eprintid: 18965
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/89/65
datestamp: 2024-06-04 14:11:25
lastmod: 2024-06-04 14:11:25
status_changed: 2024-06-04 14:04:32
type: article
metadata_visibility: show
creators_name: Yalli, J.S.
creators_name: Hasan, M.H.
creators_name: Haron, N.S.
creators_name: Shaikh, M.U.R.
creators_name: Murad, N.Y.
creators_name: Bako, A.L.
title: Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions
ispublished: pub
keywords: Learning systems; Quality assurance; Statistics; Taxonomies, Anomaly; Confusion matrix; Internet of thing; Outlier; Quality of data; Quality of data assurance technique; Trustworthiness; WSN, Internet of things
note: cited By 0
abstract: The Internet of Things (IoT) data is the main component for finding the basis that allows decisions to be made intelligently and enables other services to be explored and used. Data originates from smart things that have the capabilities to connect and share data enormously with other things in the IoT ecosystem. However, the level of intelligence obtained and the type of services provided, all depend on whether the data is trusted or not. High-quality data is the most trusted;, it can be used to extract meaningful insights from an event and can also be used to provide good services to humans. Therefore, decisions based on high-quality and trusted data could be good, whereas those based on low-quality or untrusted data are not only bad but could also have severe consequences. The term Quality of Data (QoD) is used to represent data trustworthiness and is used throughout this paper. To the best of our knowledge, this work is the first to coin the term QoD. The problems that hinder QoD are identified and discussed. One if it is an outlier, it is a major feature of the data that degrades its overall quality. Several machine-learning techniques that detect outliers have been studied and presented, with few data-cleaning techniques. This paper aims to present the elements necessary to ensure QoD by presenting the overview of the IoT state-of-the-art. Then, data quality, data in IoT, and outliers are studied, and some quality assurance techniques that maintain data quality is presented. A comprehensive taxonomy is shown to provide state-of-the-art data in IoT. Open issues and future directions were suggested at the end of the paper. © (2023), (Science and Information Organization). All Rights Reserved.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183463578&doi=10.14569%2fIJACSA.2023.01412110&partnerID=40&md5=d3cb2edd28e6213f793483935b7cd6dc
id_number: 10.14569/IJACSA.2023.01412110
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 14
number: 12
pagerange: 1075-1091
refereed: TRUE
citation:   Yalli, J.S. and Hasan, M.H. and Haron, N.S. and Shaikh, M.U.R. and Murad, N.Y. and Bako, A.L.  (2023) Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions.  International Journal of Advanced Computer Science and Applications, 14 (12).  pp. 1075-1091.