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Super-giant and Hyperscale AI + Super Connected Network Technologies including Selected Papers from the 12th International Conference on Green and Human Information Technology

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 1991

Special Issue Editors

Prof. Dr. Byung-Seo Kim

E-Mail Website
Guest Editor
Department of Software and Communications Energy, Hongik University, Sejongro 2639, Republic of Korea
Interests: wireless networks and communications; WSN; wireless ICN; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Atif Ur Rehman

E-Mail Website
Guest Editor
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: distributed networking and computing; edge/fog/cloud computing; network protocol design; wireless/mobile communication; networking and computing for the metaverse
Special Issues, Collections and Topics in MDPI journals
Dr. Rehmat Ullah

E-Mail Website
Guest Editor
Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
Interests: federated learning; sensor networks; edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 12th International Conference on Green and Human Information Technology (ICGHIT 2024) will be held 23~25 January, 2024, in Hanoi, Vietnam (http://icghit.org/). The 12th International Conference on Green and Human Information Technology is a unique global conference for researchers, industry professionals, and academics who are interested in the latest development of green and human information technology. The main theme of ICGHIT’24 is “Towards of Super-Giant and Hyperscale Artificial Intelligent.” The latest Super-Giant and Hyperscale AI technologies are already pervading our daily lives, regardless of our recognition, and present us with major challenges and great opportunities at the same time. Centering around the main theme, ICGHIT’24 will provide an exciting program: hands-on experience-based tutorial sessions and special sessions covering research issues and directions with applications from both theoretical and practical viewpoints. The conference will also include plenary sessions, technical sessions, and workshops with special sessions. Highly qualified papers selected from ICGHIT 2024 will be invited to submit to this Special Issue. However, the Special Issue also welcomes submissions from general researchers which fit within the scope of the SI, which is Networking/Computing, Sensor-based Technologies, AI-based Smart Systems, etc.

Prof. Dr. Byung-Seo Kim
Dr. Muhammad Atif Ur Rehman
Dr. Rehmat Ullah
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 2600 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

  • AI
  • ML
  • sensor networking

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Published Papers (2 papers)

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Research

18 pages, 9113 KiB  
Article
Ensemble and Gossip Learning-Based Framework for Intrusion Detection System in Vehicle-to-Everything Communication Environment
by Muhammad Nadeem Ali, Muhammad Imran, Ihsan Ullah, Ghulam Musa Raza, Hye-Young Kim and Byung-Seo Kim
Sensors 2024, 24(20), 6528; https://doi.org/10.3390/s24206528 - 10 Oct 2024
Viewed by 792
Abstract
Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, [...] Read more.
Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes. Full article
(This article belongs to the Special Issue Super-giant and Hyperscale AI + Super Connected Network Technologies including Selected Papers from the 12th International Conference on Green and Human Information Technology)
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11 pages, 331 KiB  
Article
Deep Transfer Learning Method Using Self-Pixel and Global Channel Attentive Regularization
by Changhee Kang and Sang-ug Kang
Sensors 2024, 24(11), 3522; https://doi.org/10.3390/s24113522 - 30 May 2024
Viewed by 523
Abstract
The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, problems such as knowledge loss still occur during the process of transfer learning to [...] Read more.
The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention-based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the interior of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used datasets. Full article
(This article belongs to the Special Issue Super-giant and Hyperscale AI + Super Connected Network Technologies including Selected Papers from the 12th International Conference on Green and Human Information Technology)
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