文章基本信息

标题：DRAV: Detection and repair of data availability violations in Internet of Things
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作者：Jinlin Wang ; Haining Yu ; Xing Wang 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：11
页码：1
DOI：10.1177/1550147719889899
出版社：Hindawi Publishing Corporation
摘要：The application of the Internet of Things has produced large amounts of data in different scenarios, which are accompanied with problems, such as consistency and integrity violations. Existing research on dealing with data availability violations is insufficient. In this work, the detection and repair of data availability violations (DRAV) framework is proposed to detect and repair data violations in Internet of Things with a distributed parallel computing environment. DRAV uses algorithms in the MapReduce programming framework, and these include detection and repair algorithms based on enhanced conditional function dependency for data consistency violation, MapJoin, and ReduceJoin algorithms based on master data for k-nearest neighbor–based integrity violation detection, and repair algorithms. Experiments are conducted to determine the effect of the algorithms. Results show that DRAV improves data availability in Internet of Things compared with existing methods by detecting and repairing violations.
关键词：Internet of things; data availability; violation detection; rule-based repair; conditional functional dependency; MapReduce