An intelligent signal processing method against impulsive noise interference in AIoT
Abstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-10-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-023-01061-8 |
| _version_ | 1827632741128077312 |
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| author | Bin Wang Ziyan Jiang Yanjing Sun Yan Chen |
| author_facet | Bin Wang Ziyan Jiang Yanjing Sun Yan Chen |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Abstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things. |
| first_indexed | 2024-03-09T14:49:49Z |
| format | Article |
| id | doaj.art-ee21a8983e714e95a20e5cc26f76f264 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-03-09T14:49:49Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-ee21a8983e714e95a20e5cc26f76f2642023-11-26T14:33:09ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802023-10-012023111810.1186/s13634-023-01061-8An intelligent signal processing method against impulsive noise interference in AIoTBin Wang0Ziyan Jiang1Yanjing Sun2Yan Chen3School of Communication and Information Engineering, Xi’an University of Science and TechnologySchool of Communication and Information Engineering, Xi’an University of Science and TechnologySchool of Information and Control Engineering, China University of Mining and TechnologyResearch Center for Intelligent Transportation, Zhejiang LabAbstract In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.https://doi.org/10.1186/s13634-023-01061-8AIoTIntelligent signal processingDeep learningImpulsive noise |
| spellingShingle | Bin Wang Ziyan Jiang Yanjing Sun Yan Chen An intelligent signal processing method against impulsive noise interference in AIoT EURASIP Journal on Advances in Signal Processing AIoT Intelligent signal processing Deep learning Impulsive noise |
| title | An intelligent signal processing method against impulsive noise interference in AIoT |
| title_full | An intelligent signal processing method against impulsive noise interference in AIoT |
| title_fullStr | An intelligent signal processing method against impulsive noise interference in AIoT |
| title_full_unstemmed | An intelligent signal processing method against impulsive noise interference in AIoT |
| title_short | An intelligent signal processing method against impulsive noise interference in AIoT |
| title_sort | intelligent signal processing method against impulsive noise interference in aiot |
| topic | AIoT Intelligent signal processing Deep learning Impulsive noise |
| url | https://doi.org/10.1186/s13634-023-01061-8 |
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