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Special Issue "Artificial Intelligence Applications to the Internet of Things"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 1168

Special Issue Editors

Special Issue Information

Dear Colleagues,

Internet of Things systems rely on the collection of data from devices that interact both with each other and with the world around them. In order for these data to be transformed into valuable and actionable knowledge, it is necessary to integrate technologies that enable this process, such as machine learning or data analytics. The aim of this Special Issue is to collect high-quality papers that successfully apply techniques from the field of artificial intelligence to the IoT in real-world scenarios.

Potential topics include but are not limited to the following:

  • Artificial Intelligence applications to the IoT;
  • Machine learning applications to the IoT;
  • Data analytics applied to the IoT;
  • Visual analytics and visualization applied to the IoT;
  • Computer vision applications in the IoT;
  • Multiagent system applications in the IoT;
  • Industrial IoT (IIoT).

Dr. Pablo Chamoso
Dr. Guillermo Hernández
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • ambient intelligence
  • multiagent systems
  • computer vision
  • data analytics
  • Internet of Things

Published Papers (1 paper)

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Research

Article
WYSIWYG: IoT Device Identification Based on WebUI Login Pages
Sensors 2022, 22(13), 4892; https://doi.org/10.3390/s22134892 - 29 Jun 2022
Viewed by 772
Abstract
With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important [...] Read more.
With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to the Internet of Things)
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