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Electronics
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AIoT and Mobile Networking
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AIoT and Mobile Networking

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 5363

Special Issue Editors

Prof. Dr. Young-Joo Suh

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), San 31, Hyoja-Dong, Pohang 790-784, Republic of Korea
Interests: wireless LAN MAC protocols; Internet of Things (IoT); ad-hoc and sensor networks; indoor positioning system (IPS); activity recognition using wireless signals
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Young Deok Park

E-Mail Website
Guest Editor
Department of Computer Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 42415, Korea
Interests: IoT; network intelligence; mobile networking

Special Issue Information

Dear Colleagues,

AIoT is the convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. AIoT is a technology that collects useful data by utilizing IoT infrastructure and draws insight from those data. Empowered by AI, IoT enables a variety of services across a myriad of fields from smart healthcare and personalized recommendation systems to intelligent management and large-scale surveillance systems for cities and industries including manufacturing and agriculture.

Beyond the digital transformation of services, AIoT aims to make everything intelligent and autonomous.

Despite a great amount of effort made regarding applying AI to IoT, AIoT faces technical challenges. Network systems must support ultra-high bandwidth, low latency, reliable connections, and flexible resource allocation to keep pace with the explosive growth of IoT traffic. Designing scalable multitenant AIoT platforms and frameworks is challenging due to AI-driven workloads and device diversity. Ensuring data privacy and eliminating security vulnerabilities in edge clouds and end devices with low computing power are also important challenges.

Topics include but are not limited to the following.

  • Mobile and wireless communication/networking for AIoT;
  • Resource management for AIoT;
  • Design and implementation of AIoT platform and framework;
  • Machine learning for AIoT;
  • Security, privacy, and reliability in AIoT;
  • Edge computing/network for AIoT;
  • Big data governance and analytics for AIoT;
  • Blockchain-based AIoT solutions;
  • Smart home, smart factory and smart city;
  • 5G NR and beyond 5G NR technologies for AIoT;
  • Recent trends in applications and services based on combining IoT and AI technology.

Prof. Dr. Young-Joo Suh
Prof. Dr. Young Deok Park
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. Electronics 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
  • Internet of Things
  • AIoT
  • mobile networking
  • AIoT applications

Published Papers (3 papers)

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Research

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12 pages, 694 KiB  
Article
Glocal Retriever: Glocal Image Retrieval Using the Combination of Global and Local Descriptors
by Zeu Kim, Youngin Kim and Young-Joo Suh
Electronics 2023, 12(2), 442; https://doi.org/10.3390/electronics12020442 - 14 Jan 2023
Viewed by 1957
Abstract
Development of deep learning has led to progress in computer vision, including metric learning tasks such as image retrieval, through convolutional neural networks. In image retrieval, the metric distance (i.e., the similarity) between the images needs to be computed and then compared to [...] Read more.
Development of deep learning has led to progress in computer vision, including metric learning tasks such as image retrieval, through convolutional neural networks. In image retrieval, the metric distance (i.e., the similarity) between the images needs to be computed and then compared to return similar images. Global descriptors are good at extracting holistic features of an image, such as the overall shape of the main object and the silhouette. On the other hand, the local features extract the detailed features which the model uses to help classify similar images together. This paper proposes a descriptor mixer which takes advantage of both local and global descriptors (group of features combined into one) as well as different types of global descriptors for an effect of a lighter version of an ensemble of models (i.e., fewer parameters and smaller model size than those of actual ensemble of networks). As a result, the model’s performance improved about 1.36% (recall @ 32) when the combination of the descriptors were used. We empirically found out that the combination of GeM and MAC achieved the highest performance. Full article
(This article belongs to the Special Issue AIoT and Mobile Networking)
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17 pages, 2737 KiB  
Article
Impact Analysis of Emerging Semantic Communication Systems on Network Performance
by Harim Lee, Hyeongtae Ahn and Young Deok Park
Electronics 2022, 11(10), 1567; https://doi.org/10.3390/electronics11101567 - 13 May 2022
Viewed by 1750
Abstract
With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication [...] Read more.
With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication focuses on the successful delivery of transmitted messages such as human language communication. In order to realize such new communication, both transmitter and receiver should share the same background knowledge with each other. Recently, several researchers have developed task-specific SC systems by exploiting astonishing achievements in deep learning, which can allow the same knowledge to be shared between them. However, since such SC systems are specialized to handle specific applications, not all users can be serviced by the SC systems. Therefore, a network will face a coexistence of an SC system and a traditional communication (TC) system. In this paper, we investigate how introducing emerging SC systems affects the performance of the TC system from a network perspective. For analysis, we consider the signal-to-noise ratio (SNR) differently for the user served by an SC system and the user served by a TC system. Then, by using two different SNR equations, we formulate a max-min fairness problem in the coexistence of SC and TC systems. Via extensive numerical results, we compare the network performance of TC and SC users with and without SC systems, and then confirm that SC systems are indeed a promising next-generation communication alternative. Full article
(This article belongs to the Special Issue AIoT and Mobile Networking)
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Other

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13 pages, 2640 KiB  
Technical Note
A New Link Adaptation Technique for Very High Frequency Data Exchange System in Future Maritime Communication
by Wooseong Shim, Buyoung Kim, Eui-Jik Kim and Dongwan Kim
Electronics 2024, 13(2), 323; https://doi.org/10.3390/electronics13020323 - 11 Jan 2024
Viewed by 687
Abstract
The growing demand for communication technology capable of providing high transmission rates in maritime environments has led to the exploration of the very high frequency (VHF) data exchange system (VDES) as a promising solution. The VDES, the integration of an automatic identification system [...] Read more.
The growing demand for communication technology capable of providing high transmission rates in maritime environments has led to the exploration of the very high frequency (VHF) data exchange system (VDES) as a promising solution. The VDES, the integration of an automatic identification system (AIS), application-specific messaging (ASM), and VHF data exchange (VDE), offers improved transmission rates and stable connections compared with existing technologies. Although the VDES supports high transmission rates through various modulation and coding scheme (MCS) technologies, it lacks a standardized mechanism for controlling MCS parameters and relies on user algorithms for operation. In this paper, we introduce the maritime auto-rate fall-back (mARF) technology, designed to effectively address the challenges of maritime communication scenarios using the MCS framework provided by the VDES. mARF technology incorporates fast drop-out and recovery mechanisms to swiftly adapt to changing MCS types in the presence of deep nulls, a common occurrence in maritime communication environments. These adaptive thresholds for fast drop-out and recovery operations are dynamically learned using historical communication data. Through extensive simulations, we demonstrate the effectiveness of mARF in enhancing the MCS control capabilities of the VDES. Our results show a significant performance improvement of 18% compared to the existing model, validating the potential of mARF in optimizing maritime communication channels and supporting a high transmission rate. Full article
(This article belongs to the Special Issue AIoT and Mobile Networking)
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