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In coherent optics transmission systems, the digital signal processor (DSP) application-specific integrated circuit (ASIC) is the most power-hungry part of the optical transceiver. Already in the edge of transistor technology, to achieve the power budget, we must look for opportunities to further optimize the designs. This letter explores a dynamic duty cycle for reducing the consumption in the pipeline of such DSP ASICs. We exploit the characteristics of estimator algorithms to introduce a dynamic duty cycle, reducing the mean consumption of designs originally constrained only for worst-case scenarios. We present the methodology to implement duty cycle control using the carrier frequency offset estimator (CFE) algorithm as case study, achieving in simulation level from 22% to 74% power consumption reduction in this algorithm, varying on-chip operation conditions. [ABSTRACT FROM AUTHOR]

Multipliers are one of the most power-hungry arithmetic circuits on hardware neural network accelerators on field programmable gate array (FPGA). In this letter, design of hardware-efficient softcore multipliers using adaptive logic modules (ALM) is presented. Two partial products (PP) are added using a Karnaugh map (Kmap) to fit on one Intel FPGA ALM in such a way that two consecutive rows of Booth PP matrix are merged. 29.26% energy savings are observed by AR-44 compared to the exact multiplier with a drop of 0.63% accuracy on VGG16 convolutional neural network. [ABSTRACT FROM AUTHOR]

The Peripheral Component Interconnect Express (PCI-Express or PCle) architecture has been the ubiquitous backbone interconnect in the evolving computing landscape for more than two decades. This paper delves into the multiple innovations driving the backward-compatible evolution of PCle for seven generations, doubling bandwidth every generation, while delivering power-efficient and cost-effective performance. Compute Express Link (CXL) overlays coherency and memory protocols on top of PCle for heterogeneous computing, addressing the memory wall, resource pooling and sharing across servers, and distributed computing using load-store based messaging. The die-todie industry-standard, Universal Chiplet Interconnect Express $^{TM}$ (UCle), offers orders of magnitude improvement in bandwidth density, power efficiency, and latency for PCle and CXL protocols on-package and pod-level connectivity with co-packaged optics. We foresee PCle will continue to evolve over the next few decades to serve future computing needs. It will do so by embracing alternate media while backward-compatible frequency scaling with copper will continue and protocol enhancements will enable non-tree fabric topologies. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) are considered a key enabler of highly energy-efficient 6G and beyond networks. This property arises from the absence of power amplifiers in the structure, in contrast to active nodes, such as small cells and relays. However, a certain amount of power is still required for RIS operation. To improve their energy efficiency further, we propose the notion of perpetual RISs, which secure the power needed to supply their functionalities through wireless energy harvesting (EH) of impinging transmitted electromagnetic (EM) signals. Toward this, we initially explain the rationale behind such RIS capability and proceed with a presentation of the main RIS controller architecture that can realize this vision under an in-band EH consideration. Furthermore, we present a typical EH architecture, followed by two harvesting protocols. Subsequently, we study the performance of the two protocols under a typical communications scenario. Finally, we elaborate on the main research challenges governing the realization of large-scale networks with perpetual RISs. [ABSTRACT FROM AUTHOR]

This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency, power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially, the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful comparison between the energy savings from AI techniques and their associated energy costs. Finally, we discuss future research opportunities to uncover the utility of AI for energy saving.

The massive number of APs is often perceived as a complexity bottleneck for the scalable deployment of Cell-free (CF) systems. In this context, we propose various system-level results and valuable insights on some of the multidimensional design challenges: the energy efficiency (EE) gap between cellular and the worst-case scenario of scalable CF systems, the interplay between different split processing options, the fronthauling bandwidth, and the offered low-resolution hardware implementations. We discuss the need for physical layer optimizations to trade off performance versus complexity. As a first result, we reveal significant fronthaul bandwidth savings through joint power control and access point scheduling, and proper dimensioning of resolutions for the converters and fronthaul data. In the context of the EE gap analysis, we first provide a novel generalized system model framework that depicts the possibility of all levels of processing with a single system model and allows multiple transmit antennas at each AP and user in the pool of M APs and K users and multistream transmission per user. We formulate a novel optimization framework to maximize the EE where the non-convex fractions corresponding to user performance are coupled with the log-sum function due to the necessity of selecting the optimal number of data streams for each user. To solve this problem, we propose a solution to determine the optimal number of data streams, power allocations, and transmit/receive digital filters. Based on these solutions, we introduce a novel four-step alternating optimization algorithm. Regarding the EE gap analysis, in the worst-case scenario of scalable CF networks, which is of practical interest, CF remains roughly twice as energy-efficient as cellular networks. To facilitate future comparative studies, we also provide a detailed complexity analysis.

Integrated energy systems are crucial technologies that promote the shift to sustainable energy and enhance energy efficiency. Recovering low-grade industrial waste heat to power an integrated energy system enhances its total energy efficiency, making it the optimal way to optimize the energy structure. This study has developed a novel integrated energy system planning framework for industrial waste heat recovery and utilization, drawing on exergy theory. Addresses a missing aspect in the analysis of exergy network characteristics within integrated energy system planning contexts. We have developed a more detailed planning scheme by analyzing the flow and conversion of exergy in electricity and heat networks. An in-depth examination of exergy conversion and transfer processes in integrated energy systems. The simulation results demonstrate that IES planning based on exergy theory can reduce the total exergy loss of IES to achieve energy saving compared to the energy point of view. Moreover, compared to a planning scheme that does not incorporate industrial waste heat, this study has successfully reduced the system’s total energy loss by 27.3% and decreased total operating costs by 20.6%. Consequently, the energy-saving objective has been efficiently accomplished, resulting in significant economic and environmental benefits.

We consider the problem of jointly enhancing the network throughput, minimizing energy consumption, and improving the network coverage of mobile networks. The problem is cast as a reinforcement learning (RL) problem. The reward function accounts for the joint optimization of throughput, energy consumption, and coverage (through the number of uncovered users); our RL framework allows the network operator to assign weights to each of these cost functions based on the operator’s preferences. Moreover, the state is defined by key performance indicators (KPIs) that are readily available on the network operator side. Finally, the action space for the RL agent comprises a hybrid action space, where we have two continuous action elements, namely, cell individual offsets (CIOs) and transmission powers, and one discrete action element, which is switching MIMO ON and OFF. To that end, we propose a new layered RL agent structure to account for the agent hybrid space. We test our proposed RL agent over two scenarios: a simple (proof of concept) scenario and a realistic network scenario. Our results show significant performance gains of the proposed RL agent compared to baseline approaches, such as systems without optimization or RL agents that optimize only one or two parameters.”

In the face of escalating climate challenges, environmental sustainability has greatly become an urgent and non-negotiable priority, necessitating revolutionary advancements in energy management to reduce carbon footprints and drive profound improvements in overall sustainability. This paper presents an advanced optimization framework, PST-CESS, for managing power-sharing among multiple tenants within the centralized energy storage system (ESS). Our thorough evaluation demonstrates that the centralized ESS facilitated by PST-CESS substantially exceeds the performance of individualized ESS systems in pivotal areas such as peak load reduction, variability mitigation, and financial profitability. Specifically, the centralized ESS model achieves up to a 44.05% reduction in annual peak load for certain tenants and reduces electricity consumption variability by up to 57.67%. From a financial perspective, the centralized ESS model delivers remarkable advantages, reaching a break-even point in just 2.48 years, compared to the 5.08 years required for individualized ESS systems, even when accounting for battery capacity loss costs, also known as battery degradation costs. These results highlight the centralized ESS approach as a more economically advantageous and efficient solution, providing superior financial returns and optimized energy management for multi-tenant buildings. The strategic benefits and compelling evidence presented in this study strongly support the widespread adoption of centralized ESS models to maximize both economic and environmental benefits, establishing a new standard for sustainable energy management.

Wireless Sensor Networks (WSNs) are experiencing rapid growth, reshaping human lifestyles. They consist of numerous spatially distributed sensor nodes dedicated to monitoring and storing environmental data. WSNs have been a vital component of the Internet of Things (IoT) since its inception, where all sensor nodes typically connect to the Internet for data exchange. However, WSNs lack a direct Internet connection for their nodes, necessitating the use of mediators to establish connectivity. Substantial scholarly research in recent years has focused on integrating WSNs into the IoT. WSN consist of a large number of sensors but face constraints due to limited energy capacity. These nodes can perform effectively in challenging environmental conditions, but their batteries cannot be recharged or replaced in harsh environments and also will lead to problems in basis of cost. Consequently, energy preservation is crucial for the network’s sustainability. In the field of networking, routing protocols play a significant role in impacting energy consumption. The primary focus during the design of routing protocols is the optimization of energy utilization. This paper aims to minimize overall energy consumption and enhance the network’s lifespan using Cluster-based routing algorithms. Clusters, which are essentially groups of sensors, feature a central entity called the Cluster Head (CH), along with associated sensor nodes (SN). CHs are high-energy nodes responsible for collecting and transmitting data from other nodes to the base station (BS). This research introduces a novel energy-efficient clustering approach based on the Nature Inspired Improved Squirrel Search Algorithm (ISSA) with specified objective functions. The selection of CHs considers factors like node residual energy, distance from neighbors and distance from the base station, node degree, and load based on packet processing. Significantly, this research enables energy efficient, scalable and reliable networks. Further, this research also helps in optimizing the CH selection to give better performance. The performance of ISSA is compared with other optimization algorithms like SSA, GWO and LEACH. The effectiveness of this proposed approach had given better results in terms of metrics such as the number of active nodes, dead nodes, energy consumption, and data packets received by the BS.

With the Internet of Things (IoT) rapidly increasing in popularity, it is necessary to progress Beyond fifth-generation (B5G) applications to support access to a large number of devices under the strict Ultra-Reliability and Low-Latency Communication (URLLC) requirements, which are intimately tied to strict delay requirements. This article focuses on minimizing co-channel interference, implementing efficient transmitter power, satisfying the Quality-of-Service (QoS), and assigning the energy harvesting requirement of radio frequencies using a Power Splitting Ratio (PSR) to maximize energy efficiency. This article proposes an adaptive approach algorithm for power allocation and PSR to reduce energy consumption caused by a nonconvex optimization problem, to achieve a high strict level of URLLC, QoS aware immediate reward, and huge connection. In addition, the proposed Deep Reinforcement Learning-Based Reduction Action Space (DRL-RAS) allows achieving a high degree of massive access to a huge number of devices by tackling the rate requirement of IoT devices with high intelligence to increase learning efficiency by maximizing the immediate reward function and ensuring strict URLLC. The simulation’s results demonstrate how DRL-RAS decreases co-channel interference based on decision selection to provide strict URLLC. The performance of energy efficiency and QoS are based on achieving a highly strict level of URLLC. The suggested DRL-RAS training strategy performs better, gaining QoS satisfaction levels of 17.21% and 8.67% when the needed QoS rises from 0.88 bits/sec/Hz to 1 bit/sec/Hz. Moreover, the proposed DRL-RAS eliminates error prediction during the training process by updating the weight of DRL-RAS to minimize the loss function and maximize energy efficiency.

This paper designs a novel energy-efficient hybrid federated and centralized learning (HFCL) framework for training wireless traffic prediction models in aerial networks over distributed multi-access edge computing (MEC) servers where multiple network data analytics functions (NWDAFs) are embedded in the unmanned aerial vehicle (UAV)-aided MEC servers according to the framework standardized by the 3rd Generation Partnership Project (3GPP) specification. Here, UAVs can use federated learning (FL) with their part of local datasets, while the remaining local datasets can be offloaded to the centralized server for centralized learning (CL). To achieve such HFCL energy-efficiently for battery-constrained UAVs, we propose an energy-efficient computation offloading (ECO) scheme for the HFCL. We rigorously formulate analytical models for the overall energy consumption at MEC servers on UAVs and total latency for HFCL process based on the amount of offloaded datasets at each UAV. Here the energy consumption includes i) the transmission energy consumption for offloading the local dataset and ii) the energy consumption for FL processing, which includes local training and local model uploading. To balance between the overall energy consumption and total latency during HFCL process while preventing overload at UAVs, we propose a theoretical framework for the ECO problem that optimizes the amount of offloaded local datasets over multiple UAVs on aerial networks. Here, the ECO problem is formulated as the convex optimization problem under the continuous domain of the amount of offloaded datasets. Our numerical and simulation results show that our proposed framework can construct wireless traffic prediction models with an acceptable training accuracy in an energy-efficient manner over various benchmarks by striking a balance between energy consumption and overall training latency.

Wireless multimedia sensor networks (WMSNs) have gained considerable attention across various applications due to their capabilities for real-time multimedia data collection, efficient monitoring, and flexible deployment. Despite advancements, challenges persist in ensuring security, optimizing efficiency, and minimizing energy consumption due to the open remote medium, large volumes of multimedia, and inherent resource constraints in WMSNs. This paper introduces an innovative energy-efficient protection model for WMSNs, leveraging advanced deep learning techniques. The model utilizes a lightweight Tiny YOLO-v7 framework to dynamically identify objects within captured images, thereby reducing the need to transmit superfluous data. Moreover, the model combines the lightweight Speck cipher for the encryption of detected objects with a scrambling method that permutes and shuffles all image pixels. An effective key management scheme is also integrated to secure communication and image exchange among nodes within the network. By restricting encryption and transmission to sensitive images containing foreign objects, the proposed model significantly reduces operational overhead. The experimental results showcase the effectiveness of the proposed model in reducing node power consumption by approximately 49% compared to conventional methods, which encrypt and transmit all generated images. Furthermore, the model demonstrates a significant 50% improvement in extending network lifetime compared to related encryption solutions. The security analysis substantiates the model’s resistance against diverse attacks, ensuring compliance with the stringent security requirements of WMSNs. Furthermore, the model exhibits strong potential for real-time applications in dynamic monitoring and secure environments.

Mobile edge computing (MEC) represents a promising computing paradigm within Artificial Intelligence Generated Content (AIGC), offering users instant, customized, and personalized services. However, with the continued growth of the AIGC user base and the expansion of service demands, edge computing nodes are required to handle an increasing number and complexity of offloading tasks, leading to significant energy consumption issues in edge systems. This paper introduces a distributed task offloading framework (EE-A2C) that utilizes the Advantage Actor-Critic algorithm to enhance energy efficiency in edge cloud environments. This framework facilitates distributed interactions among multiple agents and edge environments with communication queues, aiming to minimize average energy consumption and latency for AIGC users. Secondly, to achieve adaptive and efficient task offloading decisions, we have developed a complex reward-sharing model based on latency and energy consumption. Finally, we have also incorporated LSTM to enhance the model’s ability to capture critical information about energy consumption, thereby promoting more robust decision-making. Compared to eight state-of-the-art energy-saving algorithms, the proposed EE-A2C framework more effectively utilizes the computing power of edge nodes, significantly reduces average energy consumption and latency, and enhances the energy efficiency of the edge cloud system.

The Internet of Things (IoT) enables the interconnection of multiple devices, typically sharing network resources. These devices must identify a suitable time to access the channel without interfering with each other, which can lead to additional energy consumption. To extend the network lifetime, cooperative strategies have been proposed that modify the device operations between the ON/OFF states to conserve energy. However, the challenge of selecting active devices for spectrum sensing increases with mobile agents, referred to as Internet of Mobile Things (IoMT), since their positions may be unknown. To deal with this uncertainty, we propose using the ordered weighted averaging (OWA) operator, which provides a framework for decision-making under ignorance, to model the position uncertainty resulting from node movement. We estimate node positions by assigning representative values for their distances to the fusion center and primary user. We then determine the optimal number of active nodes that minimize energy consumption while meeting detection constraints. We evaluate performance for different scenarios in networks of various sizes consistent with smart agriculture environments, employing optimistic and pessimistic approaches. The quality of decisions is validated under the assumption of nodes governed by particular mobility patterns.

Nano-networks are envisioned to allow several nanoscale devices to transmit and receive information. One form of such networks is electromagnetic nano-networks working within the THz band. However, high overall path loss and molecular noise experienced in the THz band, as well as limited energy storage capabilities, restrict the communication range of nano-nodes and impact network efficiency. Therefore, optimizing the nano-network resources is necessary. In this paper, we present an optimization framework employing mixed-integer linear programming (MILP) to determine the most energy-efficient routing, bandwidth, and sub-band allocation for each nano-node in an electromagnetic nano-network operating within the THz band. Our model was tested for two different scenarios related to the priority of energy saving. We also compare our proposed optimal bandwidth, routing, and sub-band allocation against less complex designs where sub-bands with fixed bandwidth are employed in nano-nodes. Furthermore, we investigate the impact of nano-node’s processing and sensing units on the overall network energy consumption and the associated optimal bandwidth allocation and routing strategy. Given the considered parameters and the model’s assumptions, the results show that using the optimal multi-hops paths with higher bandwidth allocation for the considered sub-bands can be more energy efficient than sending the traffic using a single hop and lower bandwidths, especially when the transmission power dominates in the nano-network. On the other hand, when the processing and sensing unit’s energy consumption is dominant, then single hop schemes with lower bandwidth allocation result in the minimum network energy consumption. Finally, we discuss the limitations of the proposed energy-efficient strategies and point toward possible future research directions to which the model can be adapted.

Recently, energy efficiency (EE) has been pointed out as one of the key requirements within mobile networks. The development of intelligent algorithms providing Radio Access Networks (RAN) with EE features is possible when having access to the Key Performance Indicators (KPIs), and proper control actions, e.g., cell on/off switching, or Radio Frequency (RF) channel reconfiguration. These features together with a Machine Learning (ML) framework are available in the O-RAN architecture. This paper provides an overview of the EE framework according to the use cases specified by the O-RAN ALLIANCE. It is followed by the implementation of the Energy Saving rApp (ES-rApp). The rApp utilizes Deep Q-Learning (DQL) to increase EE through intelligent RF channel reconfiguration. Simulation results show up to a 24.8% EE gain over the static RF Channel Configuration (RCC).

Wireless Sensor Networks (WSNs) play an instrumental role in monitoring and data collection across a plethora of domains. As these networks expand and get more intricate, the need for energy-efficient and smart sensing models becomes paramount. This paper introduces the energy-efficient collaborative sensing model using game theory for WSNs. This model is unique in its approach, amalgamating game theory and reinforced learning to ensure optimal energy consumption without compromising the quality of service. A cornerstone of this model is the introduction of the Selection Propensity Index (SPI), which helps in the decision-making process of choosing the right sensors for specific tasks. Moreover, the model employs a Distributed Anticipatory Time-slot selection Algorithm (DATA), an RL-based algorithm that facilitates collaborative communication. This aspect ensures that the sensors while communicating, select the time slots that would result in minimal energy expenditure and optimal data transmission. Simulations are performed over the proposed model and the results obtained showcase that the proposed model outperforms the existing similar methods in terms of energy efficiency, packet drop ratio, and throughout. The proposed model exhibits an enhancement in terms of network lifetime by 202%, throughput by 30%, and is faster by more than 60% compared to the existing methods running on full load, thereby providing an innovative approach to energy efficiency in sensor networks.

Services that require both heavy-load computation and low-latency have been increasing. To meet these requirements, an increasing number of servers are equipped with hardware accelerators such as a Graphics Processing Unit (GPU) or Field Programmable Gate Array (FPGA). These hardware accelerators can achieve higher performance computing and perform heavy-load computation at lower latency than CPUs. However, they often have fewer power-saving features than CPUs and tend to consume a constant amount of power regardless of computation-load variation. Thus, the problem of accelerators wasting power during low-load periods needs to be solved. In this paper, we propose a method in which when the computation load is so low that an accelerator is not needed, offloaded tasks are executed by the CPU without using an accelerator. On the basis of this idea, we design and implement middleware that enables an application to execute tasks by CPUs in low-load periods and offload them to an accelerator in high-load periods. We call this “processor switching”. The proposed middleware can perform the processor switching transparently without modifying or suspending applications. We evaluate the proposed middleware in a usecase of vRAN applications, which have some of the strictest latency requirements among usecases involving accelerators. The results of the evaluations demonstrate that the proposed middleware can reduce power consumption of an accelerator in low-load periods by processor switching and the switching does not cause suspensions of an application. Furthermore, the latency overhead in offloading tasks does not violate the strict latency requirements.

This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and Radio Access Networks (RANs). Creating and managing 5G and future 6G networks that are energy-efficient is of paramount importance as the wireless industry evolves to become a major consumer of energy. Also, the networks used to connect data centers and artificial intelligence (AI) computing engines with users for machine learning (ML) applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We show here that W provides a generalized analysis of power utilization and energy waste at both the component and system levels for any source-to-sink communication system. We delineate the methodology for applying W across various network configurations, including multiple-input single-output (MISO), single-input and multiple-output (SIMO), and multiple-input multiple-output (MIMO) systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy consumption of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other key performance indicators (KPIs) and key value indicators (KVIs) to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO (MU-MIMO) system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases (e.g., energy efficiency increases over the entire network) with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit (FoM) can enable the design of more sustainable next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies.

The objective of our work is to provide an in-depth analysis and compilation of device-level strategies for enhancing the energy efficiency of Machine-Type Communication (MTC). The necessity for such strategies stems from the growing demand for sustainable and energy-efficient communication systems in various industries. We begin by presenting a comprehensive background on MTC, detailing its essential characteristics, the architecture of machine-type devices (MTDs), and their diverse applications. Next, we explore a range of energy-efficient techniques designed to optimize key subsystems of MTDs. These subsystems include the radio for communication efficiency, processing power for computational efficiency, and sensing subsystems for data acquisition efficiency. Each technique is evaluated for its potential impact on overall energy consumption and the trade-offs and limitations associated with these techniques are also assessed. In concluding, the paper highlights potential future research directions in this domain, outlining the ongoing need for innovative solutions to meet the escalating demands of energy efficiency in MTC.

Wireless Body Area Networks (WBANs) have significantly enhanced various aspects of human life, particularly in healthcare, fitness, entertainment, sports, and etc. In WBANs, the sensor nodes are placed in and around the body along with the sink node, which collects the physiological data from these sensors and forwards it for further processing. The placement of the sink node is one of the critical aspects in the design of WABNs as it affects both the energy efficiency and connectivity. To this end, this paper introduces a hybrid method called Distance and Angulation based AGglomerative Clustering (DAAG). DAAG, initially clusters the WBAN sensors using Distance and Angulation based k-Mean clustering. Afterward, Agglomerative Clustering is applied to determine the optimal placement of the sink node. The results of DAAG are compared with various machine learning and optimization approaches, including D-RMS (Distance based Random mean shift clustering), Reinforcement Q-Learning Approach (QL), Humpback Whale optimization (HWOA), Multi-Angulation (MA) and Closeness Centrality (CC). Given an initial energy, the results show that the DAAG exhibits superior performance in terms of latency, packet error rate (PER), and energy consumption. DAAG shows an energy consumption of only 1.51% outperforming QL, HWOA, MA, CC, and D-RMS along with an improved localization accuracy of 0.36 m.

Device-to-device (D2D)-assisted 6G networks are expected to support the proliferation of ubiquitous mobile applications by enhancing system capacity and overall energy efficiency towards a connected-sustainable world. However, the stringent quality of service (QoS) requirements for ultra-massive connectivity, limited network resources, and interference management are the significant challenges to deploying multiple device-to-device pairs (DDPs) without disrupting cellular users. Hence, intelligent resource management and power control are indispensable for alleviating interference among DDPs to optimize overall system performance and global energy efficiency. Considering this, we present a Federated DRL-based method for energy-efficient resource management in a D2D-assisted heterogeneous network (HetNet). We formulate a joint optimization problem of power control and channel allocation to maximize the system’s energy efficiency under QoS constraints for cellular user equipment (CUEs) and DDPs. The proposed scheme employs federated learning for a decentralized training paradigm to address user privacy, and a double-deep Q-network (DDQN) is used for intelligent resource management. The proposed DDQN method uses two separate Q-networks for action selection and target estimation to rationalize the transmit power and dynamic channel selection in which DDPs as agents could reuse the uplink channels of CUEs. Simulation results depict that the proposed method improves the overall system energy efficiency by 41.52% and achieves a better sum rate of 11.65%, 24.78%, and 47.29% than multi-agent actor-critic (MAAC), distributed deep-deterministic policy gradient (D3PG), and deep Q network (DQN) scheduling, respectively. Moreover, the proposed scheme achieves a 5.88%, 15.79%, and 27.27% reduction in cellular outage probability compared to MAAC, D3PG, and DQN scheduling, respectively, which makes it a robust solution for energy-efficient resource allocation in D2D-assisted 6G networks.

Capacitive deionization is used to purify water by applying an electric charge across two porous electrodes. These electrodes are positioned in a spacer channel that transports the desalinated water. Capacitive deionization is a more energy-efficient approach to water purification than traditional methods. The process is believed to create clean water and store energy in a capacitive electrical double layer near the charged surface. Unlike previous review papers in this area, which mostly focus on energy recovery in terms of the technological advancement, this paper aims to conduct a bibliometric review and analysis of capacitive deionization cells, specifically focused on energy recovery mechanisms for treating contaminated water. The manuscript also analyzes capacitive deionization energy efficiency and energy-efficient capacitive deionization technology. Various relevant databases are utilized to select articles under specific conditions. Our findings suggest that the utilization capacitive deionization’s energy recovery mechanism from industrial wastewater has valuable opportunities for further research and growth. The selection and review of relevant articles and their analysis in this paper can aid in the systematic development of the capacitive deionization module. This would offer viable suggestions for future research towards more efficient energy recovery and consumption, which will subsequently contribute to net zero transitioning.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) networks incorporate a cell-free architecture with distributed antennas in a geographical area, and aim to deliver high data rates and support large numbers of users. It is crucial that such networks operate in an energy-efficient manner within the available spectrum. Thus, we focus on maximizing the energy efficiency (EE) of a CF-mMIMO network, which coexists with a collocated primary network in underlay mode. The EE maximization is a non-convex problem and to compute a power allocation policy efficiently, we propose a weighted minimum-mean-square-error (WMMSE) based Dinkelbach’s algorithm. Besides this, we also provide a simplified algorithm for maximum-ratio precoding in which we approximate the non-convex EE objective function with a lower bound, transforming the non-convex EE problem into a convex problem. Subsequently, we propose a policy for downlink power allocation that maximizes the EE of the secondary CF-mMIMO network while adhering to power constraints at each access point and interference constraints at each primary user. We also compare with some heuristic power allocation policies. The results demonstrate that the proposed WMMSE based power allocation scheme outperforms the heuristic power allocation schemes to a significant degree.

Federated learning is a distributed learning framework that operates effectively over wireless networks. It enables devices to collaboratively train a model over wireless links by sharing model parameters rather than personal data. However, a key challenge in federated learning arises from the limited computational and communication resources of the devices. Therefore, optimizing energy consumption is crucial for practical implementations of federated learning. In this context, we address energy minimization by applying compression techniques that reduce the number of bits required for both local computation and uplink communications. We develop an optimization framework that aims to minimize the total energy consumption across all devices involved in the training process. This framework considers quantization levels for local computation and uplink transmission, as well as the level of sparsification for parameter transmission. The optimization is constrained by requirements on latency and the target accuracy. To solve this complex problem, we first derive the required number of global training rounds, to achieve the desired accuracy. We then employ an iterative algorithm to efficiently find the optimal parameters of the studied problem. Our numerical results show that the proposed approach achieves significant performance and considerably reduces the energy consumption compared to two different federated learning baseline schemes.

In this paper, we investigate the impact of user pairing on the power consumption of 2-user non-orthogonal multiple access (NOMA) systems in the downlink. We formulate the joint power allocation and user pairing problem as a mixed-integer programming problem with the objective of minimizing the total transmit power consumption. While the pairwise power allocation strategy is straightforward, for a system with $2K$ users and K NOMA pairs, there exist ${}\frac {(2K)!}{2^{K} \times K!}$ possible pairing strategies, resulting in a combinatorial search space that grows drastically with the number of users in the system. Hence, we propose an analytical approach to obtain the globally optimum user pairing strategy. Notably, our procedure has a linear time complexity of $\mathcal {O}(2K)$ , which is a significant improvement over the suboptimal and computationally expensive methods in the existing literature. We demonstrate through extensive simulations that the proposed optimal pairing strategy can attain considerable performance gains in terms of power savings compared to benchmark schemes. In particular, in a typical deployment environment, 63% of the total power budget is saved at a mean received signal-to-noise ratio (SNR) of 15.7 dB among the users. Finally, we evaluate the energy efficiency (EE) of NOMA transmission compared to the EE achieved through orthogonal multiple access (OMA) transmission. We demonstrate that the EE gain of NOMA transmission compared to OMA is improved more than sixfold at convergence by adopting the power minimization approach studied in this work, rather than adopting the sum rate maximization approach found in the literature.

This paper presents a waveform proposal for photonic-wireless sub-THz transmissions. The concept of Continuous Phase Modulation (CPM) is first explained. Then, a mathematical model to simulate non-linear effects from photodiodes for the Amplitude-to-Amplitude (AM-AM) and a second model for the Amplitude-to-Phase (AM-PM) transfer functions is given. These models are then used to quantify non-linearities effects on linear waveforms (M-QAM) and non-linear waveforms (CPM). Afterwards, the communications range gained from using CPM over M-QAM from the simulated non-linear effects is calculated. To further appeal to the use of CPM, a study on energy efficiency is proposed. Finally, a proof of concept is given by experimentally comparing the CPM waveforms with a regular linear modulation, such as QPSK. An error-free data rate of 7.5 GBauds with Raised-Cosine, Gaussian Minimum Shift Keying (two CPM waveforms), and QPSK at a 107.5 GHz wireless 1-meter link is measured. To further estimate the robustness of the wireless link, a simulation of a communication with different transmit powers is done by adding white Gaussian noise to the acquired signals.

Satellite networks can enhance global network coverage without geographical restrictions. By applying edge computing paradigm to satellite networks, they can provide communication and computation services for Internet of Remote Things (IoRT) mobile devices (IMDs) at any time. Thus, in 6G mobile systems, satellite networks serve as a complement to terrestrial networks. This paper places its emphasis on satellite-terrestrial integrated networks (STINs). Computation offloading and resource allocation (CORA) play a crucial role in STINs given the limited communication and computation resources of satellites. In this paper, we investigate the CORA to minimize system energy consumption while ensuring the latency tolerance via jointly optimizing the offloading decision and the allocation of radio and computation resources. Taking into account the dynamic characteristics of network conditions, the CORA problem is formulated as a Markov decision process (MDP). Subsequently, an algorithm based on twin delayed deep deterministic policy gradient (TD3) is designed to automatically determine the optimal decisions. Finally, the convergence and superiority of the proposed algorithm in terms of energy efficiency are evaluated through extensive simulation experiments.

Millimeter-wave beamspace massive multiple-input multiple-output system with a lens antenna array can minimize transceiver hardware complexity without compromising performance. However, the number of supported portable user terminals cannot exceed the number of radio frequency blocks accessible at the same time, frequency, and coding resources. In this paper, we propose the integration of rate-splitting multiple access and orthogonal random precoding into the downlink of beamspace massive multiple-input multiple-output system to support a larger number of portable user terminals than the number of available radio frequency blocks while minimizing both inter- and intra-beam interferences and extending the cell coverage percentage. Then, we formulate an optimization problem to optimize the system’s overall throughput while keeping the minimum needed throughput and power budget in consideration. The nonconvex optimization issue is then approximated into a convex optimization problem using the successive convex approximation approach. Following that, we offer an alternating method to solve the approximate optimization issue and select an optimal solution. Furthermore, we deduce an analytic expression for the downlink cell coverage percentage and evaluate the effectiveness of the suggested method in terms of total throughput, energy efficiency, and cell coverage percentage. Finally, we compare the proposed method with benchmark techniques for perfect and imperfect channel state information, and numerical results confirm the superior performance of the proposed method over benchmark techniques in terms of sum throughput, energy efficiency, and cell coverage percentage.

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by the structure and function of the human brain, designed to process and learn from large amounts of data, making them particularly well-suited for tasks such as image and speech recognition. However, applications of DNNs are experiencing emerging growth due to the deployment of specialized accelerators such as the Google’s Tensor Processing Units (TPUs). In large-scale deployments, the energy efficiency of such accelerators may become a critical concern. In the voltage overscaling (VOS) technique, the operating voltage of the system is scaled down beyond the nominal operating voltage, which increases the energy efficiency and lifetime of digital circuits. The VOS technique is usually performed without changing the frequency resulting in timing errors. However, some applications such as multimedia processing, including DNNs, have intrinsic resilience against errors and noise. In this paper, we exploit the inherent resilience of DNNs to propose a quality-aware voltage overscaling framework for TPUs, named X-TPU, which offers higher energy efficiency and lifetime compared to conventional TPUs. The X-TPU framework is composed of two main parts, a modified TPU architecture that supports a runtime voltage overscaling, and a statistical error modeling-based algorithm to determine the voltage of neurons such that the output quality is retained above a given user-defined quality threshold. We synthesized a single-neuron architecture using a 15-nm FinFET technology under various operating voltage levels. Then, we extracted different statistical error models for a neuron corresponding to those voltage levels. Using these models and the proposed algorithm, we determined the appropriate voltage of each neuron (the voltage level of each column of the X-TPU). Results show that running a DNN on X-TPU can achieve 32% energy saving for only 0.6% accuracy loss.

Cognitive radio networks (CRNs) mitigate spectrum scarcity by leveraging the holes in the licensed spectrum to enable Internet of Things (IoT) devices to opportunistically access the spectrum. However, IoT devices need to sense the spectrum before they can access it, which is an energy-intensive process and hinders the practical implementation of opportunistic spectrum access for energy-constrained IoT devices. In this context, reinforcement learning-based algorithms that encourage cooperation among IoT devices to eliminate the need for constant sensing are promising candidates for practical CRN implementation. As exciting as the application of reinforcement learning to CRNs is, benchmarking the performance of different algorithms is a huge challenge due to a lack of standardized comparison metrics, especially for hybrid action spaces that comprise both discrete and continuous actions. We propose a hybrid discrete-continuous space deep reinforcement learning algorithm that maximizes the energy efficiency of CRNs by optimizing sensing, cooperation, and transmission by IoT devices. We also analyze the algorithm’s performance by setting the theoretical upper bound for throughput and find that it reaches 99.4% of the theoretical upper bound, while its discrete action-space version reaches 96% and other baseline algorithms range between 70% and 86%.

This study investigates the optimization of proportional fair (PF) and energy efficiency in simultaneous wireless information and power transfer (SWIPT)-based device-to-device (D2D) networks considering the residual battery levels of D2D users to increase the network lifetime. We establish an optimization model that determines the subchannel allocation and transmission power levels for D2D users, to maximize an objective function that combines user fairness and energy efficiency. To tackle this problem in a distributed manner, we propose a multi-agent deep reinforcement learning (DRL) model. Given that fairness considerations necessitate information about other agents, we employ the long short-term memory (LSTM) algorithm to estimate the parameters of other D2D pairs within the state space of the multi-agent DRL model. Through simulations, we compare the performance of our proposed algorithm with that of existing iterative algorithms, namely, exhaustive search (ES) and gradient search (GS). The results demonstrate that the proposed multi-agent DRL approach achieves a solution that is nearly globally optimal, while maintaining a lower computational complexity due to the parallel computing of multi-agent DRL. Furthermore, the proposed algorithm reduces the standard deviation of residual battery levels among D2D pairs and contributes to an increased network lifetime.

The quality of service (QoS) in public transportation is not at an acceptable level in high-mobility scenarios as penetration loss severely degrades signal quality and reduces the achievable data rate. Also, high mobility requirement is included in 5G as an essential part of its design. In this paper, a background of mobility management in high-speed scenarios and a review of underlay mobility management are presented. After a discussion of the usual high mobility challenges, major techniques for dealing with mobility management are reviewed, including architectural support, handover optimization, and employing new techniques like non-orthogonal multiple access (NOMA), and energy efficiency maximization. Also, challenges and prospective research directions for each technique are provided. The primary objective of this research study is to enhance knowledge of mobility management in high-speed vehicular scenarios, specifically by employing network architectural support in an upcoming heterogeneous mobile network. The primary objective of this research study is to enhance knowledge of mobility management in high-speed vehicular scenarios, specifically by employing network architectural support in an upcoming heterogeneous mobile network.

In an effort to make pumped hydropower storage (PHS) technology feasible for regions with a flat topography, recent research shows promising results using a contra-rotating reversible pump-turbine at low-head. In this study, the impact of dual variable speed and inlet valve control is analyzed to evaluate the effect of these three degrees of freedom (DOFs) on the system efficiency and operating range. To this end, analytical models are described to assess pump-turbine performance, conduit losses and electromechanical losses. Methodologically, optimal efficiency maps are computed for every combination of the three DOFs to evaluate individual and combined effects on the overall efficiency. Furthermore, three energy storage cycles are analyzed to further study the performance in realistic use-cases. Key conclusions include an increase in round-trip efficiency by combining variable speed ratio and inlet valve control of 5.6% and 2.0% compared with only variable speed ratio control and variable inlet valve control, respectively. Furthermore, it is shown that using only 1 DOF significantly limits the operating range, with the addition of a variable inlet valve granting a higher impact than a variable speed ratio. Combining inlet valve and speed ratio control leads not only to the highest efficiency, but also the largest operating range, with a maximum round-trip efficiency of 67.5% and an energy storage capacity of 58.6 Wh/m2. The results confirm that exploiting both dual variable speed operation and inlet valve control yields the maximum efficiency and operating range, and is thus the preferred topology for contra-rotating reversible pump-turbines in low-head operation.

Edge Computing (EC) has emerged as a pivotal paradigm, offering solutions to address the challenges posed by latency-sensitive applications and to enhance overall network performance. In EC environments, efficient task offloading is crucial for minimizing latency and energy consumption while maximizing resource utilization. In this paper, we propose a hybrid task offloading approach (HybridTO) integrating Grey Wolf Optimizer and Particle Swarm Optimization. Our approach aims to optimize energy consumption and fulfil latency constraints in EC environments by taking into account various factors such as capacity constraints, proximity constraints, and latency requirements. Leveraging the collaborative capabilities inherent in EC servers, HybridTO offers a comprehensive solution to the task offloading problem. Through extensive simulations, we evaluate the performance of HybridTO against baseline approaches, demonstrating its superiority regarding energy usage, offloading utility and response delay, especially under conditions of limited resources. These results underscore the effectiveness of HybridTO as a promising solution for energy-efficient task offloading in EC environments, offering valuable insights for further research and development in this field.

This survey paper provides a comprehensive overview of integrating Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS) in wireless communication systems. IRS is known as reconfigurable metasurfaces, have emerged as a transformative technology to enhance wireless communication performance by manipulating the propagation environment. This work delves into the fundamental concepts of MIMO and IRS technologies, exploring their benefits and applications. It subsequently investigates the synergies of resource allocation and energy efficiency that emerge when these technologies are combined, elucidating the IRS improved in MIMO systems through signal manipulation and beamforming. Through an in-depth analysis of various techniques and cutting-edge algorithms in resource allocation and energy efficiency can explore the key research areas such as optimization techniques, beamforming strategies and practical implementation consideration. Furthermore, it provides open research directions, individually addressing topics such as limitations of resource allocation and energy efficiency in the MIMO IRS system. This paper offers insights into MIMO-enabled IRS systems challenges and future trends. Through presenting a consolidated view of the current state-of-the-art, this survey underscores their potential to revolutionize wireless communication paradigms, ushering in an era of enhanced connectivity, spectral efficiency and improved coverage.

The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising unprecedented levels of openness, flexibility, and intelligence within radio access networks. Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms. This paper undertakes a thorough examination of Open RAN, particularly focusing on its energy consumption aspects, which are pivotal for ensuring the sustainability of future wireless networks. In this paper, we review and compare Open RAN architecture with previous network architectures. In particular we focus on O-RAN Alliance specifications. Additionally, we explore the deployment of ML across various facets of Open RAN and highlights how to estimate the energy consumption of ML models. Through constructing explicit energy consumption models for key O-RAN components, we provide a granular analysis of their energy profiles. Finally we compare the energy dynamics of O-RAN against traditional RAN architectures, delineating the impact of virtualization and disaggregation on energy efficiency.

In the rapidly developing field of wireless communication, the control of beams in Reconfigurable Intelligent Surfaces (RISs) has emerged as a promising element beyond 5G wireless communication systems. Due to their distinctive reflecting elements, Reconfigurable Intelligent Surface (RIS) is essential in several operations, including beamforming and beam steering. However, the optimization of these functions necessitates complex solutions. In this study, the authors introduce the Feedback DNN strategy, which combines the Feedback Neural Network and Deep Neural Network techniques specifically designed for channel estimation. This methodology utilizes deep neural networks to provide the RIS and user equipment communication path, enabling improved beamforming and steering capabilities. This study highlights the incorporation of machine learning (ML) within the field of communication engineering, intending to enhance the reliability and effectiveness of wireless communication systems. The contributions encompass a novel methodology for managing RIS beams, sophisticated approaches for channel estimates, optimization of beam operations, and the potential to enhance the performance of wireless systems by utilizing RISs via a Feedback DNN (called DeepRISBeam). The proposed approach is compared against other state-of-the-art ML approaches regarding their training accuracy. At the same time, it evaluated Bit Error Rate performance in high- and low-mobility vehicular communication scenarios.

Recent improvements in the accuracy of machine learning (ML) models in the language domain have propelled their use in a multitude of products and services, touching millions of lives daily. These new levels of accuracy have been attained mainly through exponential growth in model size, creating a new category of models known as Large Language Models (LLMs) and leading to a substantial increase in computing and energy demands. While recent studies have focused on measuring and improving the energy consumption of LLMs during training, inference has received little attention. In this article, we present an approach to profile the energy consumption of LLMs during inference and leverage it to improve energy efficiency. For this, we deploy several state-of-the-art LLMs and observe how model size, number of layers, parallelized attention, and even vocabulary size affect their energy consumption. In addition, we leverage input batch size and different quantization levels to optimize their inference energy efficiency and latency.

Spectral efficiency (SE) and energy efficiency (EE) are two main metrics for the transmit design in Visible Light Communications (VLC). However, the SE-optimal and EE-optimal strategies may be in conflict with each other to some extent. In this paper, we investigate the the tradeoff between EE and SE in rate splitting multiple access (RSMA)-aided VLC systems to maximize the system resource efficiency (RE) taking both perfect and imperfect channel state information (CSI) into consideration. For the scenarios with perfect CSI, we explore the joint precoding design and common rate allocation to maximize the RE of RSMA-aided VLC systems under the constraints of quality of service (QoS) and linear operation region (LoR) of LED, and propose a primal-dual-gradient-based precoding strategy. Furthermore, in the presence of CSI estimation errors, we propose a worst-case robust precoding design by exploiting quadratic transform and $\mathcal {S}$-Procedure. Numerical results indicate that the proposed resource efficient RSMA algorithm achieves the tradeoff between EE and SE.

This article aims to address an existing research gap in the study of the most widely used mathematical procedures in the field of automatic control of energy-sensitive industrial processes. In these types of processes, as is the case of cold storage in the food sector or the pharmaceutical industry, applying energy efficiency measures is very risky. This is because the margin of variation in the temperature of the processes is very small, since the product to be manufactured or preserved is very sensitive. This is where the developments of the Internet of the Things showcase their usefulness because they allow measurements to be taken with great accuracy and to verify the effectiveness of energy efficiency proposals. Nevertheless, there are very few studies and developments on automation measures in energetically sensitive industries. This is the research gap that the present work aims to shed light on, proposing a method for optimizing the process of automatic revision in a refrigerated food warehouse. Said method prominently employs control charts, as they allow for a relatively easy set up and require minimal intervention but can be revised manually if so desired. The analysis also includes an auxiliary variable that measures the impact of the variations in the system. Improvements are also provided to the procedures and variables, with which the most commonly used methods of efficiency control in the industry can maintain good results in energy-sensitive industries. Finally, the best selection of charts for the chosen variables is then discussed and justified.

Resource allocation in vehicular communication often encounters stringent constraints that are hard to satisfy due to high mobility and a complicated communication environment, making the optimization problem infeasible. However, even in such infeasible scenarios, the resource allocation strategy should be able to provide viable solutions within a short computation time. To address this challenge, we explore the potential of a deep learning (DL) framework that can provide reasonable resource allocation solutions even when certain constraints cannot be satisfied. In particular, we focus on resource allocation to maximize overall energy efficiency while ensuring minimum spectral efficiency, where resource allocation constraints may not always be satisfied, unlike traditional works that consider only the feasible scenarios. We propose a DL framework that uses deep neural network (DNN) models to approximate resource allocation. In addition, an unsupervised learning-based training methodology is developed such that the DNN model approximates the optimal resource allocation for feasible cases while for infeasible cases, the trade-off between the objective and the constraint can be achieved, all with low computation time. Our simulation results confirm that near-optimal performance can be achieved for feasible cases, while achieving performance that balances objective and constraint satisfaction in the case of infeasible scenarios, all with low computational overhead.

There is a growing demand for low-power network devices; therefore, enabling technologies for the Internet of Things (IoT) is significantly important. This paper proposed resource allocation by maximizing the harvested energy to substantially improve Energy Efficiency (EE) and regulate transmission power for the scheduled IoT devices. Energy Harvesting (EH) is a viable technology that enables long-term and self-sustainable operations for IoT devices. The Simultaneous Wireless Information and Power Transfer (SWIPT) has been proposed as a promising solution for maximizing EE while ensuring the quality of service of all IoT devices, where the ultra-low power devices harvest energy in Power Splitting (PS) mode. This paper applied the proposed Optimal Transmit Power and PS Ratio (OTPR) algorithm to maximize the EE for SWIPT based on the partial derivative of Lagrange dual decomposition methods. The algorithm jointly optimized the allocation of the channel, PS, and power control to solve the distributed non-convex and NP-hardness caused by co-channel interference. A novel training was proposed for Deep Neural Network (DNN) algorithms chain rules to minimize the loss function based on updating the parameters of the weights hidden layer and convergence training to achieve near-optimal performance and minimize unneeded label data. The simulation results showed that the DNN training for the chain rule provided a near-optimal performance EE with the shortest training time. This observation indicated that decreasing the loss function at every training optimizes the co-channel conditions for IoT devices by assigning the EH requirement to meet the minimum harvesting need.

Traffic Classification (TC) systems are designed to identify the applications generating network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques, surpassing traditional methods in complex scenarios, including those with encrypted traffic. Notably, state-of-the-art DL-based TC systems have been developed for wireless networks using Physical Layer (L1) packets. This approach overcomes the common limitation in TC research that assumes traffic flows within a wired network under a single network management domain. Despite their benefits, DL-based TC systems often demand significant computational resources, typically available only in cloud environments. Consequently, deploying models at the edge is often infeasible due to their resource-intensive nature, given their original training and optimization for high-resource environments. The inherent challenge lies in adapting these systems for edge computing scenarios, including deployment at access points. In this paper, we propose a novel methodology that exploits expert knowledge in combination with recent advances in Multi-Task Learning (MTL) and Deep Neural Network (DNN) optimization to allow spectrum-based TC systems to run on constrained devices. This paper propose a well-defined and innovative methodology for resource-efficient, spectrum-based TC to address this issue, combining MTL with DNN optimization techniques. Performance evaluations on an NVIDIA Jetson TX2 demonstrate that our most optimized MTL model, handling four TC tasks, can reduce memory requirements by a factor of 2.65x and improve execution time by 3.6x compared to sequential execution of four Single-Task Learning (STL) models in a server-grade configuration, with minimal accuracy impact (less than a 0.5% drop) and energy efficiency of 0.97 millijoules per sample at inference. Compared to other edge platforms such as the Raspberry Pi model 3B+ (RPI3B+) with a low-power Artificial Intelligence (AI)-accelerator such as the Coral Tensor Processing Unit (TPU), the NVIDIA Jetson achieves a 12-fold improvement in energy efficiency with no impact on accuracy.These are the first available results to provide a benchmark for different performance metrics (memory, computing, energy) over heterogeneous constrained devices for this type of TC system.

With the current utilization of Floating, Production, Storage, and Offloading (FPSO) units by the petroleum industry, and the trend towards optimizing the energy efficiency of electrical systems, Variable Frequency Drives (VFDs) play a crucial role in enhancing the performance and power quality (PQ) of the power system on these offshore platforms. The FPSO unit operates in various modes, depending on its regular activities, such as contingency, production and offloading. Each operating mode has multiple grid configurations with different generation and load connections, including the contingency mode that is used for planned maintenance or failure in one of the aeroderivative gas turbine generators (GTGs). During the contingency mode, the FPSO unit runs with two GTGs and implements load shedding. Therefore, this paper investigates a specific case of the contingency operating mode, considering scenarios with zero, one, two, and three VFDs connected to the largest motors. Additionally, it analyzes VFDs with both passive and active front-end rectifiers, aiming to minimize the number of loads to be shed during contingency. The main result shows that by using the active front-end VFDs, 14.3 MW of loads that would be shut down during contingency mode can continue to operate.

In this paper, we undertake an examination of the complicated challenges associated with bandwidth allocation and power control in multi- unmanned aerial vehicles (UAVs) network. The focal point of our investigation is the formulation and subsequent proposition of a novel algorithm aimed at optimizing bandwidth utilization and energy efficiency across multiple UAVs. The intricate joint optimization problem is cast as a nontrivial nonlinear programming (NLP) challenge; thus, to efficiently address it, we split it into two subproblems. In the first sub-problem, UAVs bandwidth resources are being optimized while considering that they are operating at their maximum transmit powers. In the second sub-problem and after optimizing bandwidth allocations, UAVs transmit powers are optimized based on their inter-links speed. Sequential quadratic programing (SQP) based on coloring graph representation is proposed to address the first sub-problem, while $\mathcal {M}$ - matrix theory is employed to address the second one. Rigorous numerical simulations are conducted to prove the effectiveness of the proposed scheme against other benchmarks in maximizing both data rate and energy efficiency of the proposed multi-UAV network.

In the context of service provisioning, the integration of Network Functions Virtualization (NFV) enhances the flexibility, scalability, and programmability of telecommunication networks. However, this integration introduces challenges, particularly in optimizing the placement of Virtualized Network Functions (VNFs) within the NFV Infrastructure (NFVI). Existing studies have predominantly focused on well-connected, mains-powered ecosystems like datacentres and cloud networks. In contrast, the aim of this paper is to identify a solution that distributes and deploys a Wireless Mesh Network (WMN) as the backbone for a disaster management communication and service infrastructure. Given the mobility of mesh routers in such scenarios, these devices are often battery-powered. Consequently, the placement of VNFs directly impacts the energy consumption in the network and, subsequently, its lifetime. The proposed solution for the energy-efficient placement of VNF is formulated as a multi-objective optimization problem. This context introduces different approaches and proposes a heuristic algorithm to optimize the placement of VNFs. The evaluation results indicate that the proposed algorithm outperforms prior alternatives in various scenarios. Notably, it surpasses established methods like the Nondominated Sorting Genetic Algorithm II (NSGA-II), commonly used to solve similar problems. This research signifies a significant advancement in addressing the specific challenges associated with NFV integration in wireless mesh networks, particularly in disaster management contexts.

The advancement of Internet of Everything (IoE) propels the fast growth of next-generation, such as 6G networks, leading to a new era of coverage, connectivity, and technological innovation, which calls for novel approaches to address rising energy consumption and maximize resource use. The proposed article presents a robust hybrid algorithm that combines leader-based optimization and Adaptive Differential Evolution (DE) in the framework of the Energy Efficient Hybrid Evolutionary Algorithm (EEHEA), which is specifically designed for the complex environment of IoE-enabled 6G networks. The scheme EEHEA combines the efficacy of leader-based optimization (LBO) for an effective decision-making process and adaptive differential evolutionary optimization (ADE)’s dynamic network-parameters adaptation, enhanced convergence, and global searching ability, persistently fine-tuning optimization strategies based on the dynamics of the network. Combining these components into the scheme EEHEA allows it to balance local exploitation and global exploration effectively. This implies better resource allocation and improved energy efficiency in ecosystems with IoE-driven 6G (IoE-6G). The outcomes report that the scheme EEHEA can address the rising energy consumption issues and enhance the efficiency of IoE-6G. Based on simulation experiments, the proposed scheme EEHEA can demonstrate faster convergence times, higher accuracy, and superior flexibility concerning changing network conditions. Its capability to handle energy-related challenges and navigate complex network environments with resilience shows the ability to enhance the performance of IoE-6G. The EEHEA scheme reports its efficacy over state-of-the-art schemes regarding localization, latency, coverage, and energy expenditure performance metrics.

The integration of Software-Defined Networking (SDN) technology in Smart Grids has emerged as a transformative approach to modernizing energy infrastructures and enhancing operational efficiency. This comprehensive review paper explores the advancements, challenges, and future perspectives of SDN implementation in Smart Grid environments. It delves into the applications of SDN in areas such as real-time monitoring, energy distribution optimization, grid resilience, integration of renewable energy sources, and demand response management. Additionally, the paper addresses key challenges including security concerns, interoperability issues, scalability constraints, and regulatory compliance requirements that accompany the adoption of SDN in Smart Grids. Looking ahead, the paper discusses future perspectives such as leveraging artificial intelligence, edge computing, and blockchain technology to further enhance the capabilities of SDN in Smart Grids. Through an in-depth analysis of current developments and future trends, this review provides valuable insights for researchers, practitioners, and policymakers seeking to harness the full potential of SDN for advancing Smart Grid infrastructures.