Skip to main content
SpringerLink
Log in

Node Localization in Wireless Sensor Networks Using Multi-output Random Forest Regression

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1057))

Abstract

More advanced developments have been made in the field of wireless communications, and it has further accelerated the growth of compact and low power-consuming wireless sensor nodes. During communication, each source node estimates the shortest path to the destination node by using the location information. Location information also helps in securing the network in the prevention of intruders. Previously available sensor node localization methods in the literature such as radio signals, time of arrival (ToA), and time difference of arrival (TDoA) suffers from various drawbacks. Also, the usage of sophisticated devices like GPS to sense the location of the node increases the deployment cost and in parallel, the energy consumption is also increased. This paper aims at developing a model to predict the future location of a dynamic sensor node. The linear model is built using the historical location information of the respective node. The trained model is capable of predicting the X- and Y-coordinates of a node accurately. For each of the node, a separate model is built and their future locations are predicted. If a node has data packets to transmit to a sink node, it obtains the present and next location of the sink node from the base node.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kulkarni, R.V., Venayagamoorthy, G.K., Cheng, M.X.: Bio-inspired node localization in wireless sensor networks. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 205–210 (2009)

    Google Scholar 

  2. Zaidi, S., El Assaf, A., Affes, S., Kandil, N.: Accurate range-free localization in multi-hop wireless sensor networks. IEEE Trans. Commun. 64, 3886–3900 (2016)

    Article  Google Scholar 

  3. Singh, M., Khilar, P.M.: An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network. Wirel. Netw. 22, 2537–2550 (2016)

    Article  Google Scholar 

  4. Singh, M., Khilar, P.M.: Mobile beacon based range free localization method for wireless sensor networks. Wirel. Netw. 23, 1285–1300 (2017)

    Article  Google Scholar 

  5. He, J., Yu, Y., Wang, Q.: RSS assisted ToA-based indoor geolocation. Int. J. Wireless Inf. Networks 20(2), 157–165 (2013)

    Article  Google Scholar 

  6. Sun, S., Zhu, S., Ding, Z., Xu, B.: ToA-based source localization: a linearization approach adopting coordinate system translation. Int. J. Distrib. Sens. Netw. 2013(5), 140–154 (2013)

    Google Scholar 

  7. Kaune, R.: Accuracy studies for TDoA and to a localization. In: International Conference on Information Fusion, pp. 408–415 (2012)

    Google Scholar 

  8. Giacometti, R., Baussard, A., Cornu, C., Khenchaf, A., Jahan, D., Quellec, J.M.: Accuracy studies for TDoA-AoA localization of emitters with a single sensor. In: IEEE Radar Conference, pp. 1–4 (2016)

    Google Scholar 

  9. Stoica, P., Sharman, K.: Maximum likelihood methods for direction-of-arrival estimation. IEEE Trans. Acoust. Speech Signal Proc. 38(7), 1132–1143 (1990)

    Article  Google Scholar 

  10. Gopakumar, A., Jacob, L.: Localization in wireless sensor networks using particle swarm optimization. In: Proceedings of IET International Conference on Wireless, Mobile and Multimedia Networks, pp. 227–230 (2008)

    Google Scholar 

  11. Kulkarni, R.V., Venayagamoorthy, G.K.: Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41, 262–267 (2011)

    Article  Google Scholar 

  12. Pettersen, S.A, Johansen, D., Johansen, H., Berg-Johansen, V., Gaddam, V.R., Mortensen, A., Langseth, R., Griwodz, C., Stensland, H.K., Halvorsen, P.: Soccer video and player position dataset. In: International Conference on Multimedia Systems (MMSys), pp. 18–23. Singapore, Mar 2014

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of BCA, Anna Adarsh College for Women, Chennai, Tamil Nadu, India

    K. Madhumathi

  2. Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India

    T. Suresh

Authors
  1. K. Madhumathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Madhumathi .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Madhumathi, K., Suresh, T. (2020). Node Localization in Wireless Sensor Networks Using Multi-output Random Forest Regression. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_16

Download citation

Publish with us

Policies and ethics

Search

Navigation