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Applied Sciences
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Trustworthy and Secure Artificial Intelligence Techniques for the Internet of...
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Trustworthy and Secure Artificial Intelligence Techniques for the Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 2790

Special Issue Editors

Prof. Dr. Shigeng Zhang

E-Mail Website
Guest Editor
School of Information Science and Engineering, Central South University, Changsha, China
Interests: internet of things; IoT security; AI security; wireless sensing
Dr. Hejun Wu

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
Interests: wireless sensor networks; internet of things; artificial intelligence and robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of the Internet of Things (IoT) has posed a large number of complex problems that are difficult to address with traditional techniques/algorithms, e.g., industrial process monitoring and optimization, security monitoring, or security-related issues in the IoT. Due to their superior performance in many such tasks, Artificial Intelligence techniques, represented by deep learning in recent years, have been widely adopted in many IoT applications. However, while providing new dimensions in designing efficient and effective solutions for the Internet of Things, the applications of AI techniques in the IoT might also introduce new vulnerabilities or suffer from the essential untrustworthiness of many AI techniques, e.g., adversarial example attacks. Especially for many safety-critic IoT applications, it is necessary to guarantee that the application of AI techniques does not generate new attack surfaces when improving efficiency. This Special Issue solicits submissions from areas relating to trustworthy and secure AI technique applications in the Internet of Things, including how to design solutions based on trustworthy AI techniques, how to defend against potential attacks to IoT applications caused by AI algorithm vulnerabilities, and how to adapt existing AI techniques in IoT applications with the guarantee of security and trustworthiness. The interested topics include but are not limited to: 

  • Application of trustworthy AI techniques in the IoT
  • Analysis of AI vulnerabilities in the IoT
  • AI security in edge computing/IoT
  • AI-based security in the IoT
  • AI-based attacks in the IoT
  • AI-driven performance optimization for the IoT
  • Security analysis of AI in the IoT 

Prof. Dr. Shigeng Zhang
Dr. Hejun Wu
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. Applied Sciences 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

  • trustworthy AI in the IoT
  • AI security
  • AI-driven security of the IoT
  • IoT security
  • AI application in the IoT

Published Papers (2 papers)

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Research

17 pages, 924 KiB  
Article
PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
by Yalong Xiao, Junfeng Zhu, Shuping Yan, Hong Song and Shigeng Zhang
Appl. Sci. 2023, 13(13), 7458; https://doi.org/10.3390/app13137458 - 23 Jun 2023
Viewed by 1090
Abstract
Wireless sensing has emerged as an innovative technology that enables many smart applications such as indoor localization, activity recognition, and user tracking. However, achieving reliable and precise results in wireless sensing requires an accurate distinction between line-of-sight and non-line-of-sight transmissions. This paper introduces [...] Read more.
Wireless sensing has emerged as an innovative technology that enables many smart applications such as indoor localization, activity recognition, and user tracking. However, achieving reliable and precise results in wireless sensing requires an accurate distinction between line-of-sight and non-line-of-sight transmissions. This paper introduces PEiD, a novel method that utilizes low-cost WiFi devices for transmission path identification, offering real-time measurements with high accuracy through the application of machine-learning-based classifiers. To overcome the deficiencies of commodity WiFi in bandwidth, PEiD explores the peak energy index distribution extracted from the channel impulse responses. Our approach effectively captures the inherent randomness of channel properties and significantly reduces the number of samples required for identification, thus surpassing previous methods. Additionally, to tackle the challenge of mobility, a sliding window technique is also adopted to achieve continuous monitoring of transmission path status. According to our extensive experiments, PEiD can attain a best path identification accuracy of 97.5% for line-of-sight scenarios and 94.3% for non-line-of-sight scenarios, with an average delay of under 300 ms (92% accuracy) even in dynamic environments. Full article
(This article belongs to the Special Issue Trustworthy and Secure Artificial Intelligence Techniques for the Internet of Things)
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14 pages, 1303 KiB  
Article
Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems
by Xizhan Gao, Wei Hu, Yu Chu and Sijie Niu
Appl. Sci. 2023, 13(7), 4209; https://doi.org/10.3390/app13074209 - 26 Mar 2023
Viewed by 1058
Abstract
At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data [...] Read more.
At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems. Full article
(This article belongs to the Special Issue Trustworthy and Secure Artificial Intelligence Techniques for the Internet of Things)
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