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Electronics
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Ultra-Intelligent Computing and Communication for B5G and 6G Networks
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Ultra-Intelligent Computing and Communication for B5G and 6G Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 17922

Special Issue Editors

Prof. Nhu-Ngoc DAO

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul, Korea
Interests: network softwarization; mobile cloudization; intelligent systems; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sungrae Cho

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea
Interests: queuing system; wireless networking; ubiquitous computing; ICT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The mobile communication technology revolution is rapidly changing our daily lives, including breakthroughs in computing and telecommunications, based on information, communication and control theory, Turing’s computational model, and powerful processors. Future mobile networks such as beyond 5G (B5G) and 6G network generations promise to dramatically boost productivity. Emerging artificial intelligence (AI) technologies are bringing a new information revolution.  As the big global research world looks to AI to boost technological innovation and economic development, this Special Issue focuses on three key scenarios in AI research envisioning B5G and 6G services—autonomous and sensing systems, emergent intelligence, and collaborative and swarm intelligence. Specifically, this Special Issue is dedicated to harnessing the novel properties of super materials to develop devices that extend our senses, and with the real-time collection of massive data, supporting autonomous decision-making based on multi-source big data. Intelligent cognition strives to develop collaborative autonomous agents with cognitive capabilities, like those of humans, by simulating the neural mechanisms of cognitive behaviors, and to enhance the capabilities of processing big data and developing brain-like intelligence. Another focus is the collaboration between biological, mechanical, and electronic systems, as well as the mechanisms of self-organization and emergence for multi-agent collaboration and decision-making. The Special Issue promotes their transfer into practical applications, for developing intelligent mobile systems and pursuing artificial intelligence applications for mobile contents, platforms, networks and devices.

The Special Issue focuses on topics related to ultra-intelligent computing and communication, which include, but are not limited to

  • AI for Image Processing and Multimedia
  • AI Applications for Networking and IoT
  • AI for Control and Decision Systems
  • AI Applications for Mobile Communication
  • AI for Energy, Computer Systems and Semiconductor Device
  • AI for Data Analysis, Big Data and Cloud
  • AI for Neuroscience and Neuroengineering
  • AI for eHealth
  • AI Foundation

Prof. Nhu-Ngoc DAO
Prof. Dr. Sungrae Cho
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

  • AI for Image Processing and Multimedia
  • AI Applications for Networking and IoT
  • AI for Control and Decision Systems
  • AI Applications for Mobile Communication
  • AI for Energy, Computer Systems and Semiconductor Device
  • AI for Data Analysis, Big Data and Cloud
  • AI for Neuroscience and Neuroengineering
  • AI for eHealth
  • AI Foundation

Published Papers (6 papers)

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Research

15 pages, 3347 KiB  
Article
Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition
by Jae-Won Nam, Young-Kyun Cho and Youn Kyu Lee
Electronics 2022, 11(3), 408; https://doi.org/10.3390/electronics11030408 - 29 Jan 2022
Cited by 4 | Viewed by 3127
Abstract
An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor [...] Read more.
An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limited with regard to reflecting the intended operating conditions in the design parameters. Hereby, we propose what is termed a supervised learning artificial neural network (ANN) as a means by which to define an AMS regression model. This approach allows for rapid searches of complex design dimensions, including variations in performance metrics caused by process–voltage–temperature (PVT) changes. The method also reduces the considerable computation expense compared to that of simulation-program-with-integrated-circuit-emphasis (SPICE) simulations. Hence, the proposed AMS circuit design flow generates highly promising output to meet target requirements while showing an excellent ability to match the design target performance. To verify the potential and promise of our design flow, a successive approximation register analog-to-digital converter (SAR ADC) is designed with a 14 nm process design kit. In order to show the maximum single ADC performance (6-bit∼8-bit resolution and few GS/s conversion speed), we have set three different ADC performance targets. Under all SS/TT/FF corners, ±6.25% supply voltage variation, and temperature variation from −40 ∘C to 80 ∘C, the designed SAR ADC using our proposed AMS circuit optimization flow yields remarkable figure-of-merit energy efficiency (approximately 15 fJ/conversion step). Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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18 pages, 563 KiB  
Article
MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks
by Vinh Quoc Ta and Minho Park
Electronics 2021, 10(20), 2500; https://doi.org/10.3390/electronics10202500 - 14 Oct 2021
Cited by 7 | Viewed by 2923
Abstract
Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, [...] Read more.
Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies. Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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17 pages, 1111 KiB  
Article
An Empirical Evaluation of NVM-Aware File Systems on Intel Optane DC Persistent Memory Modules
by Guangyu Zhu, Jaehyun Han, Sangjin Lee and Yongseok Son
Electronics 2021, 10(16), 1977; https://doi.org/10.3390/electronics10161977 - 17 Aug 2021
Cited by 2 | Viewed by 2607
Abstract
The emergence of non-volatile memories (NVM) brings new opportunities and challenges to data management system design. As an important part of the data management systems, several new file systems are developed to take advantage of the characteristics of NVM. However, these NVM-aware file [...] Read more.
The emergence of non-volatile memories (NVM) brings new opportunities and challenges to data management system design. As an important part of the data management systems, several new file systems are developed to take advantage of the characteristics of NVM. However, these NVM-aware file systems are usually designed and evaluated based on simulations or emulations. In order to explore the performance and characteristics of these file systems on real hardware, in this article, we provide an empirical evaluation of NVM-aware file systems on the first commercially available byte-addressable NVM (i.e., the Intel Optane DC Persistent Memory Module (DCPMM)). First, to compare the performance difference between traditional file systems and NVM-aware file systems, we evaluate the performance of Ext4, XFS, F2FS, Ext4-DAX, XFS-DAX, and NOVA file systems on DCPMMs. To compare DCPMMs with other secondary storage devices, we also conduct the same evaluations on Optane SSDs and NAND-flash SSDs. Second, we observe how remote NUMA node access and device mapper striping affect the performance of DCPMMs. Finally, we evaluate the performance of the database (i.e., MySQL) on DCPMMs with Ext4 and Ext4-DAX file systems. We summarize several observations from the evaluation results and performance analysis. We anticipate that these observations will provide implications for various memory and storage systems. Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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13 pages, 2996 KiB  
Article
Measurement Study of Real-Time Virtual Reality Contents Streaming over IEEE 802.11ac Wireless Links
by Gusang Lee, Won Joon Yun, Yoo Jeong Ha, Soyi Jung, Jiyeon Kim, Sunghoon Hong, Joongheon Kim and Youn Kyu Lee
Electronics 2021, 10(16), 1967; https://doi.org/10.3390/electronics10161967 - 15 Aug 2021
Cited by 1 | Viewed by 2179
Abstract
Experience sharing among multiple users in virtual reality (VR) is one of the key applications in next generation wireless systems. In this VR application, one object can be reproduced as a virtual object based on recorded/captured multiple real-time images from multiple observation points. [...] Read more.
Experience sharing among multiple users in virtual reality (VR) is one of the key applications in next generation wireless systems. In this VR application, one object can be reproduced as a virtual object based on recorded/captured multiple real-time images from multiple observation points. At this time, VR applications require a lot of bandwidth to provide seamless services to users in wireless links, and thus, a certain level of data rates should be maintained. As the number of users increases, the server allocates more data rates to users on top of the limited bandwidth in wireless networks. At this time, users who utilize the VR streaming services will suffer from a lower quality, due to the limited bandwidth. This paper reports the measurement study and also analyzes the fluctuations in terms of the data rates as the number of users increases while sharing point cloud information in real-time authorized reality environments over IEEE 802.11ac wireless networks. Moreover, it measures and analyzes fluctuations in terms of frames-per-second and Jitters, which are practical quality reduction indicators. Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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15 pages, 3070 KiB  
Article
An Empirical Performance Evaluation of Multiple Intel Optane Solid-State Drives
by Jaehyun Han, Guangyu Zhu, Sangmook Lee and Yongseok Son
Electronics 2021, 10(11), 1325; https://doi.org/10.3390/electronics10111325 - 31 May 2021
Viewed by 2658
Abstract
Cloud computing as a service-on-demand architecture has grown in importance over the last few years. The storage subsystem in cloud computing has undergone enormous innovation to provide high-quality cloud services. Emerging Non-Volatile Memory Express (NVMe) technology has attracted considerable attention in cloud computing [...] Read more.
Cloud computing as a service-on-demand architecture has grown in importance over the last few years. The storage subsystem in cloud computing has undergone enormous innovation to provide high-quality cloud services. Emerging Non-Volatile Memory Express (NVMe) technology has attracted considerable attention in cloud computing by delivering high I/O performance in latency and bandwidth. Specifically, multiple NVMe solid-state drives (SSDs) can provide higher performance, fault tolerance, and storage capacity in the cloud computing environment. In this paper, we performed an empirical evaluation study of performance on recent NVMe SSDs (i.e., Intel Optane SSDs) with different redundant array of independent disks (RAID) environments. We analyzed multiple NVMe SSDs with RAID in terms of different performance metrics via synthesis and database benchmarks. We anticipate that our experimental results and performance analysis will have implications for various storage systems. Experimental results showed that the software stack overhead reduced the performance by up to 75%, 52%, 76%, 91%, and 92% in RAID 0, 1, 10, 5, and 6, respectively, compared with theoretical and expected performance. Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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15 pages, 762 KiB  
Article
Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms
by Soyi Jung, Won Joon Yun, Joongheon Kim and Jae-Hyun Kim
Electronics 2021, 10(5), 543; https://doi.org/10.3390/electronics10050543 - 25 Feb 2021
Cited by 8 | Viewed by 2537
Abstract
This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless [...] Read more.
This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements. Full article
(This article belongs to the Special Issue Ultra-Intelligent Computing and Communication for B5G and 6G Networks)
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