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Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization

K Lakshmi Prabha1, Hanan Abdullah Mengash2, Hamed Alqahtani3

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This study introduces an optimized Multi-Agent Reinforcement Learning (MARL) framework with Golden Jackal Optimization (GJO) to enhance wireless sensor network (WSN) localization accuracy. The novel approach dynamically adjusts parameters, significantly reducing localization errors in challenging environments.

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Golden jack optimizationLocalization errorMulti agent reinforcement learning algorithmPrediction. error minimizationWireless sensor network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Accurate sensor node localization is critical for Wireless Sensor Networks (WSNs) in applications like environmental monitoring and smart cities.
  • Dynamic environmental conditions, varying network densities, and parameter interdependencies pose significant challenges to localization accuracy, increasing Average Localization Error (ALE).
  • Existing methods often struggle with generalization in dynamic environments due to static parameter configurations or limitations in adapting to real-time network variations.

Purpose of the Study:

  • To propose a novel Multi-Agent Reinforcement Learning (MARL) algorithm integrated with Golden Jackal Optimization (GJO) to improve WSN localization accuracy.
  • To develop a framework that dynamically learns optimal parameter adjustments, minimizing localization error and its variability in dynamic network conditions.
  • To enhance the generalization capabilities of MARL across diverse WSN configurations through GJO hyperparameter tuning.

Main Methods:

  • Implementation of a Multi-Agent Reinforcement Learning (MARL) algorithm for dynamic parameter adjustment.
  • Integration of Golden Jackal Optimization (GJO) to fine-tune MARL hyperparameters for improved generalization.
  • Evaluation using a benchmark dataset and analysis of performance metrics including MSE, MAE, RMSE, R², and MAPE.

Main Results:

  • The proposed optimized MARL framework demonstrated significant improvements in WSN localization accuracy.
  • Achieved a Mean Squared Error (MSE) of 0.02, Mean Absolute Error (MAE) of 0.11, Root Mean Square Error (RMSE) of 0.14, R-squared (R²) of 0.88, and Mean Absolute Percentage Error (MAPE) of 2.5%.
  • Outperformed existing methods like Grid Search RF, Bayesian Optimized RF, Gradient Boosting, and Deep Neural Networks in benchmark evaluations.

Conclusions:

  • The novel MARL-GJO framework effectively addresses the challenges of dynamic WSN environments for accurate localization.
  • The dynamic learning and optimization approach significantly enhances localization accuracy and adaptability compared to static or heuristic models.
  • This research offers a robust solution for improving localization performance in critical WSN applications.