Scholarlink

<p>In many Wireless Sensor Network (WSN) applications, location information is crucial and important. Various localization strategies have been presented recently, but these research proposals mainly focused on 2D applications. In the majority of 3D applications, the region being observed may contain significant variations in altitude levels and have a complex sensing environment. Only one anchor node (AN) is placed at the top layer of an anisotropic network to locate every unknown target node. The target nodes (TNs) are placed in the middle and bottom levels. When these TNs are within three virtual anchors’ range, one anchor node is chosen using umbrella projection in a 3D environment to estimate the locations (since at least four ANs are needed to locate sensors). In order to tackle this issue, this study introduces a hybrid version of the differential evolution (DE) method and the birds-of-prey-based optimization algorithm (BPBO), namely DE-based BPBO (DEBPBO). The method is an entirely new version that has been created to address the inadequate exploration properties and the stagnation of local optima of BPBO and DE, respectively, and CEC 2019 benchmark suite is used to assess its effectiveness. The precise locations of the mobile nodes are determined using the proposed DEBPBO algorithm. Based on simulation findings, the suggested DEBPBO algorithm performs better than other metaheuristic optimization methods in terms of sensor node location estimation, computational time, and localization error.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Differential Evolution-based Birds of Prey-based Optimization Algorithm for Localization of Sensor Nodes in 3D Wireless Sensor Networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

查看全文
  • Abdelrahman H. Hussein,
  • Nitin Mittal,
  • Simrandeep Singh,
  • Mahendihasan S. Heera,
  • Jatinder Kaur,
  • Fikreselam Gared

摘要

In many Wireless Sensor Network (WSN) applications, location information is crucial and important. Various localization strategies have been presented recently, but these research proposals mainly focused on 2D applications. In the majority of 3D applications, the region being observed may contain significant variations in altitude levels and have a complex sensing environment. Only one anchor node (AN) is placed at the top layer of an anisotropic network to locate every unknown target node. The target nodes (TNs) are placed in the middle and bottom levels. When these TNs are within three virtual anchors’ range, one anchor node is chosen using umbrella projection in a 3D environment to estimate the locations (since at least four ANs are needed to locate sensors). In order to tackle this issue, this study introduces a hybrid version of the differential evolution (DE) method and the birds-of-prey-based optimization algorithm (BPBO), namely DE-based BPBO (DEBPBO). The method is an entirely new version that has been created to address the inadequate exploration properties and the stagnation of local optima of BPBO and DE, respectively, and CEC 2019 benchmark suite is used to assess its effectiveness. The precise locations of the mobile nodes are determined using the proposed DEBPBO algorithm. Based on simulation findings, the suggested DEBPBO algorithm performs better than other metaheuristic optimization methods in terms of sensor node location estimation, computational time, and localization error.