An Area-Context-Based Credibility Detection for Big Data in IoT
Joint Authors
Zhao, Bo
Li, Xiang
Li, Jiayue
Zou, Jianwen
Liu, Yifan
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Telecommunications Engineering
Abstract EN
In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data.
Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible.
So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies.
The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms.
As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.
American Psychological Association (APA)
Zhao, Bo& Li, Xiang& Li, Jiayue& Zou, Jianwen& Liu, Yifan. 2020. An Area-Context-Based Credibility Detection for Big Data in IoT. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192403
Modern Language Association (MLA)
Zhao, Bo…[et al.]. An Area-Context-Based Credibility Detection for Big Data in IoT. Mobile Information Systems No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1192403
American Medical Association (AMA)
Zhao, Bo& Li, Xiang& Li, Jiayue& Zou, Jianwen& Liu, Yifan. An Area-Context-Based Credibility Detection for Big Data in IoT. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192403
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192403