IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v80y2022i4d10.1007_s11235-022-00922-1.html
   My bibliography  Save this article

Improved DV-Hop localization algorithm based on social learning class topper optimization for wireless sensor network

Author

Listed:
  • Tapan Kumar Mohanta

    (ICFAI University)

  • Dushmanta Kumar Das

    (National Institute of Technology Nagaland)

Abstract

The process of locating nodes is really a challenging problem in the field of wireless sensor networks. Wireless sensor network localization is commonly followed by the distance vector algorithm. All beacon nodes are currently using DV-Hop algorithms to locate the dumb node. On the other hand, the approximate distance from the dumb node to certain beacon nodes contains a significant error, resulting in a large finished dumb node localization problem. To improve localization error an efficient DV-Hop method on social learning class topper optimization for wireless sensor networks is implemented in this paper. The proposed algorithm reduces communication between unknown or dumb and beacon nodes by measuring the dimensions of all the beacons at dumb nodes. The network imbalance model is frequently used to show the applicability of the proposed approach in anisotropic networks. Simulations are performed on LabVIEW 2015 platform. The results show that our proposed method outperforms some existing algorithms in terms of computing time (2%), localization error (6.6%), and localization error variance (8.3%).

Suggested Citation

  • 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.
    • Handle: RePEc:spr:telsys:v:80:y:2022:i:4:d:10.1007_s11235-022-00922-1
    DOI: 10.1007/s11235-022-00922-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-022-00922-1
    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/s11235-022-00922-1?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. 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.
    2. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    3. Gulshan Kumar & Mritunjay Kumar Rai & Rahul Saha & Hye-jin Kim, 2018. "An improved DV-Hop localization with minimum connected dominating set for mobile nodes in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
    4. Penghong Wang & Fei Xue & Hangjuan Li & Zhihua Cui & Liping Xie & Jinjun Chen, 2019. "A Multi-Objective DV-Hop Localization Algorithm Based on NSGA-II in Internet of Things," Mathematics, MDPI, vol. 7(2), pages 1-20, February.
    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. Jezek, M., 2009. "Passive Investors, Active Traders and Strategic Delegation of Price Discovery," Cambridge Working Papers in Economics 0951, Faculty of Economics, University of Cambridge.
    2. H Peyton Young, 2014. "The Evolution of Social Norms," Economics Series Working Papers 726, University of Oxford, Department of Economics.
    3. Jonas Hedlund & Carlos Oyarzun, 2018. "Imitation in heterogeneous populations," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 65(4), pages 937-973, June.
    4. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    5. Cartwright, Edward & Singh, Thomas B., 2018. "Observation and contagion effects in cooperation: An experimental investigation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 151-160.
    6. Yihang Zhao & Jing Xiong & De Hu, 2023. "Reputation, Network, and Performance: Exploring the Diffusion Mechanism of Local Governments’ Behavior during Inter-Governmental Environmental Cooperation," Land, MDPI, vol. 12(7), pages 1-17, July.
    7. Boerner, Lars & Severgnini, Battista, 2015. "Time for growth," LSE Research Online Documents on Economics 64495, London School of Economics and Political Science, LSE Library.
    8. Economo, Evan & Hong, Lu & Page, Scott E., 2016. "Social structure, endogenous diversity, and collective accuracy," Journal of Economic Behavior & Organization, Elsevier, vol. 125(C), pages 212-231.
    9. Itai Arieli & Yakov Babichenko & Ron Peretz & H. Peyton Young, 2018. "The Speed of Innovation Diffusion," Economics Papers 2018-W06, Economics Group, Nuffield College, University of Oxford.
    10. Benito-Ostolaza, Juan M. & Brañas-Garza, Pablo & Hernández, Penélope & Sanchis-Llopis, Juan A., 2015. "Strategic behaviour in Schelling dynamics: Theory and experimental evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 57(C), pages 134-147.
    11. Rehse, Dominik & Tremöhlen, Felix, 2022. "Fostering participation in digital contact tracing," Information Economics and Policy, Elsevier, vol. 58(C).
    12. Mathijs de Vaan & Saqib Mumtaz & Abhishek Nagaraj & Sameer B. Srivastava, 2021. "Social Learning in the COVID-19 Pandemic: Community Establishments’ Closure Decisions Follow Those of Nearby Chain Establishments," Management Science, INFORMS, vol. 67(7), pages 4446-4454, July.
    13. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    14. Edouard Civel & Marc Baudry, 2018. "The Fate of Inventions. What can we learn from Bayesian learning in strategic options model of adoption ?," EconomiX Working Papers 2018-47, University of Paris Nanterre, EconomiX.
    15. Pongou, Roland & Serrano, Roberto, 2016. "Volume of trade and dynamic network formation in two-sided economies," Journal of Mathematical Economics, Elsevier, vol. 63(C), pages 147-163.
    16. Elisabeth SADOULET, 2016. "Review of Theories of Learning for Adopting," Working Papers P163, FERDI.
    17. Verónica Robert & Gabriel Yoguel & Octavio Lerena, 2017. "The ontology of complexity and the neo-Schumpeterian evolutionary theory of economic change," Journal of Evolutionary Economics, Springer, vol. 27(4), pages 761-793, September.
    18. Gavrilets, Sergey & Tverskoi, Denis & Sánchez, Angel, 2023. "Modeling social norms: an integration of the norm-utility approach with beliefs dynamics," SocArXiv n934a, Center for Open Science.
    19. Zeppini, Paolo, 2015. "A discrete choice model of transitions to sustainable technologies," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 187-203.
    20. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.

    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:80:y:2022:i:4:d:10.1007_s11235-022-00922-1. 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.