TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing |
| 영문제목(English Title) |
Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing |
| 저자(Author) |
Xue-Yong Yu
Xin-Hui Guo
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| 원문수록처(Citation) |
VOL 14 NO. 10 PP. 3989 ~ 4006 (2020. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.
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| 키워드(Keyword) |
Incremental Principal Component Analysis
Offline and real-time learning
Fog Computing
Data anomaly detection
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| 파일첨부 |
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