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

Fang Fan, Shu-Chuan Chu, Jeng Shyang Pan, Chuang Lin, Huiqi Zhao

Research output: Contribution to journalArticlepeer-review

Abstract

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.

Original languageEnglish
Number of pages18
JournalJournal of Applied Statistics
Early online date26 May 2021
DOIs
Publication statusE-pub ahead of print - 26 May 2021
Externally publishedYes

Keywords

  • back propagation neural network
  • fault detection
  • parallel
  • Particle swarm optimization
  • population size
  • wireless sensor networks

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