IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v82y2023i2d10.1007_s11235-022-00984-1.html
   My bibliography  Save this article

A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks

Author

Listed:
  • Shilpi

    (Motilal Nehru National Institute of Technology Allahabad)

  • Arvind Kumar

    (Motilal Nehru National Institute of Technology Allahabad)

Abstract

In wireless sensor networks (WSNs) and large-scale IoT applications, node localization is a challenging process to identify the location of the target or unknown nodes for accurate information transmission between sensor nodes. Due to their ease of hardware implementation and suitability for large-scale WSNs, range-free localization techniques have been shown in previous studies. The existing range-free localization algorithms did not consider the anisotropy factors typically seen in WSNs, leading to poor positioning accuracy. We proposed a range-free localization solution that combines the benefits of geometric constraint and hop progress-based approaches to address this issue. Each unknown node categorizes the anchor node pairs into one of three proposed categories, and the discriminating conditions are designed using the geometric information provided by the combination of the anchor node pairs and unknown nodes. A node localization algorithm is proposed to determine the position of target nodes or unknown nodes and to reduce the effect of anisotropic factors in isotropic, O-shaped, and S-shaped anisotropic WSNs using the parameter-less Jaya algorithm (JA) and range-free method of reliable anchor pair (RAP) selection approach. In the case of anisotropic WSNs (AWSNs), finding the location of target nodes is more complicated. The presented work is compared with the existing node localization methods, including Distance Vector (DV)-maxHop, Particle Swarm Optimization (PSO), and Quantized Salp Swarm Algorithm (QSSA) based localization algorithms. The proposed approach provides improved localization accuracy compared to the existing node localization methods regarding the number of anchor nodes and node density. The proposed algorithm also looks at how the degree of irregularity and computation time affect the performance.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:telsys:v:82:y:2023:i:2:d:10.1007_s11235-022-00984-1
    DOI: 10.1007/s11235-022-00984-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-022-00984-1
    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/s11235-022-00984-1?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. Abdul Wadood & Saeid Gholami Farkoush & Tahir Khurshaid & Jiang-Tao Yu & Chang-Hwan Kim & Sang-Bong Rhee, 2019. "Application of the JAYA Algorithm in Solving the Problem of the Optimal Coordination of Overcurrent Relays in Single- and Multi-Loop Distribution Systems," Complexity, Hindawi, vol. 2019, pages 1-13, December.
    2. 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.
    3. Parulpreet Singh & Arun Khosla & Anil Kumar & Mamta Khosla, 2018. "Computational intelligence based localization of moving target nodes using single anchor node in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 69(3), pages 397-411, November.
    4. 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.
    5. Xuan Liu & Shigeng Zhang & Kai Bu, 2016. "A locality-based range-free localization algorithm for anisotropic wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(1), pages 3-13, May.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Prabhjot Singh & Nitin Mittal & Parulpreet Singh, 2022. "A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(3), pages 303-323, 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. Faraj Al-Bhadely & Aslan İnan, 2023. "Improving Directional Overcurrent Relay Coordination in Distribution Networks for Optimal Operation Using Hybrid Genetic Algorithm with Sequential Quadratic Programming," Energies, MDPI, vol. 16(20), pages 1-21, October.
    6. Soumya J. Bhat & K. V. Santhosh, 2022. "Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(2), pages 309-321, February.
    7. 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.
    8. Zixi Jia & Bo Guan, 2018. "Received signal strength difference–based tracking estimation method for arbitrarily moving target in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(3), pages 15501477187, March.
    9. 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.
    10. Muhammad Irfan & Abdul Wadood & Tahir Khurshaid & Bakht Muhammad Khan & Ki-Chai Kim & Seung-Ryle Oh & Sang-Bong Rhee, 2021. "An Optimized Adaptive Protection Scheme for Numerical and Directional Overcurrent Relay Coordination Using Harris Hawk Optimization," Energies, MDPI, vol. 14(18), pages 1-21, September.
    11. Kashif Habib & Xinquan Lai & Abdul Wadood & Shahbaz Khan & Yuheng Wang & Siting Xu, 2022. "An Improved Technique of Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays of IEEE Bus System," Energies, MDPI, vol. 15(9), pages 1-17, April.

    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:82:y:2023:i:2:d:10.1007_s11235-022-00984-1. 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.