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Robust pollution source parameter identification based on the artificial bee colony algorithm using a wireless sensor

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  • 1Logistics Engineering College, Shanghai Maritime University, Shanghai, China.

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Pollution source parameter identification using wireless sensor networks is crucial for effective pollution control. The artificial bee colony algorithm demonstrates robust performance, outperforming traditional methods in identifying pollution sources.

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Pollution source parameter identification (PSPI) is vital for efficient pollution control.
  • Wireless Sensor Networks (WSNs) offer a scalable solution for widespread environmental monitoring.
  • Least-squares estimation methods are robust to noise distribution but can face convergence issues.

Purpose of the Study:

  • To adapt and evaluate the artificial bee colony (ABC) algorithm for WSN-based PSPI.
  • To assess the feasibility and robustness of the ABC algorithm in identifying pollution sources.
  • To compare the performance of ABC with other optimization algorithms for PSPI.

Main Methods:

  • Deployment of a large number of pollution sensor nodes to form a WSN.
  • Application of least-squares estimation principles for PSPI.
  • Adaptation and implementation of the artificial bee colony (ABC) algorithm for optimization.
  • Comparative analysis with particle swarm optimization (PSO) and trust-region reflective algorithms.

Main Results:

  • The ABC algorithm achieved comparable identification results to the PSO algorithm.
  • Both ABC and PSO significantly outperformed the traditional trust-region reflective algorithm.
  • The study verified the feasibility and robustness of the ABC algorithm for WSN-based PSPI.

Conclusions:

  • The artificial bee colony algorithm is a viable and effective method for WSN-based pollution source parameter identification.
  • Population-based algorithms like ABC and PSO offer advantages over single-point search methods for this problem.
  • The findings support the use of advanced algorithms for enhancing environmental monitoring and pollution control strategies.