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Energies
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Energy-Efficient AI-Empowered Communication Networks
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Energy-Efficient AI-Empowered Communication Networks

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (30 August 2021) | Viewed by 6471

Special Issue Editors

Prof. Dr. Sangheon Pack

E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea
Interests: 5G; IoT; vehicular networking; energy harvesting; simultaneous wireless information and power transfer (SWIPT); vehicle-to-grid (V2G); network softwarization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haneul Ko

E-Mail Website
Guest Editor
Department of Computer Convergence Software, Korea University, Sejong, Korea
Interests: 5G networks; network automation; mobile cloud computing; energy harvesting; SDN/NFV; future Internet

Special Issue Information

Dear Colleagues,

With an unprecedented high degree of heterogeneity of future networks in terms of services, device classes, mobility levels, and channel conditions, it is very challenging to manage resources while providing sufficient quality of service (QoS) to users by means of traditional resource management methods, especially when environments change dynamically. To mitigate this challenge, artificial intelligence (AI) technologies can be exploited. By means of AI technologies, we not only reduce manual network management interventions, but also enable more adaptive systems by predicting network environment changes in an autonomous fashion.

Even though a number of studies have been conducted in the literature, most of these studies focus on how to optimize the performance of networks by means of AI technologies. However, large amounts of data should be collected to exploit AI technologies in networks, which can cause high energy consumption in network elements (e.g., IoT devices, routers). In addition, AI agents require great amounts of energy in order to conduct complex computations. Therefore, it is important determine how to exploit AI technologies in networks in ways that do not require such high energy consumption.

The goal of this Special Issue is to disseminate knowledge regarding recent AI technologies, jointly considering performance and energy efficiency. Review and survey papers on these topics are also welcome.

Potential topics include, but are not limited to, the following:

  • Architecture and infrastructure for energy-efficient artificial intelligence in communication networks;
  • Energy-efficient AI-based network access control system in communication networks;
  • Energy-efficient AI-based rate control system in communication networks;
  • Energy-efficient AI-based caching system in communication networks;
  • Energy-efficient AI-based offloading system in communication networks;
  • Energy-efficient AI-based security system in communication networks;
  • Energy-efficient AI-based resource management system in communication networks;
  • Testbed/prototype for energy-efficient AI for communication networks;
  • Network theory for energy-efficient AI in communication

Prof. Dr. Sangheon Pack
Prof. Dr. Haneul Ko
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. Energies 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

  • energy-efficient artificial intelligence
  • energy-efficient network automation
  • energy-efficient networking
  • energy-efficient Internet of things (IoT)
  • energy-efficient cloud
  • energy-efficient edge and fog computing
  • energy-harvesting techniques

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Published Papers (2 papers)

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Research

12 pages, 614 KiB  
Article
Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers
by Janggoon Lee, Chanhee Park and Heejun Roh
Energies 2021, 14(4), 928; https://doi.org/10.3390/en14040928 - 10 Feb 2021
Cited by 3 | Viewed by 2720
Abstract
Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification [...] Read more.
Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps. Full article
(This article belongs to the Special Issue Energy-Efficient AI-Empowered Communication Networks)
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16 pages, 2408 KiB  
Article
Continuation Power Flow Based Distributed Energy Resource Hosting Capacity Estimation Considering Renewable Energy Uncertainty and Stability in Distribution Systems
by Hyun-Tae Kim, Jungju Lee, Myungseok Yoon, Moon-Jeong Lee, Namhun Cho and Sungyun Choi
Energies 2020, 13(17), 4367; https://doi.org/10.3390/en13174367 - 24 Aug 2020
Cited by 6 | Viewed by 3137
Abstract
Recently, the demand for electricity has been increasing worldwide. Thus, more attention has been paid to renewable energy. There are acceptable limits during the integration of renewable energy into distribution systems because there are many effects of integrating renewable energy. Unlike previous studies [...] Read more.
Recently, the demand for electricity has been increasing worldwide. Thus, more attention has been paid to renewable energy. There are acceptable limits during the integration of renewable energy into distribution systems because there are many effects of integrating renewable energy. Unlike previous studies that have estimated the distributed energy resource (DER) hosting capacity using the standard high voltage and probability approach, in this study, we propose an algorithm to estimate the DER hosting capacity by considering DER outages due to abrupt disturbances or uncertainties based on the generator ramp rate and voltage stability, which involves analysis of the low-voltage aspects. Furthermore, this method does not involve a complicated process or need large amounts of data to estimate the DER hosting capacity because it requires only minimum data for power flow. The proposed algorithm was applied to the IEEE-33 radial distribution system. According to the DER capacity, a voltage stability analysis based on continuation power flow (CPF) was conducted in a case of DER outage to estimate the DER hosting capacity in this case study. Thus, the DER hosting capacity was estimated for the IEEE-33 radial distribution system. Full article
(This article belongs to the Special Issue Energy-Efficient AI-Empowered Communication Networks)
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