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An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks.

Fang Fan1, Shu-Chuan Chu1, Jeng-Shyang Pan1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, People's Republic of China.

Journal of Applied Statistics
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved particle swarm optimization (PSO) algorithm for fault detection in wireless sensor networks (WSN). The enhanced method optimizes neural network training, improving data accuracy and network reliability.

Keywords:
Particle swarm optimizationback propagation neural networkfault detectionparallelpopulation sizewireless sensor networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSN) are crucial for the Internet of Things (IoT), but data collection faults can compromise network reliability and performance.
  • Accurate data collection is vital for the real-time operation, energy efficiency, and overall dependability of WSNs.

Purpose of the Study:

  • To address fault detection challenges in WSN data collection.
  • To enhance the performance of machine learning models for identifying faulty sensor data.
  • To improve the reliability and efficiency of WSN operations through advanced fault detection.

Main Methods:

  • Combined evolutionary computing and machine learning techniques.
  • Improved the classical particle swarm optimization (PSO) algorithm by incorporating a biological population model and a parallel mechanism, creating the RS-PPSO algorithm.
  • Utilized the RS-PPSO algorithm to optimize the initial weights and biases of a back propagation neural network (BPNN).

Main Results:

  • The RS-PPSO algorithm significantly reduced the training time for the BPNN.
  • Prediction accuracy of the BPNN was substantially increased after optimization.
  • The optimized machine learning scheme effectively identified fault data in WSNs.

Conclusions:

  • The proposed RS-PPSO algorithm offers a productive technical solution for fault detection in WSN data collection.
  • Optimizing neural network parameters with evolutionary algorithms enhances WSN data integrity and network performance.
  • This approach ensures the effective operation of WSNs by accurately identifying and mitigating faulty data.