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Energy-Efficient Communication Networks and Systems

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

Deadline for manuscript submissions: closed (30 July 2023) | Viewed by 34687

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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 nonnegligible 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 which will ensure further improvements in the energy efficiency of communication networks and systems on all layers of open systems interconnection (OSI) model.

Therefore, 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 energy-efficiency of communication networks and systems
Techniques for improving 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 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 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 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 energy-efficiency of communication networks
Performance metrics for evaluation of energy-efficiency in communication networks
Green solutions for reduction of electromagnetic pollutions
Solutions for power-efficient air-conditioning and cooling of communication systems and devices
Blockchain approaches for improving energy management of communication networks

Dr. Josip Lorincz
Guest Editor

Manuscript Submission Information

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Keywords

Green wireless networks
Green wireline networking
Energy-efficient data centres
Green cloud computing
Energy-efficient sensor networks
Green IoT/IoE networks
Energy-efficient industrial communications
Sustainable networking and computing
Artificial intelligence (AI) techniques for energy efficiency
Energy-efficient resource management
Energy-efficient algorithms and protocols

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Energy-Efficient Communication Networks and Systems: 2nd Edition in Sensors (5 articles)

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

37 pages, 2895 KiB

Open AccessEditorial

Advances in Improving Energy Efficiency of Fiber–Wireless Access Networks: A Comprehensive Overview

by Josip Lorincz, Zvonimir Klarin and Dinko Begusic

Sensors 2023, 23(4), 2239; https://doi.org/10.3390/s23042239 - 16 Feb 2023

Cited by 18 | Viewed by 5851

Abstract

Due to the growing impact of the information and communications technology (ICT) sector on electricity usage and greenhouse gas emissions, telecommunication networks require new solutions which will enable the improvement of the energy efficiency of networks. Access networks, which are responsible for the last mile of connectivity and also for one of the largest shares in network energy consumption, are viable candidates for the implementation of new protocols, models and methods which will contribute to the reduction of the energy consumption of such networks. Among the different types of access networks, hybrid fiber–wireless (FiWi) networks are a type of network that combines the capacity and reliability of optical networks with the flexibility and availability of wireless networks, and as such, FiWi networks have begun to be extensively used in modern access networks. However, due to the advent of high-bandwidth applications and Internet of Things networks, the increased energy consumption of FiWi networks has become one of the most concerning challenges required to be addressed. This paper provides a comprehensive overview of the progress in approaches for improving the energy efficiency (EE) of different types of FiWi networks, which include the radio-and-fiber (R&F) networks, the radio-over-fiber networks (RoF), the FiWi networks based on multi-access edge computing (MEC) and the software-defined network (SDN)-based FiWi networks. It also discusses future directions for improving the EE in the FiWi networks. Full article

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

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Research

Jump to: Editorial, Review

21 pages, 1618 KiB

Open AccessArticle

Efficient Sigma–Delta Sensor Array Beamforming

by Sammy Johnatan Carbajal Ipenza and Bruno Sanches Masiero

Sensors 2023, 23(17), 7577; https://doi.org/10.3390/s23177577 - 31 Aug 2023

Viewed by 1435

Abstract

Nowadays, sensors with built-in sigma–delta modulators (ΣΔMs) are widely used in consumer, industrial, automotive, and medical applications, as they have become a cost-effective and convenient way to deliver data to digital processors. This is the case for micro-electro-mechanical system (MEMS), digital microphones that convert analog audio to a pulse-density modulated (PDM) bitstream. However, as the ΣΔMs output a PDM signal, sensors require either built-in or external high-order decimation filters to demodulate the PDM signal to a baseband multi-bit pulse-code modulated (PCM) signal. Because of this extra circuit requirement, the implementation of sensor array algorithms, such as beamforming in embedded systems (where the processing resources are critical) or in very large-scale integration (VLSI) circuits (where the power and area are crucial) becomes especially expensive as a large number of parallel decimation filters are required. This article proposes a novel architecture for beamforming algorithm implementation that fuses delay and decimation operations based on maximally flat (MAXFLAT) filters to make array processing more affordable. As proof of concept, we present an implementation example of a delay-and-sum (DAS) beamformer at given spatial and frequency requirements using this novel approach. Under these specifications, the proposed architecture requires 52% lower storage resources and 19% lower computational resources than the most efficient state-of-the-art architecture. Full article

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

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25 pages, 693 KiB

Open AccessArticle

Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks

by Artur Sterz, Robin Klose, Markus Sommer, Jonas Höchst, Jakob Link, Bernd Simon, Anja Klein, Matthias Hollick and Bernd Freisleben

Sensors 2023, 23(17), 7419; https://doi.org/10.3390/s23177419 - 25 Aug 2023

Cited by 1 | Viewed by 1315

Abstract

Several areas of wireless networking, such as wireless sensor networks or the Internet of Things, require application data to be distributed to multiple receivers in an area beyond the transmission range of a single node. This can be achieved by using the wireless medium’s broadcast property when retransmitting data. Due to the energy constraints of typical wireless devices, a broadcasting scheme that consumes as little energy as possible is highly desirable. In this article, we present a novel multi-hop data dissemination protocol called BTP. It uses a game-theoretical model to construct a spanning tree in a decentralized manner to minimize the total energy consumption of a network by minimizing the transmission power of each node. Although BTP is based on a game-theoretical model, it neither requires information exchange between distant nodes nor time synchronization during its operation, and it inhibits graph cycles effectively. The protocol is evaluated in Matlab and NS-3 simulations and through real-world implementation on a testbed of 75 Raspberry Pis. The evaluation conducted shows that our proposed protocol can achieve a total energy reduction of up to 90% compared to a simple broadcast protocol in real-world experiments. Full article

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

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23 pages, 2122 KiB

Open AccessArticle

Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring

by Garrett Kinman, Željko Žilić and David Purnell

Sensors 2023, 23(12), 5581; https://doi.org/10.3390/s23125581 - 14 Jun 2023

Viewed by 1694

Abstract

This paper explores the use of low earth orbit (LEO) satellite links in long-term monitoring of water levels across remote areas. Emerging sparse LEO satellite constellations maintain sporadic connection to the ground station, and transmissions need to be scheduled for satellite overfly periods. For remote sensing, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our online learning-based approach combines Monte Carlo and modified k-armed bandit approaches, to produce an inexpensive scheme that is applicable to scheduling any LEO satellite transmissions. We demonstrate its ability to adapt in three common scenarios, to save the transmission energy 20-fold, and provide the means to explore the parameters. The presented study is applicable to wide range of IoT applications in areas with no existing wireless coverages. Full article

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

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33 pages, 6142 KiB

Open AccessArticle

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks

by Francisco Rau, Ismael Soto, David Zabala-Blanco, Cesar Azurdia-Meza, Muhammad Ijaz, Sunday Ekpo and Sebastian Gutierrez

Sensors 2023, 23(11), 4997; https://doi.org/10.3390/s23114997 - 23 May 2023

Cited by 7 | Viewed by 3221

Abstract

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems. Full article

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

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15 pages, 1101 KiB

Open AccessEditor’s ChoiceArticle

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

by Donghee Han and Jaewoo So

Sensors 2023, 23(3), 1295; https://doi.org/10.3390/s23031295 - 23 Jan 2023

Cited by 10 | Viewed by 2911

Abstract

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links. Full article

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

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19 pages, 3511 KiB

Open AccessArticle

Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks

by Mayada Osama, Salwa El Ramly and Bassant Abdelhamid

Sensors 2022, 22(21), 8570; https://doi.org/10.3390/s22218570 - 7 Nov 2022

Cited by 2 | Viewed by 1982

Abstract

The dense deployment of small cells (SCs) in the 5G heterogeneous networks (HetNets) fulfills the demand for vast connectivity and larger data rates. Unfortunately, the power efficiency (PE) of the network is reduced because of the elevated power consumption of the densely deployed SCs and the interference that arise between them. An approach to ameliorate the PE is proposed by switching off the redundant SCs using machine learning (ML) techniques while sustaining the quality of service (QoS) for each user. In this paper, a linearly increasing inertia weight–binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching is proposed to minimize the power consumption of the network. Moreover, a soft frequency reuse (SFR) algorithm is proposed using classification trees (CTs) to alleviate the interference and elevate the system throughput. The results show that the proposed algorithms outperform the other conventional algorithms, as they reduce the power consumption of the network and the interference among the SCs, ameliorating the total throughput and the PE of the system. Full article

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

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31 pages, 5267 KiB

Open AccessArticle

Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems

by Yang Liu, Changqing Li, Jiong Li and Lu Feng

Sensors 2022, 22(18), 6858; https://doi.org/10.3390/s22186858 - 10 Sep 2022

Cited by 3 | Viewed by 2472

Abstract

In the satellite multigroup multicast communication systems based on the DVB-S2X standard, due to the limitation of the DVB-S2X frame structure, user scheduling and beamforming design have become the focus of academic research. In this work, we take the massive multi-input multi-output (MIMO) low earth orbit (LEO) satellite communication system adopting the DVB-S2X standard as the research scenario, and the LEO satellite adopts a uniform planar array (UPA) based on the fully connected hybrid structure. We focus on the coupling design of user scheduling and beamforming; meanwhile, the scheme design takes the influence of residual Doppler shift and phase disturbance on channel errors into account. Under the constraints of total transmission power and quality of service (QoS), we study the robust joint user scheduling and hybrid beamforming design aimed at maximizing the energy efficiency (EE). For this problem, we first adopt the hierarchical clustering algorithm to group users. Then, the semidefinite programming (SDP) algorithm and the concave convex process (CCCP) framework are applied to tackle the optimization of user scheduling and hybrid beamforming design. To handle the rank-one matrix constraint, the penalty iteration algorithm is proposed. To balance the performance and complexity of the algorithm, the user preselected step is added before joint design. Finally, to obtain the digital beamforming matrix and the analog beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is proposed. Numerical simulation results show that the EE of the proposed joint user scheduling and beamforming design algorithm is higher than that of the traditional decoupling design algorithms. Full article

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

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21 pages, 3794 KiB

Open AccessArticle

Forwarding in Energy-Constrained Wireless Information Centric Networks

by Daniel Marques, Carlos Senna and Miguel Luís

Sensors 2022, 22(4), 1438; https://doi.org/10.3390/s22041438 - 13 Feb 2022

Cited by 4 | Viewed by 2186

Abstract

Information Centric Networks (ICNs) have been considered one of the most promising candidates to overcome the disadvantages of host-centric architectures when applied to IoT networks, having the potential to address the challenges of a smart city. One of the foundations of a smart city is its sensory capacity, which is obtained through devices associated with the IoT concept. The more sensors spread out, the greater the ability to sense the city. However, such a scale demands high energy requirements and an effective improvement in the energy management is unavoidable. To improve the energy management, we are proposing an efficient forwarding scheme in energy-constrained wireless ICNs. To achieve this goal, we consider the type of devices, their internal energy and the network context, among other parameters. The proposed forwarding strategy extends and adapts concepts of ICNs, by means of packet domain analysis, neighbourhood evaluation and node sleeping and waking strategies. The proposed solution takes advantage of the neighbourhood to be aware of the moments to listen and forward packets in order to consistently address mobility, improving the quality of content delivery. The evaluation is performed by simulation with real datasets of urban mobility, one from the lagoon of “Ria de Aveiro” and the other from a vehicular network in the city of Porto. The results show that the proposed forwarding scheme resulted in significant improvements in network content availability, in the overall energy saving and, consequently, in the network lifetime. Full article

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

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29 pages, 5498 KiB

Open AccessArticle

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems

by Josip Lorincz, Ivana Ramljak and Dinko Begušić

Sensors 2022, 22(2), 631; https://doi.org/10.3390/s22020631 - 14 Jan 2022

Cited by 7 | Viewed by 2640

Abstract

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method. Full article

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

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18 pages, 2957 KiB

Open AccessArticle

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

by Josip Lorincz and Zvonimir Klarin

Sensors 2022, 22(1), 255; https://doi.org/10.3390/s22010255 - 30 Dec 2021

Cited by 11 | Viewed by 2755

Abstract

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics. Full article

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

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Review

Jump to: Editorial, Research

69 pages, 8034 KiB

Open AccessReview

Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud

by Etienne-Victor Depasquale, Franco Davoli and Humaira Rajput

Sensors 2023, 23(1), 255; https://doi.org/10.3390/s23010255 - 26 Dec 2022

Cited by 4 | Viewed by 2502

Abstract

This article is a graphical, analytical survey of the literature, over the period 2010–2020, on the measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. We present a novel review method, that summarizes the dynamics as well as the results of the research. Our method lends insight into trends, research gaps, fallacies and pitfalls. Notably, we identify limitations of the widely used linear models and the progression towards Artificial Intelligence/Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards the attribution of power consumption to virtual entities; frequency; and temperature. Full article

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

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