**Preface**

Information and communication systems are integral parts of modern society, facilitating the exchange of information and connecting people across the globe. However, the usage of information and communication systems comes with significant environmental costs, which are the consequence of the two causes. The first one is the high energy consumption required for the operation of information and communication systems, and the second one is related to the carbon emissions that occur during their operation. As a consequence, the energy efficiency of information and communication systems has become an increasingly important topic, as the need to reduce energy consumption and minimize carbon emissions has become a critical priority.

One of the most significant challenges in improving the energy efficiency of information and communication systems and consequently reducing carbon emissions is the sheer volume of data that needs to be processed and transmitted. As the volume of data continues to grow due to the proliferation of new services and applications and the increase in the number of network users, the energy required for processing and transmitting this data also increases. This has led to a continuous increase in the energy consumption of data centers and communication networks, which are responsible for storing and transmitting data. This growth in energy consumption has resulted in various negative economic impacts related to the large operational expenditures (OPEX) of communication networks and system operators and environmental impacts related to the pollution caused by increased carbon emissions. Hence, there is a constant need for the development of solutions that can reduce energy consumption while maintaining high performance, quality of service, and reliability of communication networks and systems.

Although the contribution of communication networks and systems to energy consumption and carbon emissions on a global level cannot be completely nullified, in order to stop or even reverse the increasing trend of energy consumption and carbon emissions contributed by communication networks and systems, these contributions should be maximally decreased. To achieve this, improved or completely new strategies and approaches in terms of the development and operation of communication networks and systems must be devised and practically implemented. Hence, energy-efficient operation of communication networks and systems should be envisioned on all Open System Interconnection (OIS) layers. This can be realized through developing more energy-efficient communication protocols, designing more energy-efficient hardware components, envisioning novel algorithms that will minimize the number of active components at any given time, reusing them in a better way, and exploiting renewable energy sources in powering communication networks and systems. To accomplish this challenging task, a joint effort of different participants coming from governments, standardization organizations, academia, and industry must take place. Hence, energy efficiency has an important role in ensuring the sustainable development of communication networks and systems, and it represents a topic of increasing interest and importance in modern society.

Despite such astonishing interests in improving the energy efficiency of communication networks and systems perceived during the last decade, the research on energy-efficient communication networks and systems on different levels and in many fields demands enhanced or completely new solutions. Also, versatile basic or highly sophisticated problems are still unsolved or are even in their infancy. Hence, improving the energy efficiency of communication networks and systems are and will proceed in being an actual economic, social, industrial, and particularly research challenge. This book, entitled "*Energy-efficient Communication Networks and Systems*", is dedicated to all aspects of the research and development related to solving such challenges. The reprint provides an overview of the latest research and technologies in the field of energy-efficient communication networks and systems. It contains the accepted scientific papers gathered in the "*Special Issue on Energy-efficient Communication Networks and Systems*" organized for the Sensors journal which is published by MDPI (Multidisciplinary Digital Publishing Institute). Twelve high-quality articles have been collected and reproduced in this book, demonstrating significant achievements in the field to which this reprint is dedicated. Among published scientific papers, one paper is editorial, one paper is a review type of paper, and the remaining ten works are research articles. Published papers are consolidated in this reprint as self-contained peer-reviewed scientific works.

(1). The editorial paper "*Lorincz J.; Klarin Z.; Begusic D.; Advances in Improving Energy Efficiency of Fiber–Wireless Access Networks: A Comprehensive Overview, Sensors 2023, 23, 2239.*", gives a comprehensive survey of recent research on approaches that contribute to energy efficiency (EE) improvements of Fiber-Wireless (FiWi) access networks. The presented EE analyses are performed for different types of FiWi networks, including 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. For the R&F networks, energy conservation techniques and research studies related to the optical and wireless domains were presented, as well as related works that deal with the improvement of FiWi networks' EE through the cooperation of techniques in wireless and optical domains. Furthermore, two basic RoF techniques, the digital-RoF (D–RoF) and analog-Rof (A–RoF), were elaborated in the context of EE, and an overview of research studies in the field of improving the EE of D–RoF and A–RoF systems was given. Additionally, the cloud–radio access network (C-RAN) architecture was reviewed through the prism of energy consumption with the presentation of current research efforts related to the improvement of the C-RAN energy efficiency. The MEC-based FiWi networks, which introduce cloud computing at the edge of the mobile network, were further presented, and articles dedicated to the mechanisms and concepts for the optimization of the MEC FIWi network's energy consumption were highlighted. Finally, flexible SDN FiWi networks that offer high scalability and ease of management were presented, with an emphasis on research related to energy conservation techniques implemented in such networks. The last part of the paper also discusses future directions for improving the EE in the FiWi networks.

(2). In addition, the second review paper, "*Depasquale E.-V.; Davoli F.; Rajput H.; Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud, Sensors 2023, 23, 255.*" gives analytical and a graphical survey of the literature over the period 2010–2020, on the measurement of power consumption and developed power models of virtual entities (virtual machines (VMs) or containers) implemented in the telecommunication (telco) operators cloud. The paper presents a thorough analysis of the dynamics of research related to the virtual entities (VEs) implementation challenges, approaches, pitfalls, fallacies, and research gaps with respect to the predictive modeling and supporting measurements of individual VEs power consumption that is relevant to the telco cloud. Research dynamics is characterized through a publication frequency analysis, performed based on the application of a novel developed method that is unique in its ability to parse research literature. Through the visual aids and cross-cutting themes, the authors in the paper provide a thorough characterization of the problems, approaches, developments, formal methods, pitfalls, fallacies, and research gaps that characterize the research space of predictive modeling and measurements of individual telco VEs power consumption. The presented survey can serve as a reference in the selection of the most appropriate power consumption model of VEs implemented in the telco clouds.

(3). The third published paper, "*Lorincz J.; Klarin Z.; How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks, Sensors 2022, 22, 255.*" analyses the impact of the expected increase of data volumes (DVs) through the 2020s, on the energy efficiency (EE) of the fifth generation (5G) radio access network (RAN) by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G base stations (BSs) 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 heterogeneous networks (HetNets). For every device density class characterized with increased DVs, elaboration on the process of achieving the best and worst combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches have been performed. 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.

(4). In the fourth paper "*Lorincz J.; Ramljak I.; Beguˇsic D.; Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems, Sensors 2022, 22, 631.*", the efficiency of spectrum sensing performed with the energy detection (ED) method realized through the square-law combining (SLC) of the received signals at secondary users (SUs) has been analyzed. The analyses take into account the detection threshold (DT) adjustments performed according to noise uncertainty (NU) variations in multiple-input multiple-output (MIMO)—orthogonal frequency division multiplexing (OFDM) communication systems. The mathematical expression of the main parameters used for the evaluation of the ED performance as a local spectrum sensing technique employing SLC in MIMO-OFDM CR systems has been introduced. In addition, the algorithm for simulating the ED method in versatile operating environments characterized by the influence of distinct levels of NU and performed with dynamic DT (DDT) adjustments has been presented. The analysis of ED sensing efficiency has been performed through extensive simulations, which indicates how different working parameters, including the number of samples used in the ED process, the transmit powers of the primary user (PU), the DDT and NU factors, the probabilities of false alarm, and the signal-to-noise (SNR) level impact the probability of the detection of PU signals in MIMO-OFDM CR systems.

(5). To improve energy management, in the fifth paper, "*Marques D.; Senna C.; Lu´ıs M.; Forwarding in Energy-Constrained Wireless Information Centric Networks, Sensors 2022, 22, 1438*.", authors propose an efficient forwarding scheme in energy-constrained wireless information-centric networks (ICNs). Analyzed ICNs are composed of a large number of sensors allocated across the smart city. To achieve the stated goal dedicated to the improvement of sensor nodes' energy management, authors, among other parameters, consider the different types of sensor devices, their internal energy consumption, and the network context. The proposed forwarding strategy extends and adapts concepts of ICNs through the implementation of packet domain analysis, neighborhood evaluation, and sensor node sleeping and waking strategies. In order to consistently address sensor mobility and to improve the quality of content delivery, the proposed solution takes advantage of the neighborhood awareness of the moments that indicate when to listen and forward data packets. The evaluation of the proposed strategy is performed by simulation with real datasets obtained based on real urban mobility. The results show that the proposed data forwarding strategy enables significant improvements in the network content availability, overall energy savings, and the network lifetime.

(6). In the paper "*Liu Y.; Li C.; Li J.; Feng L.; Joint User Scheduling and Hybrid Beamforming Design for Massive MIMO LEO Satellite Multigroup Multicast Communication Systems, Sensors 2022, 22, 6858.*", authors investigated the robust joint user scheduling and hybrid beamforming design scheme that can maximize the energy efficiency (EE) of the massive multiple-input multiple-output (MIMO) low Earth orbit (LEO) satellite multigroup multicast communication system. To solve the stated problem, the authors first adopted the hierarchical clustering algorithm to group users and then applied the semidefinite programming (SDP) algorithm and the concave-convex process (CCCP) framework to tackle the optimization of user scheduling and hybrid beamforming design. To obtain the digital and analog satellite antenna beamforming matrix in a hybrid beamformer, the alternative optimization algorithm based on the majorization-minimization framework (MM-AltOpt) is also proposed. Numerical simulation results show that the energy efficiency of the proposed joint user scheduling and beamforming algorithm is higher than that of the traditional decoupling algorithms.

(7). To improve the power efficiency (PE) of the fifth generation (5G) heterogeneous network, in the seventh paper "*Osama M.; El Ramly S.; Abdelhamid B.; Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks, Sensors 2022, 22, 8570.*", an approach based on switching on and off the redundant small cells (SCs) using machine learning (ML) techniques is proposed. The proposed scheme needs to ensure a reduction in the energy consumption of the radio part of the 5G network, while the quality of service (QoS) for every user must be satisfied. The proposed approach is based on a linearly increasing inertia weight–binary particle swarm optimization (IW-BPSO) algorithm for SC on/off switching. Moreover, a soft frequency reuse (SFR) algorithm is proposed using the classification trees (CTs) approach to alleviate the interference and elevate the system throughput. The obtained results show that the proposed algorithms, in terms of energy efficiency improvements, outperform the other conventional energy-saving algorithms for 5G networks. The proposed algorithms reduce the power consumption of the network and the interference among the SCs while ensuring improvements in the total throughput and the PE of the system.

(8). Next published paper, "*Han D.; So J.; EnergyEfficient Resource Allocation Based on Deep Q-Network in V2V Communications, Sensors 2023, 23, 1295.*", utilizes the artificial intelligence concept of a deep Q-network (DQN) to select the transmit resource blocks and transmit power of vehicles in the vehicle-to-vehicle (V2V) network. The goal of the proposed concept is to maximize the sum rate of the vehicle-to-infrastructure (V2I) and V2V communication links while reducing the power consumption and latency of those links. The exploited DQN concept also utilizes 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 the V2V links while reducing the interference of the V2V network to the V2I network. The performance of the proposed scheme has been evaluated in terms of the sum rate of the V2X network, the average power consumption of the V2V links, and the average outage probability of the V2V links using a real case study. The simulation results show that the proposed scheme greatly reduces the transmit power of the V2V links, especially when compared to the transmit power of the V2V links in conventional reinforcement learning-based resource allocation schemes.

(9). In the paper "*Rau, F.; Soto, I.; Zabala-Blanco, D.; Azurdia-Meza, C.; Ijaz, M.; Ekpo, S.; Gutierrez, S.; A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks. Sensors 2023, 23, 4997.*" The authors propose a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves the usage of neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the proposed methodology, a case study was conducted to address the issue of energy efficiency in the data centers of telecommunications service providers. The case study involved comparing four neural network types, more specifically, recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), for determining the best network in terms of network traffic 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 significant energy savings potential in a single day, which offers a real solution for energy efficiency and energy savings that can be applied not only to the core part but also to the aggregation networks. The obtained results confirmed that utilizing a neural network as the primary tool for prediction enables high accuracy and adaptability to different data types of data center networks.

(10). In the paper "*Kinman, G.; Zili´c, ˇ Z.; Purnell, D.; Scheduling Sparse LEO Satellite Transmissions ˇ for Remote Water Level Monitoring. Sensors 2023, 23, 5581.*", authors have presented and evaluated the transmission scheduling for emerging sparse low earth orbit (LEO) satellite services suitable for IoT (Internet of Things) applications. More specifically, the authors explore the use of LEO satellite links in the long-term monitoring of water levels across remote areas. Since emerging sparse LEO satellite constellations maintain a sporadic connection to the ground station(s), the monitoring sensor's transmissions to the satellite need to be scheduled for satellite overfly periods. Thus, for remote sensing, energy consumption optimization is critical, and this motivates authors to develop a learning approach for scheduling the transmission times for the monitoring sensors. A detailed probabilistic energy consumption model was developed in order to evaluate the proposed online learning scheme for predicting transmission periods. The proposed online learning-based approach combines Monte Carlo and modified k-armed bandit approaches in order to produce an inexpensive scheme that applies to scheduling any LEO satellite transmissions. The proposed learning approach is inexpensive computationally, learns in small increments in a modest number of training epochs, and is interpretable, unlike most modern machine learning approaches. Results presented in the paper demonstrate the ability to adapt scheduling transmission time, which results in 20-fold energy saving of the transmission energy. The presented study applies to a wide range of IoT applications in areas with no existing wireless coverages.

(11). The paper "*Sterz, A.; Klose, R.; Sommer, M.; H¨ochst, J.; Link, J.; Simon, B.; Klein, A.; Hollick, M.; Freisleben, B.; Energy-Efficient Decentralized Broadcasting in Wireless Multi-Hop Networks. Sensors 2023, 23, 7419.*" presents a novel multi-hop data broadcasting protocol named BTP (Broadcast Tree Protocol). The proposed protocol uses a game-theoretical model to construct a spanning tree in a decentralized manner. The goal of the developed spanning tree model is to minimize the transmission power of each node and consequently minimize the total energy consumption of a network. The authors in the paper integrate three algorithms capable of inhibiting the creation of graph cycles into the design of BTP. Although based on a game-theoretical model, the proposed BTP neither requires information exchange between distant nodes nor time synchronization during its operation, while BTP inhibits graph cycles effectively. Authors, through simulations, evaluate BTP with respect to various aspects, compare BTP to other algorithms from the literature, and investigate the scalability of BTP. The proposed 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 the proposed protocol can achieve significant energy reduction when compared to a simple broadcast protocol in real-world experiments.

(12). Last published paper, "*Carbajal Ipenza, S.J.; Masiero, B.S.; Efficient Sigma–Delta Sensor Array Beamforming. Sensors 2023, 23, 7577.*" analyses beamforming algorithm implementation for sensors based on sigma–delta modulators (Σ∆Ms), which are widely used in consumer, industrial, automotive and medical applications such as micro-electro-mechanical systems (MEMSs) or digital microphones. Although Σ∆Ms have become a cost-effective and convenient way to deliver data to digital processors, the Σ∆Ms output a pulse-density modulated (PDM) bitstream signal, which is the reason why 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 surface usage are crucial), becomes especially expensive as a large number of parallel decimation filters are required. Thus, the 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, the paper presents an implementation example of a delay-and-sum (DAS) beamformer at given spatial and frequency requirements using a novel proposed approach. The presented results show that in comparison with the most efficient state-of-the-art beamformer architectures, the proposed architecture requires significantly lower storage and computational resources, which contributes to the improvement of network energy efficiency.

The 40 different authors coming from academia and industry have contributed to this book. Their contributions provide valuable insights into the latest research and technologies in the area of energy-efficient communication networks and systems. The reprint also includes case studies and examples of the usage of energy-efficient communication technologies in practice, providing readers with practical insights into the implementation of these technologies. The reprint is intended for researchers, engineers, and students who are interested in the design and implementation of energy-efficient communication networks and systems. Hence, the reprint gives insights and solutions for a range of problems in the field of obtaining energy-efficient communication systems and networks, and it lays the basis for solving new challenges toward achieving future advances.

The reprint editor would like to thank all authors who have submitted their articles and all reviewers for their valuable work dedicated to giving an expert review for submitted papers. Moreover, the reprint editor is grateful to all persons involved in the edition of this reprint for their invaluable support, including the *Sensors* Journal Section managing editor Mr. Winston Wang, and other editorial team members involved in editing the papers submitted to *Sensors* journal "*Special Issue on Energy-efficient communication networks and systems*".

The reprint editor sincerely hopes that this reprint will be a valuable source of information presenting the recent advances in different fields related to greening and improving energy efficiency and sustainability of those communication networks and systems particularly addressed in this reprint.

> **Josip Lorincz** *Editor*
