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AI Technology and Wireless Advanced Networking
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AI Technology and Wireless Advanced Networking

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 (25 February 2022) | Viewed by 8004

Special Issue Editors

Prof. Dr. Lin-Huang Chang

E-Mail Website
Guest Editor
Department of Computer Science, National Taichung University of Education, Taichung 40306, Taiwan
Interests: AI; machine learning; IoT; wireless; SDN; 5G networks; network slicing; dynamic distributed networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jenq-Neng Hwang

E-Mail
Guest Editor
Electrical and Computer Engineering, University of Washington, Seattle, WA, USA

Special Issue Information

Dear Colleagues,

This Special Issue discusses the state of the art for emerging research, applications, and problems in the fields of artificial-intelligence technology and wireless advanced networking. The explosive data growth from wireless and advanced networks can be highly unstructured, heterogeneous, and unpredictable. AI techniques have been applied in almost every domain. Security issues such as network-intrusion-detection problems in the Internet of Things (IoT), intelligent transportation systems (ITSs), and wireless advanced networking systems are significantly concerning. Emerging technologies and issues in the areas of ubiquitous services, wireless and multimedia applications, and networking issues for 5G/beyond 5G are also all within the scope of this Special Issue, which focuses on prospective technologies, models, systems, and applications in AI, IoT, ITS, 5G, and advanced networking. The aim is to collect the most recent advances in artificial-intelligence research for wireless advanced networking. Accordingly, the Special Issue welcomes methods and ideas that emphasize the impact of artificial intelligence on wireless-advanced-networking technologies.

The topics of interest for this Special Issue include, but are not limited to:

artificial intelligence (AI);

machine learning and deep learning;

innovative AI incentive schemes;

distributed AI algorithms and techniques;

5G and wireless advanced networking;

intelligent transportation systems (ITSs);

ultrareliable low-latency communication;

Internet of Things (IoT) and wireless-sensor networks (WSNs);

software-defined-networking (SDN) technologies, systems, and architectures;

cloud, fog, and mobile-edge computing systems;

fault-tolerant issues in advanced networking;

security for IoT, WSN, SDN, 5G, and AI applications;

QoS/QoE performance evaluation;

and dependable and secure computing.

Prof. Dr. Lin-Huang Chang
Prof. Dr. Jenq-Neng Hwang
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

  • Artificial intelligence (AI)
  • Machine learning (ML)
  • 5G
  • Wireless advanced networking
  • Intelligent transportation systems (itss)
  • Dependable and secure computing

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Published Papers (2 papers)

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Research

13 pages, 6101 KiB  
Article
A Two-Phase Fashion Apparel Detection Method Based on YOLOv4
by Chu-Hui Lee and Chen-Wei Lin
Appl. Sci. 2021, 11(9), 3782; https://doi.org/10.3390/app11093782 - 22 Apr 2021
Cited by 13 | Viewed by 4679
Abstract
Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for [...] Read more.
Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection. Full article
(This article belongs to the Special Issue AI Technology and Wireless Advanced Networking)
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34 pages, 7487 KiB  
Article
A Traceable and Authenticated IoTs Trigger Event of Private Security Record Based on Blockchain
by Chin-Ling Chen, Zi-Yi Lim, Hsien-Chou Liao and Yong-Yuan Deng
Appl. Sci. 2021, 11(6), 2843; https://doi.org/10.3390/app11062843 - 22 Mar 2021
Cited by 2 | Viewed by 2480
Abstract
Recently, private security services have become increasingly needed by the public. The proposed scheme involves blockchain technology with a smart contract. When a private security company signs a contract with a client, they install an Internet of Things (IoTs) device in the client’s [...] Read more.
Recently, private security services have become increasingly needed by the public. The proposed scheme involves blockchain technology with a smart contract. When a private security company signs a contract with a client, they install an Internet of Things (IoTs) device in the client’s house and connect it with the IoT main controller; then, the IoT main controller connects to the security control center (SCC). Once there is an event triggered (e.g., a break-in or fire incident) by the IoTs device, the controller sends a message to the SCC. The SCC allocates a security guard (SG) to the incident scene immediately. After the task is accomplished, the SG sends a message to the SCC. All of these record the messages and events chained in the blockchain center. The proposed scheme makes security event records have the following characteristics: authenticated, traceable, and integral. The proposed scheme is proved by a security analysis with mutual authentication, traceability, integrity, and non-repudiation. The known attacks (e.g., man-in-the-middle attack, replay attack, forgery attack) are avoided by message encryption and a signing mechanism. Threat models in the communication phase can also be avoided. Finally, computation cost, communication performance, and comparison with related works are also discussed to prove its applicability. We also provide an arbitration mechanism, so that the proposed scheme can reduce disputes between private security companies and the client. Full article
(This article belongs to the Special Issue AI Technology and Wireless Advanced Networking)
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