• Title: Wireless sensor anomaly data detection by a deep learning algorithm
• Name: Smith Cassiel
• Company: Dickinson State University,USA.
• Abstract:

  Due to the dynamic differential transmission channel of wireless sensor data, the feature identification depth of some differential channel data is not enough in the process of abnormal data monitoring, resulting in the reduction of the overall detection accuracy.Therefore, a deep learning algorithm is introduced to optimize the detection parameters for wireless sensor abnormal data.Through the design and optimization calculation of three unit parameters based on wireless sensor data scale processing, abnormal screening of data deep learning, and deep learning identification of abnormal data features, the update of corresponding unit parameters is realized, thus achieving the effect of improving the overall detection accuracy.The simulation data affirms the effect of the proposed method. The data shows that the deep learning algorithm can effectively improve the abnormal data monitoring environment of wireless sensors, improve the accuracy of the abnormal data feature identification, and obtain the expected effect.It can ensure the overall detection effect in the process of application calculation.

  
  

• Keyword: deep learning algorithm; wireless sensor; abnormal data; detection;
• DOI: 10.12250/jpciams2022090513
• Citation form: Smith Cassiel.Wireless sensor anomaly data detection by a deep learning algorithm [J]. Computer Informatization and Mechanical System,2022,Vol.5,pp.57-59

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