Skip to main content
SpringerLink
Log in

DV-Hop based localization algorithm using node negotiation and multiple communication radii for wireless sensor network

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Nodes localization has been a critical subject in wireless sensor network (WSN) field. As far as existing localization algorithms are concerned, distance vector hop (DV-Hop) has the advantages of no extra hardware and implementation simplicity, however its localization accuracy cannot meet some specific requirements. In order to enhance the accuracy of WSN nodes localization, a DV-Hop based localization algorithm based on nodes negotiation and multi communication radii (NNMCR DV-Hop) is proposed in this paper. Firstly, the hop counts between WSN nodes is modified from an integer to a decimal by changing communication radius of anchor nodes through nodes negotiation. By refining the hop counts, the accuracy of the estimated distance from the unknown to the anchor nodes is improved. Secondly, the calculation of the average hop size of the anchor node is abstracted into a combinatorial optimization problem which is solved by using binary particle swarm optimization (BPSO) to improve the accuracy of the estimated distance which is between the anchor and the unknown node. Finally, when calculating the coordinates of unknown nodes, only the anchor nodes with smaller hop counts are selected to participate in the calculation. Simulation experiments show that compared with the original DV-Hop as well as other improved algorithms based on DV-Hop, NNMCR DV-Hop greatly improves the localization accuracy of unknown nodes without additional hardware.

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

Similar content being viewed by others

Data availability

The data used to support the findings of this study are included within the article.

References

  1. Shafique, K., Khawaja, B. A., Sabir, F., Qazi, S., & Mustaqim, M. (2020). Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access, 8, 23022–23040.

    Article  Google Scholar 

  2. Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020). A survey on the internet of things (IoT) forensics: Challenges, approaches, and open issues. IEEE Communications Surveys & Tutorials, 22(2), 1191–1221.

    Article  Google Scholar 

  3. Amutha, J., Sharma, S., & Nagar, J. (2020). WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: Review, approaches and open issues. Wireless Personal Communications, 111(2), 1089–1115.

    Article  Google Scholar 

  4. Haseeb, K., Ud Din, I., Almogren, A., & Islam, N. (2020). An energy efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20(7), 2081.

    Article  Google Scholar 

  5. Nassar, J., Miranda, K., Gouvy, N., & Mitton, N. (2018). Heterogeneous data reduction in WSN: Application to Smart Grids. In: Proceedings of the 4th ACM MobiHoc Workshop on Experiences with the Design and Implementation of Smart Objects (pp. 1–6).

  6. Sharma, N., & Bhatt, R. (2018). Privacy preservation in WSN for healthcare application. Procedia Computer Science, 132, 1243–1252.

    Article  Google Scholar 

  7. Moorthy, R., Bangera, V., Amrin, Z., Avinash, N. J., & NS, K. R. (2020, October). WSN in Defence Field: A Security Overview. In: 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 258–264). IEEE.

  8. Martínez, S. H., Salcedo, P. O. J., & Daza, B. S. R. (2017, May). IoT application of WSN on 5G infrastructure. In: 2017 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1–6). IEEE.

  9. Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192–219.

    Article  Google Scholar 

  10. Kanwar, V., & Kumar, A. (2021). DV-Hop-based range-free localization algorithm for wireless sensor network using runner-root optimization. The Journal of Supercomputing, 77(3), 3044–3061.

    Article  Google Scholar 

  11. Kanwar, V., & Kumar, A. (2021). DV-Hop localization methods for displaced sensor nodes in wireless sensor network using PSO. Wireless Networks, 27(1), 91–102.

    Article  Google Scholar 

  12. Shahzad, F., Sheltami, T. R., & Shakshuki, E. M. (2016). Multi-objective optimization for a reliable localization scheme in wireless sensor networks. Journal of Communications and Networks, 18(5), 796–805.

    Article  Google Scholar 

  13. Kagi, S., & Mathapati, B. S. (2021). Localization in Wireless Sensor Networks: A Compact Review on State-of-the-Art models. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT) (pp. 5–12). IEEE.

  14. Yang, Y., Mao, Y., & Sun, B. (2020). Basic performance and future developments of BeiDou global navigation satellite system. Satellite Navigation, 1(1), 1–8.

    Article  Google Scholar 

  15. Cui, L., Xu, C., Li, G., Ming, Z., Feng, Y., & Lu, N. (2018). A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network. Applied Soft Computing, 68, 39–52.

    Article  Google Scholar 

  16. Park, J. W., Park, D. H., & Lee, C. (2013). Angle and ranging based localization method for ad hoc network. The Journal of Supercomputing, 64(2), 507–521.

    Article  Google Scholar 

  17. Shalaby, M., Shokair, M., & Messiha, N. W. (2017). Performance enhancement of TOA localized wireless sensor networks. Wireless Personal Communications, 95(4), 4667–4679.

    Article  Google Scholar 

  18. Oguejiofor, O. S., Aniedu, A. N., Ejiofor, H. C., & Okolibe, A. U. (2013). Trilateration based localization algorithm for wireless sensor network. International Journal of Science and Modern Engineering (IJISME), 1(10), 2319–6386.

    Google Scholar 

  19. Kumari, J., Kumar, P., & Singh, S. K. (2019). Localization in three-dimensional wireless sensor networks: A survey. The Journal of Supercomputing, 75(8), 5040–5083.

    Article  Google Scholar 

  20. Xiao, J., Ren, L., & Tan, J. (2006). Research of TDOA based self-localization approach in wireless sensor network. In: 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 2035–2040). IEEE.

  21. Čapkun, S., Hamdi, M., & Hubaux, J. P. (2002). GPS-free positioning in mobile ad hoc networks. Cluster Computing, 5(2), 157–167.

    Article  Google Scholar 

  22. Niculescu, D., & Nath, B. (2003). Ad hoc positioning system (APS) using AOA. In: IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428) (Vol. 3, pp. 1734-1743). IEEE.

  23. Doherty, L., & El Ghaoui, L. (2001). Convex position estimation in wireless sensor networks. In: Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No. 01CH37213) (Vol. 3, pp. 1655–1663). IEEE.

  24. Cheikhrouhou, O., Bhatti, G. M., & Alroobaea, R. (2018). A hybrid DV-hop algorithm using RSSI for localization in large-scale wireless sensor networks. Sensors, 18(5), 1469. https://doi.org/10.3390/s18051469

    Article  Google Scholar 

  25. Shen, S., Yang, B., Qian, K., She, Y., & Wang, W. (2019). On improved DV-Hop localization algorithm for accurate node localization in wireless sensor networks. Chinese Journal of Electronics, 28(3), 658–666.

    Article  Google Scholar 

  26. Wang, P., Xue, F., Li, H., Cui, Z., Xie, L., & Chen, J. (2019). A multi-objective DV-Hop localization algorithm based on NSGA-II in internet of things. Mathematics, 7(2), 184.

    Article  Google Scholar 

  27. Messous, S., Liouane, H., & Liouane, N. (2020). Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks. Telecommunication Systems, 73(1), 75–86.

    Article  Google Scholar 

  28. Shi, Q., Xu, Q., & Zhang, J. (2019). An improved DV-Hop scheme based on path matching and particle swarm optimization algorithm. Wireless Personal Communications, 104(4), 1301–1320.

    Article  Google Scholar 

  29. Liu, Y., Chen, J., & Xu, Z. (2017). Improved DV-hop localization algorithm based on bat algorithm in wireless sensor networks. KSII Transactions on Internet and Information Systems (TIIS), 11(1), 215–236.

    Google Scholar 

  30. Han, D., Yu, Y., Li, K. C., & de Mello, R. F. (2020). Enhancing the sensor node localization algorithm based on improved DV-hop and DE algorithms in wireless sensor networks. Sensors, 20(2), 343.

    Article  Google Scholar 

  31. Kaur, A., Kumar, P., & Gupta, G. P. (2020). Improving DV-Hop-based localization algorithms in wireless sensor networks by considering only closest anchors. International Journal of Information Security and Privacy (IJISP), 14(1), 1–15.

    Article  Google Scholar 

  32. 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 

  33. Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470–1477). IEEE.

  34. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Article  Google Scholar 

  35. Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85.

    Article  Google Scholar 

  36. Mirjalili, S. (2019). Genetic algorithm. In S. Mirjalili (Ed.), Evolutionary algorithms and neural networks (pp. 43–55). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-93025-1_4

    Chapter  MATH  Google Scholar 

  37. Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: a novel approach for global engineering optimization. Engineering Computations, 29, 464–483.

    Article  Google Scholar 

  38. Yang, X. S., & He, X. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-inspired computation, 5(3), 141–149.

    Article  Google Scholar 

  39. Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation (Vol. 5, pp. 4104–4108). IEEE.

  40. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

  41. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences (pp. 10). IEEE.

Download references

Acknowledgments

This work was supported by the Research Fund of Nanjing Institute of Technology under Grant CKJB202002.

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, 211167, Jiangsu, China

    Yuxiao Cao & Ying Qian

  2. School of Information Science and Technology, Huizhou University, Huizhou, 516007, Guangdong, China

    Zhen Wang

Authors
  1. Yuxiao Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts 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

Cao, Y., Qian, Y. & Wang, Z. DV-Hop based localization algorithm using node negotiation and multiple communication radii for wireless sensor network. Wireless Netw 29, 3493–3513 (2023). https://doi.org/10.1007/s11276-023-03417-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03417-2

Keywords

Search

Navigation