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Electronics
Special Issues
Energy-Aware and Efficient Computing and Communications
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Energy-Aware and Efficient Computing and Communications

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

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 13710

Special Issue Editors

Prof. Dr. Jong Kook Kim

E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea
Interests: heterogeneity in computing systems; real-time mobile computing; performance measures; resource management; evolutionary heuristics; energy-aware computing; efficient computing; shipboard computing; distributed systems; artificial neural networks; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Joongheon Kim

E-Mail Website
Guest Editor
School of Electrical Engineering, Korea University, Seoul 02841, Korea
Interests: deep reinforcement learning; mobile platforms; energy-efficient computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sean (Seok-Chul) Kwon

E-Mail Website
Guest Editor
Department of Electrical Engineering, California State University, Long Beach, CA 90840, USA
Interests: energy-efficient wireless communication; multiplexing/multiple access; multiple-input multiple-output (MIMO); 5th Generation New Radio (5G NR); modulation scheme; polarizations; reference signal design; channel/interference estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, power and energy consumption has become an issue because of mobile devices, high-performance computing systems, and various communication systems. For example, data centers consume huge amounts of electricity in operating massive numbers of servers and networking devices. Embedded systems for vehicles and mobile systems are energy constrained and therefore need energy awareness and efficient operation of resources to minimize the power/energy consumption. There are many efforts to efficiently use energy (e.g., smart and micro grids). Additionally, many people and institutions are looking at Green IT, where using less energy to conserve our planet and reducing pollution is the objective. It has become essential to design and build systems that reduce or efficiently use power/energy.

Prof. Dr. Jong Kook Kim
Assist. Prof. Dr. Joongheon Kim
Prof. Dr. Sean (Seok-Chul) Kwon
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

  • Energy-aware computing
  • Energy-aware communication systems
  • Power efficient circuit design
  • Power efficient embedded systems
  • Green IT
  • Green IoT
  • Energy-efficient communication
  • Green communications
  • Energy-aware mobile platforms
  • Energy-aware machine learning and neural networks
  • Artificial intelligence techniques for energy-aware computing

Published Papers (4 papers)

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Research

17 pages, 854 KiB  
Article
Energy Efficiency Analysis of Code Refactoring Techniques for Green and Sustainable Software in Portable Devices
by İbrahim Şanlıalp, Muhammed Maruf Öztürk and Tuncay Yiğit
Electronics 2022, 11(3), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030442 - 1 Feb 2022
Cited by 9 | Viewed by 4114
Abstract
Code refactoring is a time-consuming and effort-intensive process that is applied for making improvements to source codes. There exist several refactoring techniques to improve software quality. Some of them aim to reduce the energy consumption of the software. However, the combination of applied [...] Read more.
Code refactoring is a time-consuming and effort-intensive process that is applied for making improvements to source codes. There exist several refactoring techniques to improve software quality. Some of them aim to reduce the energy consumption of the software. However, the combination of applied refactoring techniques is crucial to the success rate. In addition, to provide sustainable services on portable devices such as mobile phones and laptops, which rely on batteries, improving and optimizing the energy efficiency is important. This study focuses on examining the effect of code refactoring techniques on energy consumption. A total of 25 different source codes of applications programmed in the C# and Java languages are selected for the study, and combinations obtained from refactoring techniques are applied to these source codes. The combinations applied are analyzed using the maintainability index. Power consumption estimation tools are used to measure the energy consumption of the original and refactored codes. The results show that the combinations significantly improve the software’s energy efficiency. The results will provide a better understanding of the relationship between the energy efficiency of software and refactoring techniques. Moreover, they will help developers to improve their object-oriented code in terms of both energy efficiency and sustainability. Full article
(This article belongs to the Special Issue Energy-Aware and Efficient Computing and Communications)
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17 pages, 1497 KiB  
Article
Energy-Efficient Cluster Head Selection via Quantum Approximate Optimization
by Jaeho Choi, Seunghyeok Oh and Joongheon Kim
Electronics 2020, 9(10), 1669; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9101669 - 13 Oct 2020
Cited by 15 | Viewed by 2430
Abstract
This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends [...] Read more.
This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends on the distribution of these clusters. For an energy-efficient network topology, none of the selected cluster heads should be neighbors. In addition, all the selected cluster heads should have high energy-consumption efficiency. Accordingly, an energy-efficient cluster head selection policy can be defined as a maximum weight independent set (MWIS) formulation. The cluster head selection policy formulated with MWIS is solved by using the quantum approximate optimization algorithm (QAOA), which is a hybrid quantum-classical algorithm. The accuracy of the proposed energy-efficient cluster head selection via QAOA is verified via simulations. Full article
(This article belongs to the Special Issue Energy-Aware and Efficient Computing and Communications)
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20 pages, 1139 KiB  
Article
Joint Message-Passing and Convex Optimization Framework for Energy-Efficient Surveillance UAV Scheduling
by Soyi Jung, Joongheon Kim and Jae-Hyun Kim
Electronics 2020, 9(9), 1475; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091475 - 9 Sep 2020
Cited by 16 | Viewed by 2952
Abstract
In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, [...] Read more.
In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, the proposed algorithm pursues energy-efficient operations by deactivating selected surveillance cameras on the UAVs located in overlapping areas. For this objective, a message-passing based algorithm is used because the overlapping situations can be formulated using a max-weight independent set. Next, the unscheduled UAVs based on the message-passing fly to the charging towers to be charged. This algorithm computes the optimal matching between the UAVs and charging towers and the amount of energy allocation for the scheduled UAV-tower pairs. This joint optimization is initially formulated as non-convex, and it is then reformulated to be convex, which can guarantee optimal solutions. The proposed framework achieves the desired performance, as presented in the performance evaluation. Full article
(This article belongs to the Special Issue Energy-Aware and Efficient Computing and Communications)
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17 pages, 1146 KiB  
Article
Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms
by Joohyung Jeon, Soohyun Park, Minseok Choi, Joongheon Kim, Young-Bin Kwon and Sungrae Cho
Electronics 2020, 9(9), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091359 - 21 Aug 2020
Cited by 13 | Viewed by 3392
Abstract
Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, [...] Read more.
Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status. Full article
(This article belongs to the Special Issue Energy-Aware and Efficient Computing and Communications)
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