Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN
Abstract Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis m...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-12-01
|
| Series: | EURASIP Journal on Information Security |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13635-023-00149-w |
| _version_ | 1797388137421340672 |
|---|---|
| author | Ming Li |
| author_facet | Ming Li |
| author_sort | Ming Li |
| collection | DOAJ |
| description | Abstract Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks. |
| first_indexed | 2024-03-08T22:36:29Z |
| format | Article |
| id | doaj.art-7bdf80052bde4a7db6d68b6cab0a9dea |
| institution | Directory Open Access Journal |
| issn | 2510-523X |
| language | English |
| last_indexed | 2024-03-08T22:36:29Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Information Security |
| spelling | doaj.art-7bdf80052bde4a7db6d68b6cab0a9dea2023-12-17T12:26:53ZengSpringerOpenEURASIP Journal on Information Security2510-523X2023-12-012023111310.1186/s13635-023-00149-wNode fault diagnosis algorithm for wireless sensor networks based on BN and WSNMing Li0Department of Mechanical and Electrical Engineering, Anhui Automobile Vocational and Technical CollegeAbstract Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.https://doi.org/10.1186/s13635-023-00149-wBayesian diagnostic modelWireless sensorConstrained maximum entropyUnion treeTest |
| spellingShingle | Ming Li Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN EURASIP Journal on Information Security Bayesian diagnostic model Wireless sensor Constrained maximum entropy Union tree Test |
| title | Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN |
| title_full | Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN |
| title_fullStr | Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN |
| title_full_unstemmed | Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN |
| title_short | Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN |
| title_sort | node fault diagnosis algorithm for wireless sensor networks based on bn and wsn |
| topic | Bayesian diagnostic model Wireless sensor Constrained maximum entropy Union tree Test |
| url | https://doi.org/10.1186/s13635-023-00149-w |
| work_keys_str_mv | AT mingli nodefaultdiagnosisalgorithmforwirelesssensornetworksbasedonbnandwsn |