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Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless

Ivana Strumberger1, Miroslav Minovic2, Milan Tuba3

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Summary
This summary is machine-generated.

Improved swarm intelligence algorithms enhance wireless sensor network localization accuracy. Enhanced tree growth and elephant herding optimization algorithms provide more consistent and precise node positioning, even with noisy data.

Keywords:
NP hardnesselephant herding optimizationnode localizationswarm intelligencetree growth algorithmwireless sensor networks

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Area of Science:

  • Computer Science
  • Networking
  • Artificial Intelligence

Background:

  • Wireless sensor networks (WSNs) are crucial for diverse applications like environmental monitoring and surveillance.
  • Node localization is a significant challenge in WSNs, impacting network functionality and data reliability.
  • Swarm intelligence metaheuristics have shown promise in addressing WSN localization problems.

Purpose of the Study:

  • To improve the accuracy and consistency of node localization in wireless sensor networks.
  • To enhance existing swarm intelligence algorithms for WSN localization.
  • To evaluate the performance of improved algorithms against state-of-the-art methods.

Main Methods:

  • Modification and application of the tree growth algorithm and elephant herding optimization metaheuristics.
  • Empirical experiments conducted on sensor networks of varying sizes (25-150 nodes).
  • Simulations incorporated Gaussian noise to mimic real-world distance measurement inaccuracies.
  • Comparative analysis against butterfly optimization, particle swarm optimization, and firefly algorithms.

Main Results:

  • The proposed enhanced algorithms demonstrated superior performance in WSN localization.
  • Achieved more consistent and accurate positioning of unknown target nodes compared to existing algorithms.
  • Effectiveness validated across different network sizes and under noisy conditions.

Conclusions:

  • The enhanced tree growth and elephant herding optimization algorithms offer significant improvements for WSN localization.
  • These advanced swarm intelligence approaches provide a more reliable solution for accurate node positioning in WSNs.
  • The findings contribute to overcoming key challenges in WSN deployment and operation.