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Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks.

Abdelwahed Motwakel1,2, Aisha Hassan Abdalla Hashim1, Hayam Alamro3

  • 1Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia.

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|November 14, 2023
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Summary
This summary is machine-generated.

This study introduces a new Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for Wireless Sensor Networks (WSNs). The CMLOA-NLA significantly improves node localization accuracy and efficiency, achieving a minimum average error of 2.09%.

Keywords:
anchor nodesmetaheuristic optimization algorithmnode localizationtent chaotic mappingwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for diverse applications, including environmental monitoring and healthcare.
  • Accurate Node Localization (NL) is a fundamental challenge in WSNs, vital for applications like target tracking and data routing.
  • Existing localization methods often face limitations in accuracy and efficiency.

Purpose of the Study:

  • To develop an advanced Node Localization Approach (NLA) for WSNs.
  • To enhance the performance of the Lion Optimization Algorithm (LOA) for NL using chaotic mapping.
  • To introduce the Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA).

Main Methods:

  • The CMLOA-NLA utilizes anchor nodes (ANs) as reference points for localizing unknown sensor nodes (SNs).
  • The algorithm integrates the tent chaotic mapping concept into the standard LOA to improve convergence speed and precision.
  • Extensive simulations were conducted to evaluate the performance of the proposed CMLOA-NLA technique.

Main Results:

  • The CMLOA-NLA demonstrated significant improvements in localization accuracy and efficiency compared to existing methods.
  • The technique achieved a minimum average localization error of 2.09%.
  • The CMLOA-NLA exhibited high robustness against localization errors and variations in transmission range.

Conclusions:

  • The proposed CMLOA-NLA is an effectual technique for accurate and efficient node localization in WSNs.
  • The integration of chaotic mapping enhances the optimization capabilities of the LOA for NL.
  • The CMLOA-NLA offers a robust solution for critical WSN applications requiring precise sensor positioning.