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<p>Wireless Sensor Networks (WSNs) play a crucial role in various fields due to their ability to facilitate reliable data communication and precise localization. This paper addresses the challenges of 3-Dimensional (3D) WSN localization using a range-free algorithm known as Approximate Point In Triangle (APIT). APIT is known for its simplicity and applicability and is often hampered by high localization errors and computational complexity, especially in 3D environments. Due to growing demand for high-accuracy and low-cost localization in complex 3D deployments, where traditional methods fall short in scalability and precision, we propose a novel approach called Weighted RSSI Seagull Optimized APIT (WRSO-APIT), which introduces several enhancements to improve localization accuracy and efficiency. WRSO-APIT optimizes the selection of triangular polygons for the PIT test by calculating the optimal number of neighbor nodes required and by selecting the nearest neighbor nodes to the unknown node. Additionally, we assign weights to triangular polygons on the basis of their estimated size using Received Signal Strength Indicator (RSSI) values, further enhancing the localization process. To further reduce localization error and computational overhead, we integrate the Seagull Optimization Algorithm (SOA) to leverage its natural exploration and exploitation behavior to improve the projected node position. Given the high-dimensional search space and the iterative nature of the optimization process, the implementation of WRSO-APIT necessitated the use of high-performance computing resources to ensure scalability and timely convergence, especially during extensive simulations involving large-scale WSN deployments. Through extensive simulations, we demonstrate that WRSO-APIT significantly outperforms existing algorithms such as TDSDV-Hop, Collaborative Coefficient-Triangle APIT Localization (CCAL), and Volume Test Approximate Point-In-Triangulation Test in Three Dimensions (VT-APIT-3D) which are limited by dependency on accuracy of hop count and imprecise distance estimation (TDSDV-Hop), rigid triangle selection without contextual adaptation (CCAL), and static volume-based inclusion tests that lack responsiveness to dynamic network conditions (VT-APIT-3D). Our approach not only reduces localization error but also minimizes computational complexity, making it a robust and efficient solution for 3D WSN localization.</p>

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Localization using seagull optimized APIT in 3-dimensional wireless sensor network

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Aditi Agrawal,
  • Abhinesh Kaushik

摘要

Wireless Sensor Networks (WSNs) play a crucial role in various fields due to their ability to facilitate reliable data communication and precise localization. This paper addresses the challenges of 3-Dimensional (3D) WSN localization using a range-free algorithm known as Approximate Point In Triangle (APIT). APIT is known for its simplicity and applicability and is often hampered by high localization errors and computational complexity, especially in 3D environments. Due to growing demand for high-accuracy and low-cost localization in complex 3D deployments, where traditional methods fall short in scalability and precision, we propose a novel approach called Weighted RSSI Seagull Optimized APIT (WRSO-APIT), which introduces several enhancements to improve localization accuracy and efficiency. WRSO-APIT optimizes the selection of triangular polygons for the PIT test by calculating the optimal number of neighbor nodes required and by selecting the nearest neighbor nodes to the unknown node. Additionally, we assign weights to triangular polygons on the basis of their estimated size using Received Signal Strength Indicator (RSSI) values, further enhancing the localization process. To further reduce localization error and computational overhead, we integrate the Seagull Optimization Algorithm (SOA) to leverage its natural exploration and exploitation behavior to improve the projected node position. Given the high-dimensional search space and the iterative nature of the optimization process, the implementation of WRSO-APIT necessitated the use of high-performance computing resources to ensure scalability and timely convergence, especially during extensive simulations involving large-scale WSN deployments. Through extensive simulations, we demonstrate that WRSO-APIT significantly outperforms existing algorithms such as TDSDV-Hop, Collaborative Coefficient-Triangle APIT Localization (CCAL), and Volume Test Approximate Point-In-Triangulation Test in Three Dimensions (VT-APIT-3D) which are limited by dependency on accuracy of hop count and imprecise distance estimation (TDSDV-Hop), rigid triangle selection without contextual adaptation (CCAL), and static volume-based inclusion tests that lack responsiveness to dynamic network conditions (VT-APIT-3D). Our approach not only reduces localization error but also minimizes computational complexity, making it a robust and efficient solution for 3D WSN localization.