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A Hybrid Positioning Framework for Large-Scale Three-Dimensional IoT Environments.

Shima Koulaeizadeh1, Hatef Javadi2, Sudabeh Gholizadeh3

  • 1Communication Networks, Technische Universität, 09111 Chemnitz, Germany.

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Summary
This summary is machine-generated.

This study introduces a novel GPS-free positioning framework for 3D Internet of Things (IoT) and Edge Computing (EC) environments. The proposed method enhances data accuracy by integrating DV-Hop, JS, and ARO algorithms, achieving 39% greater precision than existing solutions.

Keywords:
distance vector-hoplocalizationoptimizationpositioningthree-dimensional Internet of Things

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Internet of Things (IoT) and Edge Computing (EC) are crucial for modern applications but require accurate data source localization.
  • Global Positioning System (GPS) is often unsuitable for IoT/EC due to coverage, energy, and interference issues.
  • Accurate geographic origin determination is vital for the meaningfulness of sensed data in IoT and EC.

Purpose of the Study:

  • To propose an innovative GPS-free positioning framework for three-dimensional (3D) Edge Computing (EC) environments.
  • To address the limitations of GPS in IoT and EC scenarios.
  • To enhance the accuracy and efficiency of data source localization in 3D IoT/EC systems.

Main Methods:

  • Generalization of Distance Vector-Hop (DV-Hop) and multi-angulation algorithms for 3D environments.
  • Integration of DV-Hop and multi-angulation with structural modifications to balance exploration and exploitation.
  • Incorporation of Jellyfish Search (JS) and Artificial Rabbits Optimization (ARO) algorithms, including Lévy flight perturbation and local search for improved convergence and precision.

Main Results:

  • Simulated performance across sixteen diverse 3D IoT environments.
  • Comparison with nine state-of-the-art localization algorithms using Mean Squared Error (MSE), Normalized Localization Error (NLE), Average Localization Error (ALE), and Localization Error Variance (LEV) metrics.
  • Demonstrated an average accuracy improvement of 39% over competing methods via quantitative relative improvement ratio tests.

Conclusions:

  • The proposed GPS-free positioning framework significantly enhances localization accuracy in 3D IoT/EC environments.
  • The integration of DV-Hop, JS, and ARO algorithms offers a robust and efficient solution for data source localization.
  • The framework effectively overcomes the limitations of GPS, providing a practical alternative for various IoT and EC applications.