Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics
Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/9/2139 |
| _version_ | 1811301211580661760 |
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| author | Guiling Sun Ziyang Zhang Bowen Zheng Yangyang Li |
| author_facet | Guiling Sun Ziyang Zhang Bowen Zheng Yangyang Li |
| author_sort | Guiling Sun |
| collection | DOAJ |
| description | Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced. |
| first_indexed | 2024-04-13T07:04:54Z |
| format | Article |
| id | doaj.art-dd0a0efcda7c42289c81fb1ed79551cd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:04:54Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dd0a0efcda7c42289c81fb1ed79551cd2022-12-22T02:57:02ZengMDPI AGSensors1424-82202019-05-01199213910.3390/s19092139s19092139Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved GeneticsGuiling Sun0Ziyang Zhang1Bowen Zheng2Yangyang Li3School of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaSchool of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaSchool of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaSchool of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaAiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.https://www.mdpi.com/1424-8220/19/9/2139greenhouseWSNsdata fusionimproved genetic algorithmtrust degreecubic exponential smoothing |
| spellingShingle | Guiling Sun Ziyang Zhang Bowen Zheng Yangyang Li Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics Sensors greenhouse WSNs data fusion improved genetic algorithm trust degree cubic exponential smoothing |
| title | Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics |
| title_full | Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics |
| title_fullStr | Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics |
| title_full_unstemmed | Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics |
| title_short | Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics |
| title_sort | multi sensor data fusion algorithm based on trust degree and improved genetics |
| topic | greenhouse WSNs data fusion improved genetic algorithm trust degree cubic exponential smoothing |
| url | https://www.mdpi.com/1424-8220/19/9/2139 |
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