Skip to main content
SpringerLink
Log in

Novel Whale Cooperative Positioning Algorithm for UWB Sensor Networks

  • Research
  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

UWB positioning can be modeled as an optimization problem for a fitness function, which is resolved through the conventional whale optimization algorithm. The fitness function used in the conventional whale positioning algorithm does not use the range measurement data between labels, and the range measurement data is incomplete, which leads to a limited improvement in its positioning accuracy. Consequently, a novel whale cooperative positioning algorithm that can increase positioning accuracy is presented in this paper. First, a fitness function is established during the positioning process that contains the range measurement data between the labels to improve positioning accuracy. Then, the conventional whale positioning method provides a good initial position for the labels, enabling the proposed algorithm to quickly search for the optimal position. Finally, Levy flight mode is adopted to prevent the positioning result from converging to the local optimal solution. According to simulation results, the proposed algorithm can provide better positioning accuracy than the conventional whale positioning algorithm while realizing simultaneous positioning of all the labels.

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

Algorithm 1
Fig. 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Availability of data and material

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  1. Wen, R., Schoof, E., & Chapman, A. (2022). Clock rigidity and joint position-clock estimation in ultra-wideband sensor networks. IEEE Transactions on Control of Network Systems, 10, 1209–1221.

    Article  Google Scholar 

  2. Wang, J., Yang, J., & Wang, M. (2021) Device-free localization and tracking of multiple person in UWB sensor networks. In International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence, pp. 1–7.

  3. Josifovic, K. M., Vujicic, V. L., Misailovic, S. D., & Markovic, G. B. (2020). Bilateration-based localization for UWB-based wireless sensor networks. Telecommunications Forum. https://doi.org/10.1109/TELFOR51502.2020.9306627

    Article  Google Scholar 

  4. V, V., & Guruprakash, C. D. (2022) A review on smart agricultural applications: crop yield and plant disease prediction. In International Conference on Applied Artificial Intelligence and Computing, pp. 885–892.

  5. Elsanhoury, M., et al. (2022). Precision positioning for smart logistics using Ultra-wideband technology-based indoor navigation: a review. IEEE Access, 10, 44413–44445.

    Article  Google Scholar 

  6. Schmidt, J. F., Chernov, D., & Bettstetter, C. (2020). Towards industrial Ultra-wideband networks: experiments for machine vibration monitoring. IEEE Access, 8, 42576–42583.

    Article  Google Scholar 

  7. Maali, A., Baudoin, G., & Mesloub, A. (2017) Improved energy detection receiver for ranging in IEEE 802.15.4a standard. In European Conference on Antennas and Propagation, pp. 1860–1863.

  8. Li, Y., Maorong, J., Qiang, L., Guili, Y., & Kai, D. (2018) Research on the IR-UWB ranging algorithm in outdoor Near-ground environment. In International Conference on Sensor Networks and Signal Processing, pp. 504–507.

  9. Liu, Q., Wu, Z., Yin, Z., Wu, M., & Ma, B. (2020) A novel multiuser UWB ranging scheme in NLOS indoor environment. In International Conference on Telecommunications, pp. 1–5.

  10. Sobron, I., Landa, I., Eizmendi, I., & Velez, M. (2020) Adaptive TOA estimation with imperfect LOS and NLOS knowledge in UWB positioning systems. IEEE Sensors, pp. 1–4.

  11. Fenghao, L., Jun, S., ZhiWei, Y., XiaoXiao, S., & Feng, C. (2019) A wireless sensor network location algorithm based on whale algorithm. In IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

  12. Lakshmishree, K. P., & Nirmala, M. B. (2018) Localization in wireless sensor networks with optimization and security schemes-a review. In International Conference on Intelligent Computing and Control Systems.

  13. Chen, Y., Shen, C., Zhang, K., Xu, L., & Huang, X. (2020) TDOA positioning method based on taylor series expansion based on cuckoo search algorithm. In International Conference on Virtual Reality and Intelligent Systems, pp. 986–989.

  14. Li, N., Shen, C., Zhang, K., & Huang, X. (2019) The TDOA algorithm based on BP neural network optimized by cuckoo search. In International Conference on Robots Intelligent System, pp. 539–542.

  15. Cheng, J., & Xia, L. (2016). An effective cuckoo search algorithm for node positioning in wireless sensor network. Sensors (Switzerland), 16(9), 1–17.

    Article  Google Scholar 

  16. Fenghao, L., Jun, S., Chunzhi, W., Yucheng, Z., Ming, W., & Xianjing, Z. (2021) An indoor localization method based on cauchy inverse whale optimization algorithm. In IEEE International Conference on Computer Science & Education, pp. 617–621.

  17. Shakila, R., & Paramasivan, B. (2021). An improved range based localization using whale optimization algorithm in underwater wireless sensor network. Journal of Ambient Intelligence and Humanized Computing, 12, 6479–6489.

    Article  Google Scholar 

  18. Xuezhen, C., Shanpei, W., Xiaoqing, L., Xingjun, L., Kun, C., & Jiming, L. (2020) DV-Hop underground positioning algorithm based on parameter disturbance WOA. In Chinese Automation Congress, pp. 3187–3192.

  19. Pingzhang, G., Bo, H., & Zhaoyang, Y. (2021) A node location algorithm based on improved whale optimization in wireless sensor networks. In Wireless Communications and Mobile Computing, pp. 1–17.

  20. Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., & Tuba, M. (2021). Whale optimization algorithm with exploratory move for wireless sensor networks localization. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-49336-3_33

    Article  Google Scholar 

  21. Zhao, C., & Wang, B. (2019) A UWB/Bluetooth fusion algorithm for indoor localization. In Chinese Control Conference, pp. 4142–4146.

  22. Monica, S., & Ferrari, G. (2014) Accurate indoor localization with UWB wireless sensor networks. In IEEE International WETICE Conference, pp. 287–289.

  23. Qin, X., et al. (2021). An improved Cuckoo search localization algorithm for UWB sensor networks. Wireless Netwoks, 27, 527–535.

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China (62001272).

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Shandong, 255000, China

    Bin Xia

  2. School of Computer Science and Technology, Shandong University of Technology, Shandong, 255000, China

    Nan Xie & Liye Zhang

Authors
  1. Bin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nan Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liye Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BX wrote the main manuscript text. NX and LZ revised the manuscript.

Corresponding author

Correspondence to Bin Xia.

Ethics declarations

Conflict of interest

The authors declared that they have 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

Xia, B., Xie, N. & Zhang, L. Novel Whale Cooperative Positioning Algorithm for UWB Sensor Networks. Wireless Pers Commun 135, 2421–2438 (2024). https://doi.org/10.1007/s11277-024-11175-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-11175-3

Keywords

Search

Navigation