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Applied Sciences
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Advances in Applied Smart Mobile Media & Network Computing
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Advances in Applied Smart Mobile Media & Network Computing

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 (31 January 2023) | Viewed by 11018

Special Issue Editors

Prof. Jinsul Kim

E-Mail Website
Guest Editor
Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea
Interests: smart mobile computing; multimedia QoS/QoE; next generation mobile networking; mobile edge computing; deep learning neural networks; intelligent network design; new media processing
Special Issues, Collections and Topics in MDPI journals
Prof. Tai-Won Um

E-Mail Website
Guest Editor
College of Science and Technology, Duksung Women’s University, Seoul 01369, Korea
Interests: IoT; digital twin; cyber-physical system; trust information management; deep reinforcement learning
Prof. Dr. Gyu Myoung Lee

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: data processing and management; data sharing and analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on new media systems, smart edge computing, and networking-based research and breakthroughs. Mobile edge computing, cloud computing, and artificial intelligence are now essential parts of handling the data volumes generated by new media and smart devices. Further 5G/6G enhancements, such as but not limited to network slicing, non-public networks, and core networking infrastructures, will ultimately help to realize the vision of a global network. Various applied strategies and systems, or more specifically new media and smart computing, will provide a pathway for modern research and applied sciences. Applied new media management/ processing, AR/VR technology and applications with mobile content computing and networking are crucial to facilitate new advances in this section of applied sciences. In this Special Issue, we will present recent advances in applied new media systems, smart computing, and networking-based research and breakthroughs. We are aiming to publish the latest, and most technically sound research articles that demonstrate theoretical and practical contributions to smart mobile media and network computing.

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

  • Cloud computing-based optimization;
  • Mobile edge computing;
  • Applied new media management/ processing;
  • AR/VR new media technology and applications;
  • Mobile content computing and networking;
  • New media QoS/QoE analysis and evaluations;
  • Deep learning approaches for new media processing;
  • Smart networking and mobile communications;
  • Applied computer vision for new media;
  • Applied machine learning approaches to utilize smart computing and networking;
  • IoT based Opportunities, benefits, and use cases in applications;
  • Digital twin and cyber-physical system;
  • Large datasets on new media analysis;
  • Current state-of-the-art and future trends of new media and smart mobile computing-based applications;
  • New generation broadcasting, streaming and telecommunication convergence media;
  • Next-generation 5G/6G network and communication;
  • Edge-based multimedia processing in real-time;
  • Applied augmented vision and communications in edge services;
  • Emerging and innovative convergence technology and applications.

Prof. Jinsul Kim
Prof. Tai-Won Um
Prof. Gyu Myoung Lee
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

  • smart mobile computing
  • AR/VR new media technology
  • media QoS/QoE
  • deep learning, broadcasting and telecommunication convergence media
  • multimedia processing
  • intelligent network design
  • 5G/6G networks based media transmission
  • innovative convergence technology

Published Papers (5 papers)

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Research

14 pages, 1578 KiB  
Article
Designing a Vertical Handover Algorithm for Security-Constrained Applications
by Omar Khattab, Murad Khan and Basil Alothman
Appl. Sci. 2023, 13(4), 2166; https://doi.org/10.3390/app13042166 - 08 Feb 2023
Cited by 2 | Viewed by 1413
Abstract
In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the [...] Read more.
In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the global system for mobile communication (GSM), and the universal mobile telecommunications system (UMTS). In the literature, many recent VHO research works have been proposed in which a number of critical issues still arise when performing seamless vertical handover, including VHO packet loss, VHO delay, and VHO throughput. Moreover, the security aspect of triggering VHO has not been carefully considered, particularly when an MU intends to use a payment application. In this paper, we present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account all the above issues, for co-located Wi-Fi and UMTS networks. The simulation results are compared with the media independent handover (MIH) IEEE 802.21 standard, which facilitates the seamless transfer of connection among heterogeneous networks. We show that the proposed algorithm outperforms the MIH standard in terms of VHO delay, VHO packet loss, and VHO throughput in providing uninterrupted connection to security-constrained applications. Full article
(This article belongs to the Special Issue Advances in Applied Smart Mobile Media & Network Computing)
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15 pages, 1578 KiB  
Article
Stride-TCN for Energy Consumption Forecasting and Its Optimization
by Le Hoang Anh, Gwang Hyun Yu, Dang Thanh Vu, Jin Sul Kim, Jung Il Lee, Jun Churl Yoon and Jin Young Kim
Appl. Sci. 2022, 12(19), 9422; https://doi.org/10.3390/app12199422 - 20 Sep 2022
Cited by 2 | Viewed by 1327
Abstract
Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance [...] Read more.
Forecasting, commonly used in econometrics, meteorology, or energy consumption prediction, is the field of study that deals with time series data to predict future trends. Former studies have revealed that both traditional statistical models and recent deep learning-based approaches have achieved good performance in forecasting. In particular, temporal convolutional networks (TCNs) have proved their effectiveness in several time series benchmarks. However, presented TCN models are too heavy to deploy on resource-constrained systems, such as edge devices. As a resolution, this study proposes a stride–dilation mechanism for TCN that favors a lightweight model yet still achieves on-pair accuracy with the heavy counterparts. We also present the Chonnam National University (CNU) Electric Power Consumption dataset, the dataset of energy consumption measured at CNU by smart meters every hour. The experimental results indicate that our best model reduces the mean squared error by 32.7%, whereas the model size is only 1.6% compared to the baseline TCN. Full article
(This article belongs to the Special Issue Advances in Applied Smart Mobile Media & Network Computing)
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14 pages, 1221 KiB  
Article
Trust Management for Artificial Intelligence: A Standardization Perspective
by Tai-Won Um, Jinsul Kim, Sunhwan Lim and Gyu Myoung Lee
Appl. Sci. 2022, 12(12), 6022; https://doi.org/10.3390/app12126022 - 14 Jun 2022
Cited by 5 | Viewed by 2585
Abstract
With the continuous increase in the development and use of artificial intelligence systems and applications, problems due to unexpected operations and errors of artificial intelligence systems have emerged. In particular, the importance of trust analysis and management technology for artificial intelligence systems is [...] Read more.
With the continuous increase in the development and use of artificial intelligence systems and applications, problems due to unexpected operations and errors of artificial intelligence systems have emerged. In particular, the importance of trust analysis and management technology for artificial intelligence systems is continuously growing so that users who desire to apply and use artificial intelligence systems can predict and safely use services. This study proposes trust management requirements for artificial intelligence and a trust management framework based on it. Furthermore, we present challenges for standardization so that trust management technology can be applied and spread to actual artificial intelligence systems. In this paper, we aim to stimulate related standardization activities to develop globally acceptable methodology in order to support trust management for artificial intelligence while emphasizing challenges to be addressed in the future from a standardization perspective. Full article
(This article belongs to the Special Issue Advances in Applied Smart Mobile Media & Network Computing)
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15 pages, 2549 KiB  
Article
Improved Q Network Auto-Scaling in Microservice Architecture
by Yeonggwang Kim, Jaehyung Park, Junchurl Yoon and Jinsul Kim
Appl. Sci. 2022, 12(3), 1206; https://doi.org/10.3390/app12031206 - 24 Jan 2022
Cited by 3 | Viewed by 2525
Abstract
Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as [...] Read more.
Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service. A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning. A common approach used in both commercial and open-source computing platforms is workload-based automatic scaling, which expands instances by increasing the number of incoming requests. Concurrency is a request-based policy that has recently been proposed in the evolving microservice framework; in this policy, the algorithm can expand its resources to the maximum number of configured requests to be processed in parallel per instance. However, it has proven difficult to identify the concurrency configuration that provides the best possible service quality, as various factors can affect the throughput and latency based on the workloads and complexity of the infrastructure characteristics. Therefore, this study aimed to investigate the applicability of an artificial intelligence approach to request-based auto-scaling in the microservice framework. Our results showed that the proposed model could learn an effective expansion policy within a limited number of pods, thereby showing an improved performance over the underlying auto expansion configuration. Full article
(This article belongs to the Special Issue Advances in Applied Smart Mobile Media & Network Computing)
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13 pages, 5372 KiB  
Article
Switched Low-Noise Amplifier Using Gyrator-Based Matching Network for TD-LTE/LTE-U/Mid-Band 5G and WLAN Applications
by Ching-Han Tsai, Chun-Yi Lin, Ching-Piao Liang, Shyh-Jong Chung and Jenn-Hwan Tarng
Appl. Sci. 2021, 11(4), 1477; https://doi.org/10.3390/app11041477 - 06 Feb 2021
Cited by 1 | Viewed by 1808
Abstract
This paper presents a triple-band low-noise amplifier (LNA) fabricated using a 0.18 μm Complementary Metal-Oxide-Semiconductor (CMOS) process. The LNA uses a double-peak load network with a switched component to accomplish the triple-band operation. Moreover, noise reduction using a substrate resistor to ameliorate the [...] Read more.
This paper presents a triple-band low-noise amplifier (LNA) fabricated using a 0.18 μm Complementary Metal-Oxide-Semiconductor (CMOS) process. The LNA uses a double-peak load network with a switched component to accomplish the triple-band operation. Moreover, noise reduction using a substrate resistor to ameliorate the noise performance is presented. Noise reduction of 1.5 dB can be achieved at 2.5 GHz without additional dc power and extra manufacturing costs. An input matching technique is realized simultaneously using a gyrator-based feedback topology. The triple-band LNA can be realized by using a dual-band input network with a switched matching mechanism. The target frequencies of the triple-band LNA are 2.3–2.7 GHz, 3.4–3.8 GHz, and 5.1–5.9 GHz, covering the operating frequency bands of time-division long-term evolution (TD-LTE), mid-band Fifth-generation (5G), LTE-unlicensed (LTE-U) band, and Wireless LAN (WLAN) technology. The measured power gains and noise figures at 2.5, 3.5, and 5.2 GHz are 12.3, 15.3, and 13.1 dB and 2.3, 2.2, and 2.6 dB, respectively. Full article
(This article belongs to the Special Issue Advances in Applied Smart Mobile Media & Network Computing)
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