DRAV: Detection and repair of data availability violations in Internet of Things. (November 2019)

• Record Type: Journal Article
• Authors: Wang, Jinlin; Yu, Haining; Wang, Xing; Zhang, Hongli; Fang, Binxing; Yang, Yuchen; Zhu, Xiaozhou
• Is Part Of: International journal of distributed sensor networks. Volume 15:Number 11(2019)
• Publication Date: 2019-11
• Subjects: Internet of things -- data availability -- violation detection -- rule-based repair -- conditional functional dependency -- MapReduce; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147719889899
• Languages: English
• ISSNs: 1550-1329

Abstract

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.