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Improved DV-Hop based on parallel compact Willow Catkin Optimization algorithm for 3D nodes localization in WSN
Wireless sensor networks (WSNs) play a critical role in environmental sensing and data transmission. However, their performances are often hindered by challenges like localization accuracy and storage capacity. Existing variants of DV-Hop algorithm suffer from issues like high memory usage, low loca...
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Published in: | Wireless networks 2024-05, Vol.30 (4), p.2017-2039 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Wireless sensor networks (WSNs) play a critical role in environmental sensing and data transmission. However, their performances are often hindered by challenges like localization accuracy and storage capacity. Existing variants of DV-Hop algorithm suffer from issues like high memory usage, low localization accuracy, and limited applicability in realistic three-dimensional (3D) environments. To overcome these challenges and solve the localization problem for DV-Hop based WSN nodes in 3D space, this research proposes a novel hybrid optimizer called PCWCO (Parallel Compact Willow Catkin Optimization Algorithm). The PCWCO algorithm incorporates compact technique and a new parallel strategy into the Willow Catkin Optimization (WCO) framework, aiming to reduce memory usage while enhancing solution quality. Rigorous numerical validations are conducted using benchmark functions from the CEC2017 to assess the performance of the proposed PCWCO optimizer. The results demonstrate that PCWCO exhibits competitive performance compared to classical intelligent optimization algorithms. Moreover, we synergistically integrate the PCWCO algorithm with DV-Hop to form a hybrid approach called PCWCO-3D-DV-Hop to facilitate the localization efficiency of WSN nodes in 3D space. |
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ISSN: | 1022-0038 1572-8196 |
DOI: | 10.1007/s11276-023-03638-5 |