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Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT

Author

Listed:
  • Haixu Niu

    (Faculty of Information Science and Engineering, Management and Science University, Shah Alam 40100, Malaysia
    School of Management, Henan University of Technology, Zhengzhou 450001, China)

  • Yonghai Li

    (School of Management, Henan University of Technology, Zhengzhou 450001, China)

  • Shuaixin Hou

    (College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Tianfei Chen

    (College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Lijun Sun

    (College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Mingyang Gu

    (College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Muhammad Irsyad Abdullah

    (Faculty of Information Science and Engineering, Management and Science University, Shah Alam 40100, Malaysia)

Abstract

Node localization is a critical challenge in Internet of Things (IoT) applications. The DV-Hop algorithm, which relies on hop counts for localization, assumes that network nodes are uniformly distributed. It estimates actual distances between nodes based on the number of hops. However, in practical IoT networks, node distribution is often non-uniform, leading to complex and irregular topologies that significantly reduce the localization accuracy of the original DV-Hop algorithm. To improve localization performance in non-uniform topologies, we propose an enhanced DV-Hop algorithm using Grey Wolf Optimization (GWO). First, the impact of non-uniform node distribution on hop count and average hop distance is analyzed. A binary Grey Wolf Optimization algorithm (BGWO) is then applied to develop an optimal anchor node selection strategy. This strategy eliminates anchor nodes with high estimation errors and selects a subset of high-quality anchors to improve the localization of unknown nodes. Second, in the multilateration stage, the traditional least square method is replaced by a continuous GWO algorithm to solve the distance equations with higher precision. Simulated experimental results show that the proposed GWO-enhanced DV-Hop algorithm significantly improves localization accuracy in non-uniform topologies.

Suggested Citation

  • Haixu Niu & Yonghai Li & Shuaixin Hou & Tianfei Chen & Lijun Sun & Mingyang Gu & Muhammad Irsyad Abdullah, 2025. "Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT," Future Internet, MDPI, vol. 17(6), pages 1-18, June.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:6:p:253-:d:1674386
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    References listed on IDEAS

    as
    1. Sheetal Ghorpade & Marco Zennaro & Bharat Chaudhari, 2021. "Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues," Future Internet, MDPI, vol. 13(8), pages 1-26, August.
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