Skip to main content
Springer Nature Link
Log in

iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, we propose an iterative Hessian regularization technique for node localization in wireless sensor network (WSN). The technique is based on the observation that received signal strength indicator (RSSI)-based node localization problem using a few location-aware anchor nodes, and a large number of location-unaware non-anchor nodes can be modeled as a manifold regularization-based regression problem. A signal transmitted from a node attenuates with distance. However, this attenuation is not uniform due to noise and other detrimental channel conditions. This apparently embeds the nodes in an unknown high-dimensional space. The proposed technique assumes that these nodes are the data points lying on a high-dimensional manifold and the locations of these nodes can be obtained accurately through an iterative Hessian regularized regression. In Hessian regularization, temporary location values are assigned to each non-anchor node, which are further refined in the subsequent iterations. This is followed by localized Procrustes analysis to offset the affine transformation on temporary location values. To validate the proposed technique, we deployed TelosB motes to form a WSN. The beacons broadcast from nodes were used for finding the RSSI distance estimates followed by the localization process. We observed that our proposed technique is able to localize the sensor nodes accurately and outperformed the baseline supervised learning, Hessian and iterative Laplacian regularization methods with an increase in accuracy of around \(70\%\) over the baseline supervised method.

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

Similar content being viewed by others

Notes

  1. \(d_{max}\) is maximum communication range of the underlying WSN nodes.

  2. http://tinyos.stanford.edu/tinyos-wiki/index.php/Main_Page.

  3. https://github.com/gitr00ki3/telosBRSSi.

  4. Here, \(P_{r_{i}}\) is the power received at \(i^{th}\) distance denoted by \(d_{i}\).

References

  1. Kumari J, Kumar P, Singh SK (2019) Localization in three-dimensional wireless sensor networks: a survey. J Supercomput 75(8):5040–5083

    Article  Google Scholar 

  2. Jain N, Verma S, Kumar M (2018) Patch-based lle with selective neighborhood for node localization. IEEE Sens J 18(9):3891–3899

    Article  Google Scholar 

  3. Kashniyal J, Verma S, Singh KP (2019) A new patch and stitch algorithm for localization in wireless sensor networks. Wireless Netw 25(6):3251–3264

    Article  Google Scholar 

  4. Yin F, Zhao Y, Gunnarsson F, Gustafsson F (2017) Received-signal-strength threshold optimization using gaussian processes. IEEE Trans Signal Process 65(8):2164–2177

    Article  MathSciNet  Google Scholar 

  5. Entezami F, Tunicliffe M, Politis C (2014) Find the weakest link: Statistical analysis on wireless sensor network link-quality metrics. IEEE Veh Technol Mag 9(3):28–38

    Article  Google Scholar 

  6. Singh A, Verma S (2017) Graph laplacian regularization with procrustes analysis for sensor node localization. IEEE Sens J 17(16):5367–5376

    Article  Google Scholar 

  7. Chappelle O, Schölkopf B, Zien A (2010) Semi-supervised learning. adaptive computation and machine learning

  8. Ayodele TO (2010) Introduction to machine learning. New Advances in Machine Learning, 1–9

  9. Song Y, Nie F, Zhang C, Xiang S (2008) A unified framework for semi-supervised dimensionality reduction. Pattern Recogn 41(9):2789–2799

    Article  Google Scholar 

  10. Xiao B, Chen L, Xiao Q, Li M (2009) Reliable anchor-based sensor localization in irregular areas. IEEE Trans Mob Comput 9(1):60–72

    Article  Google Scholar 

  11. Yi S, Wheeler R, Ying Z, Fromherz Markus PJ (2004) Localization from connectivity in sensor networks. IEEE Trans. Parallel Distributed Sys 15:961–974

    Article  Google Scholar 

  12. Shang Y, Ruml W, Zhang Y, Fromherz MP (2003) Localization from mere connectivity. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking&Amp; Computing, MobiHoc ’03, 201–212

  13. Shang Y, Ruml W (2004) Improved mds-based localization. In: INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, 4, 2640–2651

  14. Costa JA, Patwari N, Hero III AO (2006) Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks (TOSN) 2(1):39–64

  15. Behera AP, Singh A, Verma S, Kumar M (2020) Manifold learning with localized procrustes analysis based wsn localization. IEEE Sens Lett 4(10):1–4

    Article  Google Scholar 

  16. Morral G, Bianchi P (2016) Distributed on-line multidimensional scaling for self-localization in wireless sensor networks. Sig Process 120:88–98

    Article  Google Scholar 

  17. Hao X (2020) Semi-supervised manifold learning based on polynomial mapping for localization in wireless sensor networks. Sig Process 172:107570

    Article  Google Scholar 

  18. El Assaf A, Zaidi S, Affes S, Kandil N (2016) Robust anns-based wsn localization in the presence of anisotropic signal attenuation. IEEE Wireless Commun Lett 5(5):504–507

    Article  Google Scholar 

  19. Zhao L, Su C, Dai Z, Huang H, Ding S, Huang X, Han Z (2019) Indoor device-free passive localization with dcnn for location-based services. The Journal of Supercomputing, 1–18

  20. Liu S, Luo H, Zou S (2009) A low-cost and accurate indoor localization algorithm using label propagation based semi-supervised learning. In: 2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks, 108–111. IEEE

  21. Jondhale SR, Deshpande RS (2018) Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sens J 19(1):224–233

    Article  Google Scholar 

  22. Keller Y, Gur Y (2011) A diffusion approach to network localization. IEEE Trans Signal Process 59(6):2642–2654

    Article  MathSciNet  Google Scholar 

  23. Guan Z, Peng J, Tan S (2013) Manifold ranking using hessian energy. Int J Softw Inf 7(3):391–405

    Google Scholar 

  24. Kim KI, Steinke F, Hein M (2009) Semi-supervised regression using hessian energy with an application to semi-supervised dimensionality reduction. In: Advances in Neural Information Processing Systems, 979–987

  25. Shi C, Ruan Q, An G, Zhao R (2014) Hessian semi-supervised sparse feature selection based on \({L}_{2, 1/2}\) - matrix norm. IEEE Trans Multimedia 17(1):16–28

    Article  Google Scholar 

  26. Kim KL, Steinke F, Hein M Supplementary material of “semi-supervised regression using hessian energy with an application to semi-supervised dimensionality reduction”

  27. Saeed N, Nam H (2016) Cluster based multidimensional scaling for irregular cognitive radio networks localization. IEEE Trans Signal Process 64(10):2649–2659

    Article  MathSciNet  Google Scholar 

  28. Igual L, Perez-Sala X, Escalera S, Angulo C, De la Torre F (2014) Continuous generalized procrustes analysis. Pattern Recogn 47(2):659–671

    Article  Google Scholar 

  29. Li B, He Y, Guo F, Zuo L (2012) A novel localization algorithm based on isomap and partial least squares for wireless sensor networks. IEEE Trans Instrum Meas 62(2):304–314

    Article  Google Scholar 

  30. Xiang S, Nie F, Pan C, Zhang C (2011) Regression reformulations of lle and ltsa with locally linear transformation. IEEE Trans Sys, Man, and Cybernetics, Part B (Cybernetics) 41(5):1250–1262

    Article  Google Scholar 

  31. TelosB Datasheet. Crossbow inc, (2013)

  32. Dong C, Ding J, Lin J (2016) Segmented polynomial rssi-lqi ranging modelling for zigbee-based positioning systems. In: 2016 35th Chinese Control Conference (CCC), 8387–8390. IEEE

  33. Zhang T, Yang J, Zhao D, Ge X (2007) Linear local tangent space alignment and application to face recognition. Neurocomputing 70(7–9):1547–1553

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT, Indian Institute of Information Technology Allahabad, Deoghat, Jhalwa, Prayagraj, Uttar Pradesh, India

    Abhishek, Rakesh Kumar Yadav, Shekhar Verma & S. Venkatesan

Authors
  1. Abhishek
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rakesh Kumar Yadav
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shekhar Verma
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. S. Venkatesan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Rakesh Kumar Yadav.

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

Abhishek, Yadav, R.K., Verma, S. et al. iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN. J Supercomput 77, 12026–12049 (2021). https://doi.org/10.1007/s11227-021-03761-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03761-0

Keywords