Vol. 14, No. 10, October 31, 2020
10.3837/tiis.2020.10.004,
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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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Cite this article
[IEEE Style]
X. Yu and X. Guo, "Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing," KSII Transactions on Internet and Information Systems, vol. 14, no. 10, pp. 3989-4006, 2020. DOI: 10.3837/tiis.2020.10.004.
[ACM Style]
Xue-Yong Yu and Xin-Hui Guo. 2020. Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing. KSII Transactions on Internet and Information Systems, 14, 10, (2020), 3989-4006. DOI: 10.3837/tiis.2020.10.004.
[BibTeX Style]
@article{tiis:23916, title="Data anomaly detection and Data fusion based on Incremental Principal Component Analysis in Fog Computing", author="Xue-Yong Yu and Xin-Hui Guo and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.10.004}, volume={14}, number={10}, year="2020", month={October}, pages={3989-4006}}