J 2023

IoT Data Quality Issues and Potential Solutions: A Literature Review

MANSOURI, Taha, Mohammad Reza Sadeghi MOGHADAM, Fatemeh MONSHIZADEH and Ahad ZARERAVASAN

Basic information

Original name

IoT Data Quality Issues and Potential Solutions: A Literature Review

Authors

MANSOURI, Taha, Mohammad Reza Sadeghi MOGHADAM, Fatemeh MONSHIZADEH and Ahad ZARERAVASAN

Edition

COMPUTER JOURNAL, OXFORD (ENGLAND), Oxford University Press, 2023, 0010-4620

Other information

Language

English

Type of outcome

Article in a journal

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

is not subject to a state or trade secret

References:

Organization

Ekonomicko-správní fakulta – Repository – Repository

UT WoS

000756711200001

Keywords in English

data quality; Internet of Things (IoT); IoT data quality dimensions; IoT data quality issues; literature review

Links

EF16_027/0008360, research and development project.
Changed: 19/1/2024 03:19, RNDr. Daniel Jakubík

Abstract

V originále

In the Internet of Things (IoT), data gathered from dozens of devices are the base for creating business value and developing new products and services. If data are of poor quality, decisions are likely to be non-sense. Data quality is crucial to gain business value of the IoT initiatives. This paper presents a systematic literature review regarding IoT data quality from 2000 to 2020. We analyzed 58 articles to identify IoT data quality dimensions and issues and their categorizations. According to this analysis, we offer a classification of IoT data characterizations using the focus group method and clarify the link between dimensions and issues in each category. Manifesting a link between dimensions and issues in each category is incumbent, while this critical affair in extant categorizations is ignored. We also examine data security as an important data quality issue and suggest potential solutions to overcome IoT's security issues. The finding of this study proposes a new research discipline for additional examination for researchers and practitioners in determining data quality in the context of IoT.

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