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Energy-Efficient Communication Networks and Systems: 2nd Edition

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 30 July 2025 | Viewed by 16191

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Dr. Josip Lorincz

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Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB), University of Split, R. Boskovica 32, 21000 Split, Croatia
Interests: energy-efficient networking and computing; telecommunications; wireline/wireless networks; sensor networks; Internet of Things; cloud computing; system optimization; renewable energies; cognitive radio
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Special Issue Information

Dear Colleagues,

During the past decade, research and the industrial community start to invest considerable efforts in improving the energy efficiency of communication networks and systems due to energetic, economic, and environmental reasons. The energetic reasons are reflected in the number of studies according to which Information and Communication Technologies (ICT) infrastructure and computer systems consume significant amounts of world electricity consumption. The economic reasons are related to the expectations that power consumed by communication networks and systems will increase due to the necessity of satisfying constantly increasing demand for new applications, data volumes transfer and the number of user devices, which will additionally increase the energy bills of service providers. Finally, the environmental reasons are dedicated to the non-negligible contribution of the overall communication networks and systems exploitation lifecycle to the global carbon footprint, which further contributes to global warming. All indicated reasons mandate the necessity for continuation in attempts that will ensure further improvements in the energy efficiency of communication networks and systems on all layers of the open systems interconnection (OSI) model.

Therefore, this 2nd Special issue Edition is a continuation of the first successfully organized Special issue on energy-efficient communication networks and systems for Sensors journal. This Special Issue aims to serve as a platform for researchers and visionaries from academia, research labs, and industry in presenting novelties related to energy efficiency improvements of communication networks and computing systems. Sharing ideas, views, results, and experiences dedicated to improving the energy efficiency of communication networks and systems is what this Special Issue is intended to be about. Anything from theoretical and experimental achievements to innovative design and management approaches, prototyping efforts, and case studies are in the focus of this Special Issue. This Special Issue aims to open new research ways toward more energy-efficient communication networks and computing systems. The Special Issue accepts original research and review papers dedicated to the topic of improving the energy efficiency of communication networks and systems.

The broad range of topics of interest to this Special Issue include, but are not limited to, the following:

Implementation of artificial intelligence (AI) for improving the energy efficiency of communication networks and systems;
Techniques for improving the energy efficiency of wireless communication networks;
Approaches for improving energy-efficiency of wireline communication networks;
Solutions for reducing power consumption of data centers;
Optimization of energy consumption in optical networks;
Techniques for improving energy efficiency of fiber-wireless (FiWi) networks;
Security and energy-efficiency trade-offs in communication networks;
Green communication technologies for smart cities;
Approaches based on cloud and edge computing for improving network energy efficiency;
Network function virtualization (NFV) concepts for optimizing the energy efficiency of communication networks;
Green future Internet and energy-efficient software-defined networking concepts;
Energy-efficient Internet of Things/Everything (IoT/E) networks;
Solutions for improving the energy efficiency of sensor networks;
Improving energy efficiency with and within Unmanned Aerial Vehicle (UAV)-based networks;
Applications of green networking technologies and principles for peer-to-peer and ad hoc networks;
Energy-efficient underwater communications;
Energy-efficient satellite communications;
Energy-efficiency improvements of low-power wireless networks and devices;
Techniques for optimizing the energy efficiency of user devices;
Energy-efficient public health solutions;
Green network design and energy-efficient smart grids;
Energy-efficient vehicle communications;
Energy-efficient automation and industrial communications;
Energy-efficient critical communications;
Computer and software engineering for improving energy efficiency;
Techniques for ensuring Quality of Service (QoS) in energy-efficient communication networks;
Green mobile applications;
Green cognitive radio networks;
Communication solutions for green buildings;
Power consumption and cost models of networking infrastructure;
Power consumption measurements and energy profiling of communication networks;
Smart metering and data analyses for improving energy efficiency;
Big data analyses for meeting green challenges;
Hardware and architectural enhancements for reducing power consumption of communication network devices and systems;
Energy-efficient management of communication networks;
Cross-layer optimizations for reducing the energy consumption of communication networks;
Energy-efficient algorithms, protocols, and protocol extensions;
Energy-efficient transmission technologies;
Energy cost models for network operators;
Renewable energy sources for power supply of communication networks;
Antenna design and transmission technologies for reducing energy consumption;
Intelligent reflective surfaces for improving energy efficiency in wireless networks;
Energy harvesting solutions and prototypes in communication networks;
Cooperative communication systems for improving energy efficiency;
Field trials for ensuring sustainable networking and computing;
Standardization and regulation policy for improving the energy efficiency of communication networks;
Performance metrics for evaluation of energy efficiency in communication networks;
Green solutions for reduction of electromagnetic pollution;
Solutions for power-efficient air-conditioning and cooling of communication systems and devices;
Blockchain approaches for improving energy management of communication networks.

Besides regular paper submissions, this Special Issue aims to comprise extended versions of conference papers from the 14th Symposium on “Green Networking and Computing” (SGNC 2023) chaired by Dr. Josip Lorincz, which will be held in the frame of the SoftCOM2023 conference organized in Split, Croatia, during 21–23 September 2023.

Dr. Josip Lorincz
Guest Editor

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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. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Related Special Issue

Energy-Efficient Communication Networks and Systems in Sensors (12 articles)

Published Papers (8 papers)

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Research

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24 pages, 5550 KiB

Open AccessArticle

Energy-Aware Edge Infrastructure Traffic Management Using Programmable Data Planes in 5G and Beyond

by Jorge Andrés Brito, José Ignacio Moreno and Luis M. Contreras

Sensors 2025, 25(8), 2375; https://doi.org/10.3390/s25082375 - 9 Apr 2025

Viewed by 458

Abstract

Next-generation networks, particularly 5G and beyond, face rising energy demands that pose both economic and environmental challenges. In this work, we present a traffic management scheme leveraging programmable data planes and an SDN controller to achieve energy proportionality, matching network resource usage to fluctuating traffic loads. This approach integrates flow monitoring on programmable switches with a dynamic power manager in the controller, which selectively powers off inactive switches. We evaluate this scheme in an emulated edge environment across multiple urban traffic profiles. Our results show that disabling switches not handling traffic can significantly reduce energy consumption, even under relatively subtle load variations, while maintaining normal network operations and minimizing overhead on the control plane. We further include a projected savings analysis illustrating the potential benefits if the solution is deployed on hardware devices such as Tofino-based switches. Overall, these findings highlight how data plane-centric, energy-aware traffic management can make 5G-and-beyond edge infrastructures both sustainable and adaptable for future networking needs. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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19 pages, 12800 KiB

Open AccessArticle

Pareto Front Transformation in the Decision-Making Process for Spectral and Energy Efficiency Trade-Off in Massive MIMO Systems

by Eni Haxhiraj, Desar Shahu and Elson Agastra

Sensors 2025, 25(5), 1451; https://doi.org/10.3390/s25051451 - 27 Feb 2025

Viewed by 376

Abstract

This paper presents a method of choosing a single solution in the Pareto Optimal Front of the multi-objective problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple Input, Multiple Output) systems. It proposes the transformation of the group of non-dominated alternatives using the Box–Cox transformation with values of λ < 1 so that the graph with a complex shape is transformed into a concave graph. The Box–Cox transformation solves the selection bias shown by the decision-making algorithms in the non-concave part of the Pareto Front. After the transformation, four different MCDM (Multi-Criteria Decision-Making) algorithms were implemented and compared: SAW (Simple Additive Weighting), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), PROMITHEE (Preference Ranking Organization Method for Enrichment Evaluations) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The simulations showed that the best value of the λ parameter is 0, and the MCDM algorithms which explore the Pareto Front completely for different values of weights of the objectives are VIKOR as well as SAW and TOPSIS when they include the Max–Min normalization technique. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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24 pages, 1016 KiB

Open AccessArticle

MILD: Minimizing Idle Listening Energy Consumption via Down-Clocking for Energy-Efficient Wi-Fi Communications

by Jae-Hyeon Park, Young-Joo Suh, Dongdeok Kim, Harim Lee, Hyeongtae Ahn and Young Deok Park

Sensors 2025, 25(4), 1155; https://doi.org/10.3390/s25041155 - 13 Feb 2025

Viewed by 628

Abstract

Mobile devices, such as smartphones and laptops, face energy consumption challenges due to battery limitations, with Wi-Fi being one of the major sources of energy consumption in these devices. The IEEE 802.11 standard addresses this issue with Power Saving Mode (PSM), which reduces power consumption but increases latency. To mitigate this latency, Adaptive-PSM (A-PSM) dynamically switches between PSM and Constantly Awake Mode (CAM); however, the associated Idle Listening (IL) process still results in high energy consumption. Various strategies have been proposed to optimize IL time; however, Medium Access Control (MAC)-level contention and network delays limit their effectiveness. To overcome these limitations, we propose MILD (Minimizing Idle Listening energy consumption via Down-clocking), a novel scheme that reduces energy consumption without compromising throughput. MILD introduces specialized preambles for Packet Arrival Detection (PAD) and Device Address Recognition (DAR), allowing the client to operate in a down-clocked state during IL and switch to full clocking only when necessary. Experimental results demonstrate that MILD reduces energy consumption by up to 23.6% while maintaining a minimal throughput loss of 12.5%, outperforming existing schemes. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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17 pages, 2684 KiB

Open AccessArticle

Ergodic Rate Analysis of Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surface-Assisted Rate-Splitting Multiple Access Systems Based on Discrete Phase Shifts

by Tao Liu and Yue Zhou

Sensors 2024, 24(17), 5480; https://doi.org/10.3390/s24175480 - 23 Aug 2024

Viewed by 1109

Abstract

In this paper, we combine simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with rate-splitting multiple access (RSMA) technology and investigate the ergodic rate performance of an STAR-assisted RSMA system. Considering the discrete phase shifts of the STAR-RIS in practice, the downlink performance of STAR-RIS-assisted RSMA with discrete phase shifts is compared to that with continuous phase shifts. Firstly, the cumulative distribution function of signal-to-interference-plus-noise ratio (SINR) of users is analyzed. Then, the total ergodic rate of the system and its approximate closed-form solution are, respectively, derived based on the cumulative distribution function of users. The simulation results validate the effectiveness of the theoretical analysis, showing good agreement between the derived theoretical ergodic rate and the corresponding simulations. Although the system performance with discrete phase shifts is inferior to that with continuous phase shifts due to quantization errors, the performance of the continuous phase shift system is well approximated when the quantization bit of the phase shift system reaches 3 in the simulations. Additionally, the impact of the number of STAR-RIS elements on the system’s performance is analyzed. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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26 pages, 3689 KiB

Open AccessArticle

AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems

by Haitham Khaled and Emad Alkhazraji

Sensors 2024, 24(15), 4956; https://doi.org/10.3390/s24154956 - 31 Jul 2024

Cited by 1 | Viewed by 1322

Abstract

Traditional heterogeneous networks (HetNets) are constrained by their hardware design and configuration. These HetNets have a limited ability to adapt to variations in network dynamics. Software-defined radio technology has the potential to address this adaptability issue. In this paper, we introduce a software-defined radio (SDR)-based long-term evolution licensed assisted access (LTE-LAA) architecture for next-generation communication networks. We show that with proper design and tuning of the proposed architecture, high-level adaptability in HetNets becomes feasible with a higher throughput and lower power consumption. Firstly, maximizing the throughput and minimizing power consumption are formulated as a constrained optimization problem. Then, the obtained solution, alongside a heuristic solution, is compared against the solutions to existing approaches, showing our proposed strategy is drastically superior in terms of both power efficiency and system throughput. This study is then concluded by employing artificial intelligence techniques in multi-objective optimization, namely random forest regression, particle swarm, and genetic algorithms, to balance out the trade-offs between maximizing the throughput and power efficiency and minimizing energy consumption. These investigations demonstrate the potential of employing the proposed LTE-LAA architecture in addressing the requirements of next-generation HetNets in terms of power, throughput, and green scalability. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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28 pages, 4453 KiB

Open AccessArticle

A Survey on Green Enablers: A Study on the Energy Efficiency of AI-Based 5G Networks

by Zeinab Ezzeddine, Ayman Khalil, Besma Zeddini and Habiba Hafdallah Ouslimani

Sensors 2024, 24(14), 4609; https://doi.org/10.3390/s24144609 - 16 Jul 2024

Cited by 1 | Viewed by 2991

Abstract

In today’s world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI’s rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI’s integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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Review

Jump to: Research

34 pages, 5772 KiB

Open AccessReview

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks

by Josip Lorincz, Amar Kukuruzović and Zoran Blažević

Sensors 2024, 24(10), 3242; https://doi.org/10.3390/s24103242 - 20 May 2024

Cited by 3 | Viewed by 3255

Abstract

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks’ energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks’ energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices’ energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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31 pages, 1076 KiB

Open AccessReview

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

by Muhammad Asad, Saima Shaukat, Dou Hu, Zekun Wang, Ehsan Javanmardi, Jin Nakazato and Manabu Tsukada

Sensors 2023, 23(17), 7358; https://doi.org/10.3390/s23177358 - 23 Aug 2023

Cited by 12 | Viewed by 5180

Abstract

This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for the edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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