DRAV: Detection and repair of data availability violations in Internet of Things

Author:

Wang Jinlin1ORCID,Yu Haining1,Wang Xing1,Zhang Hongli1,Fang Binxing1,Yang Yuchen1,Zhu Xiaozhou1

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

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.

Funder

National Natural Science Foundation of China

national basic research program of china

Publisher

SAGE Publications

Subject

Computer Networks and Communications,General Engineering

Reference41 articles.

1. Delaney K, Nicole F, Kapur A, et al. Internet of things: challenges, breakthroughs and best practices. Technical Report, Cisco Systems, Inc, San Jose, CA, November 2017.

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