Skip to main content

Advertisement

Log in

A novel predictive localization algorithm for underwater wireless sensor networks

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

Abstract

The location of nodes is critical in underwater wireless sensor networks (UWSNs), which is an ocean monitoring platform. UWSNs are motivated by the popular usage of localization and play a major role in several technologies that depend primarily on innovations and localization of these nodes. Underwater node localization is a critical technology that enables the deployment of a variety of underwater applications. In this study, the underwater nodes are divided into two levels. Firstly, a clock asynchronous localization system (LS-AC) for base layer’s node localization is presented. In order to eradicate the original ranging strategy's dependence on active nodes and address the problem of energy consumption, LS-AC performs in-network situation-based monitoring by relying on asynchronous clocks. Secondly, we propose a backtracking search algorithm (OTKL-BSA) based on optimal topology and knowledge learning. It is used to address the issues associated with traditional algorithms' lack of diversity and the imbalance between exploration and exploitation. Thirdly, to solve the problems that the traditional gray wolf optimizer (GWO) is prone to falling into local optimal values and has a low search efficiency, this paper proposes a GWO scheme based on hunting step size (GWO-HSS). Finally, simulation results show that the proposed algorithm outperforms SLMP, MCL-MP, MP-PSO, and MGP in aspects of localization performance.

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

Access this article

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

Similar content being viewed by others

References

  1. Amirthavalli, R., Ramya, S. T., & Shanker, N. R. (2022). Modified Mackenzie equation and CVOA algorithm reduce delay in UASN. Computer Systems Science and Engineering, 41(2), 829–847. https://doi.org/10.32604/csse.2022.020307

    Article  Google Scholar 

  2. Arul, R., Alroobaea, R., Mechti, S., Rubaiee, S., Andejany, M., Tariq, U., et al. (2021). Intelligent data analytics in energy optimization for the internet of underwater things. Soft Computing, 25(18), 12507–12519. https://doi.org/10.1007/s00500-021-06002-x

    Article  Google Scholar 

  3. Bhuvaneswari, P. T. V., Karthikeyan, S., Jeeva, B., & Prasath, M. A. (2012). An efficient mobility based localization in underwater sensor networks. In 2012 fourth international conference on computational intelligence and communication networks, 3–5 November 2012 (pp. 90–94).

  4. Chen, D., Zou, F., Lu, R., & Li, S. (2018). Backtracking search optimization algorithm based on knowledge learning. Information Sciences. https://doi.org/10.1016/j.ins.2018.09.039

    Article  Google Scholar 

  5. Erlich, I., Rueda, J. L., Wildenhues, S., & Shewarega, F. (2014). Solving the IEEE-CEC 2014 expensive optimization test problems by using single-particle MVMO. In: 2014 IEEE congress on evolutionary computation (CEC), 6–11 July 2014 (pp. 1084–1091).

  6. Gola, K. K., & Gupta, B. (2022). Void node avoidance in underwater acoustic sensor network using black widow optimization algorithm. Ad Hoc & Sensor Wireless Networks, 52(1–2), 45–71.

    Google Scholar 

  7. Gong, Z., Li, C., & Jiang, F. (2020). A machine learning-based approach for auto-detection and localization of targets in underwater acoustic array networks. IEEE Transactions on Vehicular Technology, 69(12), 15857–15866. https://doi.org/10.1109/TVT.2020.3036350

    Article  Google Scholar 

  8. Goronzy, S. (2002). Robust adaptation to non-native accents in automatic speech recognition. Berlin: Springer.

    Book  MATH  Google Scholar 

  9. Hemalatha, R., Prakash, R., & Sivapragash, C. (2020). Analysis of energy consumption in smart grid WSN using path operator calculus centrality-based HSA-PSO algorithm. Soft Computing, 24(14), 10771–10783. https://doi.org/10.1007/s00500-019-04580-5

    Article  Google Scholar 

  10. Humphries, N. E., Queiroz, N., Dyer, J. R. M., Pade, N. G., Musyl, M. K., Schaefer, K. M., et al. (2010). Environmental context explains Levy and Brownian movement patterns of marine predators. Nature, 465(7301), 1066–1069. https://doi.org/10.1038/nature09116

    Article  Google Scholar 

  11. Jensi, R., & Jiji, G. W. (2016). An enhanced particle swarm optimization with levy flight for global optimization. Applied Soft Computing, 43, 248–261. https://doi.org/10.1016/j.asoc.2016.02.018

    Article  Google Scholar 

  12. Kim, S., & Yoo, Y. (2013). High-precision and practical localization using seawater movement pattern and filters in underwater wireless networks. In 2013 IEEE 16th international conference on computational science and engineering, 3–5 December 2013 (pp. 374–381).

  13. Kumari, S., Mishra, P. K., & Anand, V. (2021). Fault-resilient localization using fuzzy logic and NSGA II-based metaheuristic scheme for UWSNs. Soft Computing, 25(17), 11603–11619. https://doi.org/10.1007/s00500-021-05975-z

    Article  Google Scholar 

  14. Li, J., Li, S., Li, B., & Liu, B. (2022). Germ integrity detection for rice using a combination of germ color image features and deep learning. Soft Computing. https://doi.org/10.1007/s00500-022-06902-6

    Article  Google Scholar 

  15. Li, S., Li, B., Li, J., & Bin, L. (2022). Brown rice germ integrity identification based on deep learning network. Journal of Food Quality. https://doi.org/10.1155/2022/6709787

    Article  Google Scholar 

  16. Li, X., & Deb, K. (2010). Comparing best PSO niching algorithms using different position update rules.

  17. Yan, J., Zhang, X., Luo, X., Wang, Y., Chen, C., & Guan, X. (2017). Asynchronous localization with mobility prediction for underwater acoustic sensor networks. IEEE Transactions on Vehicular Technology, 67(3), 2543–2556.

    Article  Google Scholar 

  18. Ye, Y., Ngo, H. H., Guo, W., Liu, Y., Chang, S. W., Nguyen, D. D., Liang, H., & Wang, J. (2018). A critical review on ammonium recovery from wastewater for sustainable wastewater management. Bioresource Technology, 268, 749–758.

    Article  Google Scholar 

  19. Zhang, W., Han, G., Wang, X., Guizani, M., Fan, K., & Shu, L. (2020). A node location algorithm based on node movement prediction in underwater acoustic sensor networks. IEEE Transactions on Vehicular Technology, 69(3), 3166–3178.

    Article  Google Scholar 

  20. Hao, K., Yu, K., Gong, Z., Du, X., Liu, Y., & Zhao, L. (2020). An enhanced AUV-aided TDoA localization algorithm for underwater acoustic sensor networks. Mobile Networks and Applications, 25(5), 1673–1682.

    Article  Google Scholar 

  21. Liu, H., Xu, B., & Liu, B. (2022). An automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network. Journal of Marine Science and Engineering, 10(2), 283.

    Article  Google Scholar 

  22. Artemi, M., & Liu, H. (2020). Image optimization using improved gray-scale quantization for content-based image retrieval. In 2020 IEEE 6th international conference on optimization and applications (ICOA) (pp. 1–6). IEEE.

  23. Raja, M. (2021). PRAVN: A perspective on road safety adopted routing protocol for hybrid VANET-WSN communication using balanced clustering and optimal neighborhood selection. Soft Computing, 25(5), 4053–4072. https://doi.org/10.1007/s00500-020-05432-3

    Article  Google Scholar 

  24. Sims, D. W., Southall, E. J., Humphries, N. E., Hays, G. C., Bradshaw, C. J. A., Pitchford, J. W., et al. (2008). Scaling laws of marine predator search behavior. Nature, 451(7182), 1098-U5. https://doi.org/10.1038/nature06518

    Article  Google Scholar 

  25. Usharani, B. (2022). ILF-LSTM: Enhanced loss function in LSTM to predict the sea surface temperature. Soft Computing. https://doi.org/10.1007/s00500-022-06899-y

    Article  Google Scholar 

  26. Yan, J., Zhang, X., Luo, X., Wang, Y., Chen, C., & Guan, X. (2018). Asynchronous localization with mobility prediction for underwater acoustic sensor networks. IEEE Transactions on Vehicular Technology, 67(3), 2543–2556. https://doi.org/10.1109/TVT.2017.2764265

    Article  Google Scholar 

  27. Yongheng, W., Jucheng, Z., Yunfeng, H., Cuie, Z., & Dajun, S. (2016). Underwater node localization using range based multilateral accumulation method (RBMAM) and least square method (LSM).In: OCEANS 2016 MTS/IEEE Monterey, 19–23 September 2016 (pp. 1–4).

  28. Zadeh, S. M., Powers, D. M. W., Sammut, K., & Yazdani, A. M. (2018). A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment. Soft Computing, 22(5), 1687–1710. https://doi.org/10.1007/s00500-016-2433-2

    Article  Google Scholar 

  29. Zhang, K., Qiujun, H., & Zhang, Y. (2018). Enhancing comprehensive learning particle swarm optimization with local optima topology. Information Sciences. https://doi.org/10.1016/j.ins.2018.08.049

    Article  Google Scholar 

  30. Zhang, Q., Liu, M., & Zhang, S. (2015). Node topology effect on target tracking based on UWSNs using quantized measurements. IEEE Transactions on Cybernetics, 45(10), 2323–2335. https://doi.org/10.1109/TCYB.2014.2371232

    Article  Google Scholar 

  31. Zhang, Q., Zhang, C., Liu, M., & Zhang, S. (2014). Local node selection for target tracking based on underwater wireless sensor networks. International Journal of Systems Science, 46, 1–10. https://doi.org/10.1080/00207721.2014.880199

    Article  MATH  Google Scholar 

  32. Zhang, Y., Wang, M., Liang, J., Zhang, H., Chen, W., & Jiang, S. (2017). Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm. Soft Computing, 21(20), 6019–6029. https://doi.org/10.1007/s00500-017-2667-7

    Article  Google Scholar 

  33. Zhou, Z., Peng, Z., Cui, J.-H., Shi, Z., & Bagtzoglou, A. C. (2011). Scalable localization with mobility prediction for underwater sensor networks. IEEE Transactions on Mobile Computing, 10(3), 335–348. https://doi.org/10.1109/tmc.2010.158

    Article  Google Scholar 

  34. Zhu, G., Jiang, R., Xie, L., & Chen, Y. (2014). A distributed localization scheme based on mobility prediction for underwater wireless sensor networks. In: The 26th Chinese control and decision conference (2014 CCDC), 31 May–2 June 2014 (pp. 4863–4867).

Download references

Funding

Foundation of the China State Key Laboratory of Ocean Engineering, No. 1616. Natural Science Foundation of Heilongjiang Province, No. 2018009.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bo Xu.

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 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

Liu, H., Xu, B. & Liu, B. A novel predictive localization algorithm for underwater wireless sensor networks. Wireless Netw 29, 303–319 (2023). https://doi.org/10.1007/s11276-022-03107-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-03107-5

Keywords

Navigation