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Area of Science:

  • Agricultural Engineering
  • Sensor Networks
  • Data Science

Background:

  • Wireless sensor networks (WSNs) are crucial for environmental monitoring in agriculture.
  • Actuator-induced noise complicates WSN data, leading to inaccuracies and decision errors.
  • Effective data fusion is needed to address noise and improve automation accuracy.

Purpose of the Study:

  • To develop a robust data fusion technique for WSNs that effectively handles actuator noise.
  • To identify and mitigate the impact of noisy sensor nodes on overall data quality.
  • To accelerate data fusion processes in large-scale WSN deployments.

Main Methods:

  • Introduced a smoothing value and Prim's algorithm-based search for stable sensing data.
  • Proposed a dynamic weighted voting mechanism to reduce actuator noise influence.
  • Developed a prediction-based acceleration method to shorten data collection time.

Main Results:

  • The dynamic weighting significantly reduced actuator noise impact, improving data conditioning over time.
  • The acceleration method decreased data collection time in large networks.
  • Experimental validation on STM32F103 and nRF24L01 platforms confirmed algorithm improvements.

Conclusions:

  • The proposed data fusion method effectively suppresses actuator noise in WSNs.
  • The algorithms enhance the accuracy and efficiency of environmental data acquisition for agriculture.
  • The system demonstrates practical applicability and improved performance in real-time scenarios.