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Energy-Efficient AI-Empowered Communication Networks

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Prof. Dr. Sangheon Pack

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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
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Prof. Dr. Haneul Ko

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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

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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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Research

12 pages, 614 KiB

Open AccessArticle

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 2 | Viewed by 2196

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 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

Open AccessArticle

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 5 | Viewed by 2770

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 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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