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Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics.

Guiling Sun1, Ziyang Zhang2, Bowen Zheng3

  • 1School of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China. sungl@nankai.edu.cn.

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Summary
This summary is machine-generated.

This study introduces an improved data fusion algorithm for greenhouse wireless sensor networks (WSNs), enhancing precision and stability using trust degree and genetic algorithms. The new method significantly reduces data fluctuation and processing time.

Keywords:
WSNscubic exponential smoothingdata fusiongreenhouseimproved genetic algorithmtrust degree

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Area of Science:

  • Agricultural Engineering
  • Computer Science
  • Sensor Networks

Background:

  • Greenhouse wireless sensor networks (WSNs) face challenges with low data fusion precision and poor system stability.
  • Existing data fusion algorithms often lack robustness in handling noisy and abnormal sensor data.

Purpose of the Study:

  • To propose a novel multi-sensor data fusion algorithm for WSNs that improves data fusion precision and system stability.
  • To enhance the performance of genetic algorithms for optimizing data fusion estimation.

Main Methods:

  • Data preprocessing using cubic exponential smoothing to eliminate noise and outliers.
  • A data fusion algorithm based on exponential trust degree to mitigate absolute mutual trust issues.
  • An improved genetic algorithm with enhanced crossover and mutation operations, utilizing chaotic sequences for optimization and avoiding premature convergence.

Main Results:

  • Cubic exponential smoothing effectively reduces data fluctuation and enhances system stability.
  • The proposed trust degree and improved genetic algorithm-based fusion method demonstrates higher precision compared to arithmetic average and adaptive weighting methods.
  • The algorithm achieves a significant reduction in execution time.

Conclusions:

  • The developed data fusion algorithm offers superior performance in terms of precision and stability for greenhouse WSNs.
  • The integration of trust degree and optimized genetic algorithms provides a robust solution for complex sensor network data.
  • This approach presents a promising advancement for environmental monitoring and control in agricultural settings.