Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting

Elmaghbub, Abdurrahman; Hamdaoui, Bechir; Wong, Weng‐Keen

doi:10.1109/access.2023.3305257

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…In IoT visions, things share data automatically with neighboring nodes based on their configuration. Domain-agnostic data cleaning methods confirm that data transmitted between "things" is uninterrupted, without human involvement, and with minimal human control, which is essential to the creation of a seamless IoT service [125].…”

Section: Domain-agnostic/automated Verificationmentioning

confidence: 99%

Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

Yalli,

Hasan,

Haron

et al. 2023

IJACSA

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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.

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Section: Domain-agnostic/automated Verificationmentioning

confidence: 99%