fischione C, Xiao M (2015) Energy efficient sensor activation for water distribution networks based on compressive sensing. IEEE J Sel Areas Commun 33(12):2997–3010

    Article  Google Scholar 

  4. Boubrima A, Bechkit W, Rivano H (2017) Optimal WSN deployment models for air pollution monitoring. IEEE Trans Wireless Commun 16(5):2723–2735

    Article  Google Scholar 

  5. Boukerche A, Oliveira HA, Nakamura EF, Loureiro AA (2007) Localization systems for wireless sensor networks. IEEE Wirel Commun 14(6):6–12

    Article  Google Scholar 

  6. Tomic S, Beko M, Dinis R, Montezuma P (2017) Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements. Pervasive Mob Comput 37:63–77

    Article  Google Scholar 

  7. Nemer I, Sheltami T, Shakshuki E, Elkhail AA, Adam M (2021) Performance evaluation of range-free localization algorithms for wireless sensor networks. Pers Ubiquit Comput 25:177–203

    Article  Google Scholar 

  8. Hoang MT, Yuen B, Dong X, Lu T, Westendorp R, Reddy K (2019) Recurrent neural networks for accurate RSSI indoor localization. IEEE Internet Things J 6(6):10639–10651

    Article  Google Scholar 

  9. Zhao S, Zhang XP, Cui X, Lu M (2021) A new TOA localization and synchronization system with virtually synchronized periodic asymmetric ranging network. IEEE Internet Things J 8(11):9030–9044

    Article  Google Scholar 

  10. Wu P, Su S, Zuo Z, Guo X, Sun B, Wen X (2019) Time difference of arrival (TDoA) localization combining weighted least squares and firefly algorithm. Sensors 19(11):2554–2567

    Article  Google Scholar 

  11. Hao K, Xue Q, Li C, Yu K (2020) A hybrid localization algorithm based on Doppler shift and AOA for an underwater mobile node. IEEE Access 8:181662–181673

    Article  Google Scholar 

  12. Cai X, Wang P, Du L, Cui Z, Zhang W, Chen J (2019) Multi-objective three-dimensional DV-hop localization algorithm with NSGA-II. IEEE Sens J 19(21):10003–10015

    Article  Google Scholar 

  13. Liu J, Wang Z, Yao M, Qiu Z (2016) VN-APIT: Virtual nodes-based range-free APIT localization scheme for WSN. Wireless Netw 22:867–878

    Article  Google Scholar 

  14. Blumenthal J, Grossmann R, Golatowski F, Timmermann D (2007) Weighted centroid localization in zigbee-based sensor networks. In 2007 IEEE International Symposium on Intelligent Signal Processing, pp 1–6

  15. Zhao LZ, Wen XB, Li D (2015) Amorphous localization algorithm based on BP artificial neural network. Int J Distrib Sens Netw 11(7):657241–657246

    Article  Google Scholar 

  16. Cao Y, Wang Z (2019) Improved DV-hop localization algorithm based on dynamic anchor node set for wireless sensor networks. IEEE Access 7:124876–124890

    Article  Google Scholar 

  17. Chen H, Sezaki K, Deng P, So HC (2008) An improved DV-Hop localization algorithm with reduced node location error for wireless sensor networks. IEICE Trans Fundam Electron Commun Comput Sci 91(8):2232–2236

    Article  Google Scholar 

  18. Kumar S, Lobiyal DK (2017) Novel DV-Hop localization algorithm for wireless sensor networks. Telecommun Syst 64:509–524

    Article  Google Scholar 

  19. Kumar S, Lobiyal DK (2013) An advanced DV-Hop localization algorithm for wireless sensor networks. Wireless Pers Commun 71:1365–1385

    Article  Google Scholar 

  20. Hu Y, Li X (2013) An improvement of DV-Hop localization algorithm for wireless sensor networks. Telecommun Syst 53:13–18

    Article  Google Scholar 

  21. Messous S, Liouane H, Cheikhrouhou O, Hamam H (2021) Improved recursive DV-hop localization algorithm with RSSI measurement for wireless sensor networks. Sensors 21(12):4152–4166

    Article  Google Scholar 

  22. Xiao H, Zhang H, Wang Z, Gulliver TA (2017) An RSSI based DV-hop algorithm for wireless sensor networks. In 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), pp 1–6

  23. Kaur A, Kumar P, Gupta GP (2019) A weighted centroid localization algorithm for randomly deployed wireless sensor networks. J King Saud Univ-Comput Inform Sci 31(1):82–91

    Google Scholar 

  24. Messous S, Liouane H, Liouane N (2020) Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks. Telecommun Syst 73:75–86

    Article  Google Scholar 

  25. Ouyang A, Lu Y, Liu Y, Wu M, Peng X (2021) An improved adaptive genetic algorithm based on DV-Hop for locating nodes in wireless sensor networks. Neurocomputing 458:500–510

    Article  Google Scholar 

  26. Chen X, Zhang B (2012) Improved DV-Hop node localization algorithm in wireless sensor networks. Int J Distrib Sens Netw 8(8):213980–213986

    Article  Google Scholar 

  27. Lei Y, De G, Fei L (2020) Improved sparrow search algorithm based DV-Hop localization in WSN. In 2020 Chinese Automation Congress (CAC), pp 2240–2244

  28. Cui Z, Sun B, Wang G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber–physical systems. J Parallel Distribut Comput 103:42–52

    Article  Google Scholar 

  29. Li J, Gao M, Pan JS, Chu SC (2021) A parallel compact cat swarm optimization and its application in DV-Hop node localization for wireless sensor network. Wireless Netw 27:2081–2101

    Article  Google Scholar 

  30. Chen J, Zhang W, Liu Z, Wang R, Zhang S (2020) CWDV-Hop: A hybrid localization algorithm with distance-weight DV-Hop and CSO for wireless sensor networks. IEEE Access 9:380–399

    Article  Google Scholar 

  31. Shi Q, Wu C, Xu Q, Zhang J (2021) Optimization for DV-Hop type of localization scheme in wireless sensor networks. J Supercomput 77(12):13629–13652

    Article  Google Scholar 

  32. Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Ind Eng 145:106559–106581

    Article  Google Scholar 

  33. Yu XW, Huang LP, Liu Y, Zhang K, Li P, Li Y (2022) WSN node location based on beetle antennae search to improve the gray wolf algorithm. Wireless Netw 28(2):539–549

    Article  Google Scholar 

  34. Zhang J, Wang JS (2020) Improved salp swarm algorithm based on levy flight and sine cosine operator. IEEE Access 8:99740–99771

    Article  Google Scholar 

  35. Yang X, Liu J, Liu Y, Xu P, Yu L, Zhu L, Deng W (2021) A novel adaptive sparrow search algorithm based on chaotic mapping and t-distribution mutation. Appl Sci 11(23):11192–11213

    Article  Google Scholar 

Download references

Funding

This work was in part supported by Hunan Provincial Natural Science Foundation of China (2024JJ5338); National Natural Science Foundation of China (No.11875164); University of South China Postdoctoral Research star-up Fund(230XQD053).

Author information

Authors and Affiliations

  1. School of Resource & Environment and Safety Engineering, University of South China, Hengyang, 421001, China

    Xiuwu Yu, Wei Peng, Zixiang Zhou, Ke Zhang & Yong Liu

Authors
  1. Xiuwu Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wei Peng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zixiang Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ke Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Xiuwu Yu and Wei Peng wrote the main part of the paper. Zixiang Zhou, Ke Zhang and Yong Liu checked the paper.

Corresponding author

Correspondence to Wei Peng.

Ethics declarations

Ethics approval

The research adhered to all applicable laws and regulations governing online research, data privacy, and human subject protection.

Consent to publish

We agree to publish.

Competing interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

This article is part of the Topical Collection: 1- Track on Networking and Applications

Guest Editor: Vojislav B. Misic

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

Yu, X., Peng, W., Zhou, Z. et al. An IMOA DV-Hop localization algorithm in WSN based on hop count and hop distance correction. Peer-to-Peer Netw. Appl. 17, 2637–2650 (2024). https://doi.org/10.1007/s12083-024-01710-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-024-01710-1

Keywords