Skip to main content
SpringerLink
Log in

Improved Generalized Regression Neural Network for Target Localization

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Knowledge of location is of utmost importance in many indoor Location-Based Services (LBS). Although traditional technique such as trilateration involving the use of received signal strengths (RSS’s) is quite popular and simple to use for wireless sensor network (WSN) based target localization, the location estimates obtained using it are not accurate and reliable. The reason behind this is the highly fluctuating nature of RSS’s due to dynamic RF environment and non-linear system dynamics. If the dataset is sparse, the concept of centroid is very useful to estimate fairly closer approximation to the underlying relationship in the given dataset. The GRNN architecture is well known for mapping any nonlinear relationship between input and output. To address the problems with the RSS based target localization and tracking (L&T) using WSN for indoor environment, a novel range free Centroid Generalized Regression Neural Network (C-GRNN) algorithm is presented in this paper. The proposed C-GRNN algorithm is formed by combining the advantages of both centroid and GRNN. In order to realize the dynamicity in given RF environment, the variance in the RSSI measurements is varied from 3 to 6 dBm. During simulation experiments, although the variance in the RSSI measurements is doubled, the average RMSE and average localization error are increased by only approximately 28.31%, and 22.28% respectively. This rise in localization errors with the proposed C-GRNN architecture is very less as compared to the trilateration as well as GRNN based technique.

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

Similar content being viewed by others

References

  1. Viani, F., Rocca, P., Oliveri, G., Trinchero, D., & Massa, A. (2011). Localization, tracking, and imaging of targets in wireless sensor networks: An invited review. Radio Science. https://doi.org/10.1029/2010RS004561

    Article  Google Scholar 

  2. Jondhale, S. R., Deshpande, R. S., Walke, S. M., & Jondhale, A. S. (2016). Issues and challenges in RSSI based target localization and tracking in wireless sensor networks, doi: https://doi.org/10.1109/ICACDOT.2016.7877655.

  3. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computing Networks. https://doi.org/10.1016/S1389-1286(01)00302-4

    Article  Google Scholar 

  4. Payal, A., Rai, C. S., & Reddy, B. V. R. (2015). Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks. Wireless Personal Communications. https://doi.org/10.1007/s11277-015-2362-x

    Article  Google Scholar 

  5. Jondhale, S. R., Shubair, R., Labade, R. P., Lloret, J., & Gunjal, P. R. (2020). Application of supervised learning approach for target localization in wireless sensor. Network, 1132, 493–519.

    Google Scholar 

  6. Bouchard, K., Fortin-Simard, D., Gaboury, S., Bouchard, B., & Bouzouane, A. (2014). Accurate trilateration for passive RFID localization in smart homes. International Journal of Wireless Information Networks, 21(1), 32–47. https://doi.org/10.1007/s10776-013-0234-4

    Article  Google Scholar 

  7. Higgins, M. B. (1999). Heighting with GPS: Possibilities and limitations. The Proceedings of: Geodesy and Surveying in the Future: The Importance of Heights, Jubilee Seminar, 25, 15–17.

    Google Scholar 

  8. Tariq, Z. B., Cheema, D. M., Kamran, M. Z., & Naqvi, I. H. (2017). Non-GPS positioning systems: A survey. ACM Computing Surveys (CSUR), 50(4), 1–34. https://doi.org/10.1145/3098207

    Article  Google Scholar 

  9. Jondhale, S. R., & Deshpande, R. S. (2018). Modified Kalman filtering framework based real time target tracking against environmental dynamicity inwireless sensor networks. Adhoc & Sensor Wireless Networks, 40.

  10. Jondhale, S. R., & Deshpande, R. S. (2018). Tracking target with constant acceleration motion using Kalman filtering, doi: https://doi.org/10.1109/ICACCT.2018.8529628.

  11. Jondhale, S. R., & Deshpande, R. S. (2019). Self recurrent neural network based target tracking in wireless sensor network using state observer. International Journal of Sensors Wireless Communications and Control, 9(2), 165–178. https://doi.org/10.2174/2210327908666181029103202

    Article  Google Scholar 

  12. Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine. https://doi.org/10.1109/MSP.2005.1458287

    Article  Google Scholar 

  13. Weerasinghe, Y. P., & Dissanayake, M. B. (2019). Towards IoT; Comparison of RSS based indoor localization using supervised learning and trilateration in WSN, doi: https://doi.org/10.1109/ICIIS47346.2019.9063297.

  14. Yang, Z., Liu, Y., & Li, X. Y. (2010). Beyond trilateration: On the localizability of wireless ad hoc networks. IEEE/ACM Trans. Netw. https://doi.org/10.1109/TNET.2010.2049578

    Article  Google Scholar 

  15. Uren, J., & Price, W. F. (1985). Triangulation and trilateration. Surveying for engineers (pp. 163–187). Palgrave.

    Google Scholar 

  16. Jondhale, S. R., & Deshpande, R. S. (2019). Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2018.2873357

    Article  Google Scholar 

  17. Jondhale, S. R., & Deshpande, R. S. (2019). Efficient localization of target in large scale farmland using generalized regression neural network. International Journal of Communication Systems, 32(16), e4120. https://doi.org/10.1002/dac.4120

    Article  Google Scholar 

  18. Jondhale, S. R., & Deshpande, R. S. (2019). GRNN and KF framework based real time target tracking using PSOC BLE and smartphone. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2018.09.017

    Article  Google Scholar 

  19. Phoemphon, S., So-In, C., & Leelathakul, N. (2018). Fuzzy weighted centroid localization with virtual node approximation in wireless sensor networks. IEEE Internet Things Journal, 5(6), 4728–4752. https://doi.org/10.1109/JIOT.2018.2811741

    Article  Google Scholar 

  20. Kaur, A., Kumar, P., & Gupta, G. P. (2019). A weighted centroid localization algorithm for randomly deployed wireless sensor networks. Journal of King Saud University Computer and Information Sciences, 31(1), 82–91. https://doi.org/10.1016/j.jksuci.2017.01.007

    Article  Google Scholar 

  21. Magowe, K., Giorgetti, A., & Sithamparanathan, K. (2019). Closed-form approximation of weighted centroid localization performance. IEEE Sensors Letters, 3(12), 1–4. https://doi.org/10.1109/LSENS.2019.2948585

    Article  Google Scholar 

  22. Magowe, K., Giorgetti, A., Kandeepan, S., & Yu, X. (2019). Accurate analysis of weighted centroid localization. IEEE Transactions on Cognitive Communications and Networking, 5(1), 153–164. https://doi.org/10.1109/TCCN.2018.2874452

    Article  Google Scholar 

  23. Zhang, Z., Zhang, C., Li, M., & Xie, T. (2020). Target positioning based on particle centroid drift in large-scale WSNs. IEEE Access, 8, 122709. https://doi.org/10.1109/ACCESS.2020.3008373

    Article  Google Scholar 

  24. Haykin, S. (2009). Neural networks and learning machines, Third Edition. Upper Saddle River, NJ: Pearson Education.

  25. Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.97934

    Article  Google Scholar 

  26. Jann, B. (2013). COEFPLOT: Stata module to plot regression coefficients and other results. Statistical Software Components.

Download references

Author information

Authors and Affiliations

  1. E&TC Engineering Department, Amrutvahini COE, Sangamner, Maharashtra, India

    Satish R. Jondhale

  2. Computer Engineering Department, Amrutvahini COE, Sangamner, Maharashtra, India

    Manoj A. Wakchaure

  3. E&TC Department, Sanjivani COE, Kopargaon, Maharashtra, India

    Balasaheb S. Agarkar

  4. Computer Engineering Department, PREC, Loni, Maharashtra, India

    Sagar B. Tambe

Authors
  1. Satish R. Jondhale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj A. Wakchaure
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Balasaheb S. Agarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sagar B. Tambe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satish R. Jondhale.

Ethics declarations

Conflict of interest

On behalf of all the authors, it is declared that the work has not been published and is not being considered for publication elsewhere. The authors also declare that there is no any conflict of interest involved. We also declare that this research work is not funded by any agency, and the publisher will not be held legally responsible for any kind claim for compensation. We are also ready to adhere to data transparency as well as code availability.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jondhale, S.R., Wakchaure, M.A., Agarkar, B.S. et al. Improved Generalized Regression Neural Network for Target Localization. Wireless Pers Commun 125, 1677–1693 (2022). https://doi.org/10.1007/s11277-022-09627-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09627-9

Keywords

Search

Navigation