Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
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...
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MDPI AG
2025-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/6/253 |
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| author | Haixu Niu Yonghai Li Shuaixin Hou Tianfei Chen Lijun Sun Mingyang Gu Muhammad Irsyad Abdullah |
| author_facet | Haixu Niu Yonghai Li Shuaixin Hou Tianfei Chen Lijun Sun Mingyang Gu Muhammad Irsyad Abdullah |
| author_sort | Haixu Niu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3027dcc2ad4a480c8fced9d22fcc590d |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-3027dcc2ad4a480c8fced9d22fcc590d2025-08-20T03:27:29ZengMDPI AGFuture Internet1999-59032025-06-0117625310.3390/fi17060253Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoTHaixu Niu0Yonghai Li1Shuaixin Hou2Tianfei Chen3Lijun Sun4Mingyang Gu5Muhammad Irsyad Abdullah6Faculty of Information Science and Engineering, Management and Science University, Shah Alam 40100, MalaysiaSchool of Management, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaFaculty of Information Science and Engineering, Management and Science University, Shah Alam 40100, MalaysiaNode 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.https://www.mdpi.com/1999-5903/17/6/253Internet of Thingsnode localizationDV-Hopanchor node selection |
| spellingShingle | Haixu Niu Yonghai Li Shuaixin Hou Tianfei Chen Lijun Sun Mingyang Gu Muhammad Irsyad Abdullah Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT Future Internet Internet of Things node localization DV-Hop anchor node selection |
| title | Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT |
| title_full | Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT |
| title_fullStr | Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT |
| title_full_unstemmed | Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT |
| title_short | Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT |
| title_sort | topology aware anchor node selection optimization for enhanced dv hop localization in iot |
| topic | Internet of Things node localization DV-Hop anchor node selection |
| url | https://www.mdpi.com/1999-5903/17/6/253 |
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