IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v67y2018i2d10.1007_s11235-017-0328-x.html
   My bibliography  Save this article

Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

Author

Listed:
  • Gaurav Sharma

    (National Institute of Technology)

  • Ashok Kumar

    (National Institute of Technology)

Abstract

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

Suggested Citation

  • Gaurav Sharma & Ashok Kumar, 2018. "Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(2), pages 163-178, February.
    • Handle: RePEc:spr:telsys:v:67:y:2018:i:2:d:10.1007_s11235-017-0328-x
    DOI: 10.1007/s11235-017-0328-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-017-0328-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-017-0328-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mahshid Mehrabi & Hassan Taheri & Pooria Taghdiri, 2017. "An improved DV-Hop localization algorithm based on evolutionary algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 639-647, April.
    2. Stefan Tomic & Ivan Mezei, 2016. "Improvements of DV-Hop localization algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(1), pages 93-106, January.
    3. Shrawan Kumar & D. K. Lobiyal, 2017. "Novel DV-Hop localization algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(3), pages 509-524, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haibin Sun & Dong Wang & Hongxing Li & Ziran Meng, 2023. "An improved DV-Hop algorithm based on PSO and Modified DE algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(3), pages 403-418, March.
    2. Hilary I. Okagbue & Muminu O. Adamu & Timothy A. Anake & Ashiribo S. Wusu, 2019. "Nature inspired quantile estimates of the Nakagami distribution," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 517-541, December.
    3. Hend Liouane & Sana Messous & Omar Cheikhrouhou, 2022. "Regularized least square multi-hops localization algorithm based on DV-Hop for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(3), pages 349-358, July.
    4. Tapan Kumar Mohanta & Dushmanta Kumar Das, 2022. "Improved DV-Hop localization algorithm based on social learning class topper optimization for wireless sensor network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(4), pages 529-543, August.
    5. Xiuwu Yu & Yinhao Liu & Yong Liu, 2024. "Optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 86(3), pages 547-558, July.
    6. Shilpi & Arvind Kumar, 2023. "A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 277-289, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaurav Sharma & Ashok Kumar, 2018. "Fuzzy logic based 3D localization in wireless sensor networks using invasive weed and bacterial foraging optimization," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(2), pages 149-162, February.
    2. Mahshid Mehrabi & Hassan Taheri & Pooria Taghdiri, 2017. "An improved DV-Hop localization algorithm based on evolutionary algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 639-647, April.
    3. Sana Messous & Hend Liouane & Noureddine Liouane, 2020. "Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(1), pages 75-86, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:telsys:v:67:y:2018:i:2:d:10.1007_s11235-017-0328-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.