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In this paper, we improve networks' spectral efficiency (SE), extend networks' lifetime, and maximize networks' energy efficiency (EE) of two-way full-duplex (FD) relay networks. Firstly, to improve networks' SE and to extend networks' lifetime simultaneously, we design a two-way FD relay transmission strategy with simultaneous wireless information and power transfer and direct links (DLs). The designed transmission strategy can complete a bidirectional communication in only one time slot with the exists of DLs and the energy-constrained relay node. With the designed transmission strategy, we further give the characteristics of relay amplification factor, the analysis of the designed transmission strategy, and the EE analysis of traditional half-duplex two-way amplify-and-forward relaying. Secondly, to maximize networks' EE, we present both the EE maximization problems and analyses of the designed transmission strategy with equal power allocation and optimal power allocation. To solve the EE maximization problems, we further propose the alternating optimal algorithm and give complexity analysis of the algorithm. Simulations show that our designed transmission strategy can improve the SE and EE of the networks. [ABSTRACT FROM AUTHOR]

In this paper, we prove the existence of a reservoir that has a finite-dimensional output and makes the reservoir computing model universal. Reservoir computing is a method for dynamical system approximation that trains the static part of a model but fixes the dynamical part called the reservoir. Hence, reservoir computing has the advantage of training models with a low computational cost. Moreover, fixed reservoirs can be implemented as physical systems. Such reservoirs have attracted attention in terms of computation speed and energy consumption. The universality of a reservoir computing model is its ability to approximate an arbitrary system with arbitrary accuracy. Two sufficient reservoir conditions to make the model universal have been proposed. The first is the combination of fading memory and the separation property. The second is the neighborhood separation property, which we proposed recently. To date, it has been unknown whether a reservoir with a finite-dimensional output can satisfy these conditions. In this study, we prove that no reservoir with a finite-dimensional output satisfies the former condition. By contrast, we propose a single output reservoir that satisfies the latter condition. This implies that, for any dimension, a reservoir making the model universal exists with the output of that specified dimension. These results clarify the practical importance of our proposed conditions. [ABSTRACT FROM AUTHOR]

Composite cutter bar with high dynamic stiffness and damping can be used to suppress chatter in high speed milling process. However, for a rotating composite cutter bar, a new region of instability occurs at the high-speed range because of such factors as the internal damping of the composite cutter bar and gyroscopic effect. This paper proposes a dynamic model of high speed milling system with composite cutter bar in fixed and rotating coordinate frame. Based on the viscoelastic constitutive relation of composite material, the damping model is established by using the energy approach. The dynamic equations of the milling system are established according to Hamilton principle. The natural frequency and decay rate of composite cutter bar are studied by the proposed theory. The stability results are obtained by using the semi-discretization method and verified by the time domain response curves. The influences of gyroscopic effect, internal damping, ply angle and aspect ratio are examined. It is shown that gyroscopic effect has a significant effect on stability lobe diagrams at the high speed range. A new unstable region appears at the high speed range when the internal damping of the composite cutter bar is included. [ABSTRACT FROM AUTHOR]

The emergence of Mobile Edge Computing (MEC) not only provides low-latency computing services for the User Equipment (UE), but also extends the battery life of the UE. However, the computational resources of MEC servers are usually limited, and how to efficiently offload UE's task and allocate the resources of MEC servers has become a research hotspot in MEC. In this paper, we develop an improved arithmetic optimization algorithm (IAOA) to optimize the convergence speed and convergence accuracy of the arithmetic optimization algorithm. Then a task offloading algorithm based on IAOA is designed to reduce the cost of offloading tasks in the framework including a single MEC server and multi-UE. The proposed algorithm jointly optimizes the task offloading strategy of the UEs and the resource allocation of the MEC server, meanwhile, models the weighted sum of delay and energy consumption as the system cost, with the goal of minimizing the system cost while satisfying the delay and energy consumption constraints of the tasks. Simulation results show that the proposed algorithm can effectively reduce the system cost and achieve a performance improvement of up to 20% compared with the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Considering the trend of scaling down conventional technologies toward next-generation devices and the importance of new practical and efficient procedures in increasing the efficiency of digital computing circuits, this paper presents three full adder circuits. Suggested circuits, named Design-1 to Design-3, are proposed based on FinFET technology with channel length of 11 nm. In order to optimize energy consumption and area occupation, they have been implemented based on approximate computing with 4, 10, and 12 transistors, respectively. The main characteristics of the presented circuits lie in their utilization of the gate diffusion input technique, which has resulted in the generation of only three errors in their output. Diverse objectives for error generation have been incorporated into these circuits, positioning them as highly accurate competitors when compared to the most state-of-the-art designs. Also, due to the use of low number of transistors, which has led to low internal nodes, these circuits are in a favorable position in terms of power consumption and speed. To evaluate these circuits, extensive simulations are conducted considering various implementations, including single-bit configurations and integration within 4-bit ripple carry adders with 2 and 4 approximate bits. Based on the figure of merits used consist of diverse design parameters, Design-1 with the least number of transistors has a difference of about 85% improvement compared to the nearest competitor, while design-2 and design-3 circuits are also placed in desirable rankings. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of renewable energy consumption, total factor productivity (TFP), income, imports, and exports on consumption-based CO2 emissions in Turkey from 1990 to 2019, utilizing the Autoregressive Distributed Lagged (ARDL) method. The findings indicated that the use of renewable energy, total factor productivity and exports negatively influence CO2 emissions. On the other hand, the positive impact of income and imports on CO2 emissions was found. The negative impact of renewable energy consumption and total factor productivity highlights the importance of implementing environmentally friendly measures in the energy sector (particularly raising the proportion of renewable energy in overall energy consumption) and technological innovation. [ABSTRACT FROM AUTHOR]

Energy efficiency in buildings has a central role to play in reaching the climate neutrality goal set by the European Green Deal. With detailed knowledge of their building stock and the profile of their occupants, regional and local authorities can forge an important link between financiers, industry professionals and homeowners to ensure the successful deployment of financial schemes that support the uptake of energy efficiency upgrades. This paper provides a first assessment of the role of regional and local authorities by reviewing relevant initiatives and programmes on energy efficiency. Based on a questionnaire completed for more than 150 schemes, it explores how European municipalities and regions stimulate energy upgrades in residential, commercial and public buildings through public financial support. It is found that 60% of the examined schemes are offered in the form of grants and subsidies, and 45% of them targeted residential upgrades. The use of EU cohesion policy funds in supporting regional schemes, and promotion of energy efficiency investments at local level through the European Covenant of Mayors initiative are also explored. In spite of possible resource limitations at this level of governance, regional and local authorities are in a good position to utilise European structural or research funds to develop financial schemes, as standalone programmes or blended with national ones, thus providing additional support and funds for deep renovations. The results suggest that energy efficiency in buildings has become an important part of local and regional strategies in several European countries, but could be further enhanced. [ABSTRACT FROM AUTHOR]

Solar absorber is the key component to realize efficient utilization of solar energy. In this paper, only titanium nitride (TiN) is used to create a broadband, wide-angle, polarization insensitivity solar absorber. The average absorption of the absorber in the spectral range of 300-2500 nm is 91.3%. The distributions of electric and magnetic fields indicate that the strong absorption is caused by the coupling effect of surface plasmon resonance and guided mode resonance. Investigate the effects of the geometric parameters of the absorber on the spectral absorption performance. Moreover, we also discuss the absorption performance of the solar absorber at different incidence angles. It is demonstrated that even at a large incidence angle of 60°, the average spectral absorption can exceed more than 70%. The proposed solar energy absorber is also insensitive to polarization. The research results in this work could benefit the applications in solar thermophotovoltaic system. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) currently have numerous applications, especially in tracking and observing non-human activities. Sensor nodes in WSNs are known to have limited lifespans due to continuous sensing, which causes the battery to drain quickly. Therefore, Energy consumption is a significant research issue in WSN-assisted applications. Energy conservation now places a high priority on exact clustering and the choice of the best route from the sensor nodes to the sink. This research paper proposes a fuzzy with adaptive sailfish optimizer (ASFO) for cluster head selection and improved elephant herd optimization approach to find the most efficient shortest path route to preserve energy efficiency in WSNs. The suggested hybrid approach was implemented in MATLAB and achieved results are compared to those of four widely-used techniques, such as improved artificial bee colony optimization-based clustering (IABC-C), genetic algorithms (GA), particle swarm optimization (PSO), and hierarchical clustering-based CH election (HCCHE) approach. The Fuzzy with ASFO technique improves the Quality of Service (QoS) of performance metrics such as energy usage, packet loss ratio, end-to-end delay, packet delivery ratio, network lifetime, and buffer occupancy. The results show that the suggested Fuzzy with SFO has a better packet delivery ratio (99.8%), packet latency (1.12 s), throughput (98 bps), energy usage (10.90 mJ), network lifetime (5400 cycles), and packet loss ratio (0.6%) than the existing methods (PSO, GA, IABC-C, and HCCHE algorithms). [ABSTRACT FROM AUTHOR]

To construct a femtocell based ultra-dense network (UDN) is the important way to satisfy the ever-growing data traffic demands in the next generation mobile networks. However, it causes frequent handovers (HOs), thus leading to negative consequences such as increased power consumption and poor quality of service (QoS). This paper represents a research on the uplink handover (UL-HO) being watched recently and proposes a new method to further improve the energy efficient and QoS. First, we confirm the UL-HO mechanism, formulate the power consumption caused by UL-HO and compare downlink handover (DL-HO) with UL-HO in terms of the performance parameters including the power consumption, handover rate (HOR), handover failure rate (HOFR), ping-pong rate (PPR), delay, packet loss rate (PLR) and throughput during handover (HO). Then, it is proved through simulation that the reliable target cell determination scheme considering uplink reference signal received power (UL-RSRP), available bandwidth, direction of user equipment (UE), and admission control is lower in HOR, HOFR, PPR, power consumption, delay and PLR. In particular, we propose a new algorithm to determine the reasonable target cell considering UL-RSRP, available bandwidth and moving direction of UE, thus reducing HOR and PPR. The improved UL-HO scheme can be implemented easily by adding some functions besides the upper-layer protocol, while it can provide a lot of benefits such as reduced power consumption, delay, frequent HOs resulting in reducing HOR and PPR. Accordingly, this can be introduced into 5G as a HO scheme. [ABSTRACT FROM AUTHOR]

In recent years, content-centric networks (CCN) have introduced the significant feature of in-network caching, which saves transmission energy consumption in content distribution. However, because of the extra logic needed for the caching mechanism, one of these networks' main challenges is optimizing the trade-off between transmission and caching energy consumption. Moreover, in an energy-aware CCN, less popular content is cached near the content provider despite more popular content caching near end users. Therefore, in real-time or delay-sensitive traffic with less popularity, this caching strategy degrades the quality of service, drops delayed chunks, and wastes energy consumption. Accordingly, designing an appropriate content caching policy to improve energy efficiency and service quality is a long-term goal of the green CCN. This paper considers minimizing energy consumption and the queuing delay in CCN as a multi-objective optimization problem. Thus, to drive the proposed approach, called ED-CCN-MOP, the CCN queuing delay for receiving the Interest and Data packets is analyzed and formulated. Furthermore, the ED-CCN-MOP model is solved using the proposed Non-dominated Sorting Markov Approximation (NSMA) method. According to the numerical results, the NSMA algorithm outperforms the NSGA-II, NSGA-III, and MODA algorithms by about 49%, 46%, and 38%, respectively, in terms of their average energy-delay-product metric with the possibility of distributed implementation. Furthermore, the quality of NSMA solutions is evaluated and compared using performance metrics. The results of this evaluation indicate that NSMA consistently achieves a high level of performance. [ABSTRACT FROM AUTHOR]

Routing protocol plays a vital role in FANETs (Flying Ad-Hoc Networks) by facilitating communication between nodes and enabling the establishment of efficient routes. In a FANET, where the nodes can be drones, airplanes or other unmanned aerial vehicles, mobility is a key aspect that requires proper route management. Among the most well-known and widely used routing protocols in different types of networks is AODV (Ad hoc On-Demand Distance Vector). AODV is a reactive routing protocol specially designed for ad hoc networks, including FANET networks. It offers an efficient solution to establish dynamic routes between nodes when direct communication between a source node and the destination node is not possible. However, most of the studies available in the literature mainly focus on the comparison between different routing protocols. They often neglect to examine in detail the performance of the same routing protocol under different circumstances or conditions. This paper offers a thorough investigation into the influence of noise on the AODV protocol in FANET networks. Furthermore, we introduce a proposed enhancement to the AODV protocol, specifically designed to improve its ability to withstand the effects of noise. Additionally, we present simulation results that evaluate the performance of AODV in terms of throughput, End-to-End Delay, Packet Delivery Ratio (PDR), and energy consumption. [ABSTRACT FROM AUTHOR]

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031. [ABSTRACT FROM AUTHOR]

Open water transposition channels in hot and arid regions, like those in the São Francisco River Integration Project (PISF) in Brazil, suffer significant water losses through evaporation. This paper proposes covering these channels with photovoltaic (PV) panels to reduce evaporation while simultaneously generating clean energy. The research aims to quantify water savings and energy generation potential across all channel lengths and assess whether the generated solar power can substitute grid electricity for powering the transposition pumps during peak hours, thereby enhancing energy efficiency. This study analyzed the state-of-the-art of PV generation and calculated their solar potential. Identified the specific characteristics of PISF channels and watercourses considering the regional geography, meteorology, irradiation, and social peculiarities. And, finally, assessed the feasibility of covering the watercourses with solar panels. The results reveal that covering all current PISF channels with PV panels could save up to 25,000 cubic meters of water per day, significantly contributing to water security and improving the quality of life for the local population. Additionally, the project could generate 1200 gigawatt-hours of electricity annually, meeting the energy demands of the transposition pumps during peak hours and promoting energy efficiency within the project. This research paves the way for utilizing PV technology to address water scarcity challenges and enhance the sustainability of water infrastructure projects in arid regions worldwide. [ABSTRACT FROM AUTHOR]

A mechanism of fog computing environment is employed in order to enhance the cloud computing services toward the edge devices in a range of locations with low latency. A fog computing environment is effective when compared to cloud computing for providing communication between various edge devices such as smart devices and mobile devices used by users in the same location. Even though fog servicing extends the best services of cloud computing, it also suffers from a set of security threats like authentication, key management, data privacy and trust management. Authentication with effective key management between edge devices is the most pressing security issue in fog computing. This paper proposes an effective two-way authentication between edge devices with key management in fog computing environments (TAKM-FC). The edge nodes are the user's mobile devices and set of smart devices controlled by the fog server. To improve the proposed authentication system, we have made use of techniques like fuzzy extractor and one-way hash with cryptographic primitives. The proposed TAKM-FC scheme is validated mathematically based on the ROR model and then verified using the ProVerif tool. The TAKM-FC scheme has been evaluated using iFogSim to measure the performance parameters like throughput, end-to-end delay, packet loss, energy consumption and network usage. The overhead analysis of the proposed scheme is carried out and shows that the computation cost, communication cost and storage cost are improved by 11–21%, 8–19% and 6–13%, respectively, compared to existing schemes. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining the number and placement of controllers is known as the controller placement problem (CPP). In this paper, game theory is used to solve CPP by identifying the optimal number of controllers. Two algorithms, golden eagle optimization (GEO) and grey wolf optimization (GWO), are utilized to find the most efficient mapping between switches and controllers. Since CPP is a discrete problem, GEO and GWO have first been discretized and then hybridized to form a new algorithm called GEWO. This algorithm is used to discover the most efficient mapping between switches and controllers. Additionally, simulated annealing is employed for better local search. The effectiveness of this approach is evaluated using different numbers of controllers on four well-known software-defined networks from the Internet Topology Zoo. The results are compared against various existing algorithms in the field, and it is observed that GEWO outperforms the competition. The findings demonstrate that GEWO reduces load imbalance by 24.07%, decreases end-to-end delay by 20.95%, and lowers average energy consumption by 11.65%. [ABSTRACT FROM AUTHOR]

Modern applications need to flexibly adjust the processing process according to the different environments and real-time processing, thus putting forward higher requirements for the reconfiguration performance of coarse-grained reconfigurable architecture (CGRA). Traditional dynamic reconfiguration requires constant configuration of the processor by the host computer. Still, this reconfiguration method severely restricts switching different tasks, making it difficult to achieve dynamic real-time reconfiguration and limiting performance improvement. This paper presents a real-time monitoring-based self-reconfiguration mechanism (RMSRM) based on the implementation of CGRA. This mechanism improves the reconfiguration performance by monitoring the execution process of the array in real-time through a programmable controller on the basis of the homogeneous processing element (PE) array and dynamic scheduling of PE array resources according to different application requirements. The proposed RMSRM is capable of feeding array execution state information to the programmable controller within 1 clock cycle, switching configurations between different tasks within 10 clock cycles, and shutting down the unnecessary PE/PEs according to the current configuration. To verify the correctness and efficiency, we model hardware with synthesizable RTL coding and implement it on FPGA and a chip. The experimental results show that RMSRM can effectively reduce the reconfiguration overhead compared with traditional reconfiguration methods. The volume of bits in the configuration file has decreased by an average of 39.37%, and the reconfiguration time has decreased by an average of 43.57%. Based on the SMIC 55nm process, the operating frequency can reach 320 MHz. Meanwhile, the resource consumption is 80506 logic gates, the chip area is 12 mm 2 , and the energy efficiency is 312 GOPS/W. [ABSTRACT FROM AUTHOR]

The energy consumption from the building sector accounts for more than 21% of the total in China, where the energy-use intensity of public buildings could reach 3 times that of residential buildings. Energy efficiency improvement (EEI) is considered to be one of the most rapid and cost-effective means for the building sector to achieve carbon neutrality. The Chinese government has carried out a series of policy tool exploration to promote the energy efficiency improvement efforts by public building owners since over a decade ago, but encountered various problems and obstacles. This paper investigated and compared the current status challenges, and problems in the implementation of EEI policy tools in China and developed countries through literature review, field investigations and interviews with experts and stakeholders, and finally also put forward some possible coping strategies for Chinese government. Results indicated that energy grading and labeling, energy benchmarking, carbon-emission trading and energy-consumption quota management were the main 4 EEI policy tools designed based on measured energy consumption data, where the first 3 ones had been widely used over the developed countries but the last one was unique to China. Difficulty in large-scale collection of building energy-consumption data, lack of scientific methods on building energy-use level evaluation, limited data application and feedback, undesirable association between stakeholders, etc., were the main obstacles for achieving the long-term effectiveness of the EEI policy tools. Cooperating with energy supply enterprises to collect data, establishing an effective energy grading system and a closed loop of policy implementation, exploring a new model of building carbon-emission trading and a differential pricing mechanism, combined with innovative financial supports, might be the solutions. The main contribution of this study is that a comprehensive investigation and analysis on the current development status, obstacles, and possible solutions of the public building EEI policy tools in China was carried out, which is expected to provide a reference for the government of China and even the world. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for optimal charge scheduling and on-board vehicular control of electrified fleets based on synthetic driving cycles. The proposed approach is conducted within a real case-study in Cairo, Egypt, whereto a representative distance-based driving cycle has been synthesized using K-means clustering over a sliding horizon of gathered data-sets. Two multi-objective problems defining optimal charge scheduling and vehicular control have been formulated to achieve minimal energy consumption and operating cost of the fleet. Non-dominant genetic algorithm (NSGA-II) has been implemented to solve the optimization problems jointly considering fluctuating electricity cost of the grid. The comparative evaluation of results reveals an improvement of 19% and 28% in energy consumption and retention of on-board energy accordingly, with less than 2% mitigation of driveability. Moreover, a reduction of 40.8%, 20%, and 21.9% in fleet size, required charging stations, and annual recharging cost respectively has been realized. The main innovation of this work can be put forward as the ability to address the above-mentioned quadrilateral objectives of electrified fleets in a single comprehensive approach, considering synthetic driving cycles and electricity prices to yield a customized-optimal solution. [ABSTRACT FROM AUTHOR]

Smart objects in the Internet of Things (IoT) communicate with one another, gather information, and respond to users requests. In these systems, wireless sensors are used to collect data and monitor the environment at the lowest level. In IoT applications, wireless sensor networks play a pivotal role. Since IoT devices often use batteries, efficiency is important to them such that IoT-related standards and research efforts focus more on energy saving or conservation. In this paper, we have used two meta-heuristics algorithm for clustering and routing in IoT. We cluster the network using a clustering method called WOA-clustering based on the meta-heuristic Whale Optimization Algorithm (WOA) and select the optimal cluster heads. We then use a routing method called HHO-Routing based on the Harris Hawks Optimization (HHO) algorithm, a novel meta-heuristic algorithm, to route the cluster heads to BS. The use of the above methods results in reduced power consumption for reaching the base station (BS). Also, to prove the optimal performance of the proposed methods, these methods were simulated and compared with five different methods in a similar context. It was observed that the consumed energy, the number of survival cycles for the death of the first node, and the data transmission rate were improved. The proposed method is simulated in cooja simulator, and for a more accurate evaluation, we compare it with UCCGRA, PSO-SD, PUDCRP, EECRA, EEMRP algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan. [ABSTRACT FROM AUTHOR]

In buildings, air conditioning and mechanical ventilation (ACMV) systems are the major shareholders of overall energy consumption. Energy-efficient designs for ACMV systems in building applications are therefore needed. While designing an efficient ACMV system, consideration must be given to the growing concerns of enhanced thermal comfort and improved indoor air quality. The variable refrigerant flow (VRF) air-conditioning system is a widely adopted alternative to the existing building cooling systems due to the higher energy efficiency and individualized temperature control feature. However, it still suffers from shortcomings such as no outdoor air induction for ventilation and higher initial cost. Therefore, this paper reviewed the variable refrigerant flow and mechanical ventilation/air distribution systems, their integrated designs for non-residential buildings, performance evaluation and control optimization of the integrated systems, VRF systems' faults detection and diagnosis, current application of the VRF systems, and associated challenges. Together with these all, some advanced buildings' cooling techniques and improvements toward nearly/net-zero energy buildings are briefly discussed. Indoor thermal comfort models and criteria for different climates are also presented for an in-depth understanding of the VRF integrated mechanical ventilation designs. The literature survey shows that the supply air temperature and airflow rate are foremost in parameters that can be optimized in VRF integrated ventilation design as they greatly reduce the energy consumption. Further, policies on elevated indoor temperatures in air-conditioned buildings to mitigate their carbon footprint are strictly being implemented. Therefore, this review provides an insight to the researchers for further improvement in the integrated design and control optimization of the parameters involved. A paradigm shifts from the conventional compression-based electric-powered air conditioning systems to the renewable energy driven advanced air conditioning technologies which is also an emerging research area to be focused on achieving the target of nearly/net-zero energy buildings. [ABSTRACT FROM AUTHOR]

The emergence of quantum computers is having an impact on almost every field of engineering. Electrical drives, in particular, can benefit from the power of quantum computing. This paper presents a quantum version of the pulse-width modulation (PWM) algorithm, which is used widely to control electrical motors. The proposed algorithm is implemented using a real number comparator, which is novel to the authors' knowledge. Unlike other quantum comparators in the literature, which provide quantum versions of binary-based comparators, the suggested comparator compares real numbers ranging from 0 to 100%, making it more usable in practice for many industrial applications, particularly control. When applied to DC motor speed control, the quantum PWM controller achieved similar speed precision to the classical version, but with lower energy consumption and less switching. It could therefore significantly increase the lifetime of power devices while achieving similar control performance at lower energy consumption. [ABSTRACT FROM AUTHOR]

Satellite constellation deployment is a cohesive mission where the trajectories of satellites must be planned concurrently. This paper presents an Integrated Program for Optimal Deployment of a Satellite Constellation (PODSC) consisting of m non-identical satellites in any desired arrangement in n orbital planes. The PODSC can optimize the scheduling of mission timelines, ensuring effective coordination with the trajectory of each satellite. This involves meticulous planning that considers temporal constraints and regards collision avoidance constraint. Additionally, the PODSC can select the most favorable deployment strategy, considering the trade-offs between time and fuel consumption across all possible deployment methods. The PODSC also utilizes an innovative Perturbed Multi-impulsive Inclined transfer trajectory Amalgamated with a modified Lambert targeting problem (PMIAL). The main idea of designing the mentioned maneuver is to eliminate the defects of the Lambert Targeting Problem (LTP). The LTP cannot account for space perturbations. Moreover, the LTP faces challenges when attempting to align the transfer trajectory tangentially with the final orbit in situations where there exists a substantial disparity in inclination and right ascension between the initial and final orbits. The PMIAL establishes three consecutive steps to fix the mentioned defects. Balancing the trade-off between time and achieving optimal fuel consumption will be possible by applying a hybrid IWO/PSO (The hybrid Invasive Weed Optimization/Particle Swarm Optimization) optimization algorithm in both PMIAL and PODSC. The case study will involve simulating two constellation deployment missions, with a particular focus on considering the Earth's oblateness as a notable perturbation; however, the proposed algorithms can consider any space perturbations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the viscoelastic wave equation with variable coefficients and a delay, which is subject to nonlinear and nonlocal boundary dissipation. The existence of strong and weak solutions is obtained by means of Faedo-Galerkin approximation and denseness argument. By introducing an equivalent energy functional and using the Riemannian geometry method, we show the general decay rate of energy when kernel function satisfies some sufficient small conditions that we provide the range accurately. [ABSTRACT FROM AUTHOR]

In the world of connected devices with enormous application market, wireless sensor network comes with inherent challenges. Energy consumption balancing among the sensors affects its lifetime and connectivity. A hybrid approach for the randomly deployed sensor network is proposed in this paper with better consumption energy balance through the lifetime of the sensor network. An optimized threshold probability is analyzed for the improvement of network lifetime in comparison with stable election protocol. Simulation results provide improved consumption energy distribution and prolonged network lifetime of heterogeneous wireless sensor network. The proposed protocol is reactive to the change in the network, making it suitable for the Internet of Things applications. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a consistent increase in the number of Internet of Things (IoT) devices utilizing Cloud services. However, this growth has brought about new challenges, particularly in terms of latency. To tackle this issue, fog computing has emerged as a promising trend. By incorporating additional resources at the edge of the Cloud architecture, the fog–cloud architecture aims to reduce latency by bringing processing closer to end-users. This trend has significant implications for enhancing the overall performance and user experience of IoT systems. One major challenge in achieving this is minimizing latency without increasing total energy consumption. To address this challenge, it is crucial to employ a powerful scheduling solution. Unfortunately, this scheduling problem is generally known as NP-hard, implying that no optimal solution that can be obtained in a reasonable time has been discovered to date. In this paper, we focus on the problem of task scheduling in a fog–cloud based environment. Therefore, we propose a novel genetic-based algorithm called GAMMR that aims to achieve an optimal balance between total consumed energy and total response time. We evaluate the proposed algorithm using simulations on 8 datasets of varying sizes. The results demonstrate that our proposed GAMMR algorithm outperforms the standard genetic algorithm in all tested cases, with an average improvement of 3.4% in the normalized function. [ABSTRACT FROM AUTHOR]

In pursuit of the "Dual Carbon Goals" and to mitigate the adverse effects of "power supply restrictions," a microgrid scheme integrating wind and solar power with hydrogen energy storage is proposed. This paper introduces the principles of system capacity configuration and establishes a mathematical model. This research offers a novel method for configuring wind and solar hydrogen storage systems called quantum-enhanced multi-objective collaboration. This work intends to address the complicated issues of achieving effective energy storage, reducing prices, and maximising renewable energy utilisation by leveraging the capabilities of quantum computing. A multi-objective capacity optimization configuration model for wind–solar–hydrogen energy storage is developed using Homer Pro software and an enhanced BAS-GA algorithm. Under off-grid operating conditions, the wind–solar–hydrogen energy storage system's capacity optimization configuration model is validated through practical examples. The results indicate that, in comparison to wind storage, solar storage, wind–solar storage, and wind–solar–diesel storage systems, the net present value and levelized cost of electricity of the wind–solar–hydrogen energy storage system decrease to 14.25 million RMB and 1.529 RMB/(kW h), respectively. The renewable energy utilization rate increases to 98.7%, and the load shedding rate decreases to 5.50%. [ABSTRACT FROM AUTHOR]

This paper proposes a refrigerant cooling method using an inner tube in a storage tank to improve the cooling performance and thermal uniformity during the storing of edible oil. With a prototype of an oil tank in Central Grain Reserve of Zhenjiang, the experimental oil tank was built in a scale of 50:1. Both natural and manual cooling experiments were carried out for the experimental tank. The manual cooling process involved two supplying modes for the refrigerant tube (top and bottom) and four different refrigerant temperatures (10 ℃, 12 ℃, 14 ℃, 16 ℃). The experimental results show that, compared with natural cooling, manual cooling can effectively reduce the temperature difference and thermal stratification between upper and lower layers. The temperature difference is 6.79 ℃, 1.93 ℃, and 3.67 ℃ for the natural cooling, manual top supplying, and manual bottom supplying mode, respectively. Furthermore, for the two manual modes, the cooling efficiency of bottom supplying is 21.4% higher than that of the top supplying, and the average oil temperature drops by 0.8–1 ℃. Based on experimental results, different working conditions (20, 40, and 60 ml/s) were simulated to determine the optimal flow rate for bottom supplying mode. The simulation results indicate that the low flow rate (20 ml/s) corresponds to the best thermal uniformity, and the maximum temperature has no obvious change under different flow rate conditions. Therefore, it is not necessary to increase the flow rate to improve cooling efficiency considering the rising energy consumption. [ABSTRACT FROM AUTHOR]

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error. [ABSTRACT FROM AUTHOR]

The awareness of energy consumption is gaining much more attention in manufacturing due to its economic and sustainability benefits. Reducing energy consumption in advanced manufacturing processes, like directed energy deposition (DED) manufacturing, is critical for Industry 4.0 and the forthcoming Industry 5.0 since it allows for more economical, sustainable, and environmentally friendly manufacturing approaches. An energy consumption model is needed for quantifying the consumption and predicting the impact of various process parameters in manufacturing. This paper aims to develop an energy consumption model for DED-based hybrid additive manufacturing (HAM) for an Inconel 718 part. The specific energy consumption (SEC) is used while developing the energy consumption of the product manufacturing lifecycle. This study focuses on the analysis to investigate three significant factors (scanning speed, laser power, and feed rate), their interactions' effects, and whether they have a significant effect on energy consumption. The results suggest that all the factors have a strong influence, but their interaction effects have a weak influence on the energy consumption for HAM. Among the three process parameters, it is found that laser power has the most significant effect on energy consumption. Again, based on the regression analysis, this study also recommends high scanning speed while the laser power and feed rate should be low. Also, idle time has significant energy consumption during the whole HAM process. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Designing a cost-effective, energy-efficient and highly scalable network for data centers that can deliver sufficient bandwidth has drawn tremendous attentions recently. The data center networks constructed by using multi-port servers can provide sufficient bandwidth, such as BCube, DCell and GBC3. As the volume of data keeps growing rapidly, more and more servers are continuously added into data centers. In order to reduce the cost and energy consumption, data center networks can be expanded gradually by adding a small number of servers from time to time instead of adding a huge number of servers at a time. This paper proposes a new type of data center network structure called GHB, which is constructed by using commercial switches and multi-port servers. Two types of incomplete GHB structures are also proposed. A small number of servers can be gradually added into the incomplete structures without changing their topological properties. As shown in the experimental results, the throughput of GHB is comparable to that of BCube, and is larger than that of GBC3 and DCell. The analysis results indicate that GHB strikes a good balance among diameter, bisection width, incremental scalability, cost, and energy consumption in contrast to BCube, DCell and GBC3. Compared with the BCube and GBC3, GHB reduces the cost and energy consumption by about 6% and 18%, respectively. The highest throughput of GHB is higher than that of DCell and GBC3 by about 3.4% and 8.35%, respectively. [ABSTRACT FROM AUTHOR]

Since the 1970s, Brazil has carried out the most successful world program of commercial biomass for use and production of energy by stimulating its sugarcane industry and promoting the large-scale production of ethanol nationwide in response to the first oil shock. Today, the technologies behind ethanol production are well established. Brazil is the world's largest sugarcane supplier, producing its ethanol at a competitive price. If other sugarcane producing countries decided to join Brazil's move toward the production of this biofuel, what impacts might there be for each country's economy and employment? This is what we investigate in this paper. Prime candidates for ethanol production include Australia, China, Colombia, India, Indonesia, Mexico, Pakistan, the Philippines, Thailand, and the United States of America. We evaluated the potential socioeconomic impacts of developing this promising industry by using an input–output approach. More specifically, we adapted the Brazilian method of producing ethanol to these countries' distinct economies. We augmented the input–output table of each country, inserting a new ethanol industry based on the Brazilian ethanol production model. We also augmented their new ethanol industry's sales following a hypothetical hydrous and anhydrous ethanol consumption scenario. Thereafter, we reconcile the national accounts, concluding our analysis by quantifying and comparing the different net effects of this new industry for each of the assessed countries for the year of 2009. We demonstrate which industries would be expected to be positively or negatively impacted by this substitution in each country; and find that not all of the countries we assessed would experience positive socioeconomic results from emulating Brazil's production of ethanol. [ABSTRACT FROM AUTHOR]

Combine harvesters (CHs) use large amounts of diesel fuel and they contribute significantly to greenhouse gas (GHG) emissions and air pollutants. A reduction in fuel consumption of agricultural machines has immediate economic benefits but can also results in longer term environmental benefits. Savickas et al. (Precis Agric 2023. https://doi.org/10.1007/s11119-023-10060-6) introduced extensive telematics data analysis and field tests for identifying the potential sustainable use of CHs. This paper follows on from that research and discusses the need for an IT tool for the management of economic and environmental indicators of a CH. The innovative tool allows the comparison of the data of specific agricultural machines with the multi-year telematics data stored in the database. It offers opportunities to reduce environmental impact and evaluate the results of field trials, presenting them in terms of environmental impact, expressed as CO2 equivalents. [ABSTRACT FROM AUTHOR]

Intelligent load scheduling for the smart city is essential due to the growth of industrialization, which directly consumes energy from the grid. The energy management system in industrialization is enhanced using the Internet of things in the same domain. The general need in a smart city is to maintain the energy utilization and cost that the industrial Internet of things could achieve for industry application. Researchers have also found a solution to this problem, but every time the generator cannot sell energy, the industry purchases the same. There is a chance for the industry to supply its energy to other industries or the grid. By keeping this constraint, an intelligent approach to load scheduling based on optimized Q-Net is proposed in this paper, comprising the supplies as renewable energy, battery, and industrial energy. Also, the reactive power flow of smart cities during scheduling is mitigated by classifying the energy available in the bus network. The proposed method scheduled demand power with 99% accuracy with the lowest cost of 13.7041$/KW for peak hr. scheduling with a demand of 6000KW/hr. [ABSTRACT FROM AUTHOR]

Cooperative Non-Orthogonal Multiple Access (Cooperative NOMA) is a communication technique used in wireless networks to enhance spectral efficiency and overall system performance. Non-Orthogonal Multiple Access is a multiple access scheme that allows multiple users to share the same time-frequency resources by allocating different power levels to each user. This paper presents a comprehensive performance validation of NOMA empowered by optimal relay selection and deep learning-based power allocation techniques. To achieve effective data transmission to far users, the optimal relay selection process employs the Maximum Energy Harvested Relay (MEHR) selection approach, which intelligently selects relays based on their energy harvesting capabilities. Furthermore, an enhanced Deep Convolutional Optimized Neural Network (DCOnN) is utilized for optimal power allocation among users in a cooperative NOMA system. To efficiently optimize the power allocation process with the DCOnN model, the Grey Wolf (GW) optimization is utilized to fine-tune the network weights. The presented approach significantly increases the energy efficiency in cooperative NOMA networks. The combination of optimal relay selection and deep learning-based power allocation enhances outage probability performance, ensuring better coverage and communication reliability while reducing the bit error rate for users in challenging channel conditions. Finally, the integration of optimal relay selection using DCOnN with GW optimization and cooperative cognitive NOMA provides a powerful framework to achieve superior communication performance in wireless networks. The proposed scheme achieves higher performance in terms of energy efficiency (519.257 Bits/J/Hz) and CPU time (0.012 seconds). The experimental results proved that the proposed performance is enhanced than the different existing schemes. [ABSTRACT FROM AUTHOR]

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach's robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems. [ABSTRACT FROM AUTHOR]

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS (The Code of TConNS is available at https://github.com/zhengLabs/NSO) . Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures. [ABSTRACT FROM AUTHOR]

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals' intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China's national conditions. It validates carbon-related enterprise data and employs data mining's association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement. [ABSTRACT FROM AUTHOR]

In the light of the adverse effects of climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict the energy demand and generation trade-offs. This paper focuses on developing and comparing predictive algorithms based on historical data from a near Zero Energy Building. This involves energy load, as well as temperature data, which are used to develop and evaluate various base ML algorithms and methodologies, including Artificial Neural Networks and Decision-trees, as well as their combination. Each algorithm is fine-tuned and tested, accounting for the unique data characteristics, such as the presence of photovoltaics, in order to produce a robust approach for One-Step-Ahead ELF. To this end, a novel hybrid model utilizing ensemble methods was developed. It combines multiple base ML algorithms the outputs of which are utilized to train a meta-model voting regressor. This hybrid model acts as a normalizer for any new data input. An experimental comparison of the model against unseen data and other ensemble approaches, showed promising forecasting results (mean absolute percentage error = 5.39%), particularly compared to the base algorithms. [ABSTRACT FROM AUTHOR]

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents a low-power multi-rate clock and data recovery (CDR) for receivers of serial links. Its basic structure includes a current-mode logic bang–bang phase detector sampled by low-mismatch half-rate quadrature clocks, which are generated by voltage-controlled delay line (VCDL) and two-stage time-average circuits. The total delay of VCDL can be adjusted to accommodate a wide frequency range by its bias voltage, which is generated by a delay-locked-loop-based bias generator. The quadrature clocks are 64-phase adjustable with high linearity, which is realized by phase interpolator with a compensating structure. The parameters of phase detection loop are well designed to satisfy both high jitter tolerance and low clock jitter. Fabricated in a 40 nm CMOS technology, the CDR occupies an active area of 0.036 mm 2 only. With a wide operating range of 2–20 Gb/s, the chip consumes 62.5 mW, corresponding to an energy efficiency of 3.1 pJ/bit. The measured root-mean-square jitter and peak-to-peak jitter for the recovered clock at 9 GHz are 1.9 and 10.8 ps, respectively. [ABSTRACT FROM AUTHOR]

Domestic hot water production is the second most important energy use in the European residential sector, nowadays accounting for 14% of the sector's total final energy consumption. Despite its importance, the energy efficiency improvement rates for domestic hot water are lower than for other residential energy services, hence calling for energy-saving measures. One key measure is to install flow restrictors. Their advantages are the low upfront cost, easy installation, and suitability for integration into energy efficiency programs. Focusing on flow restrictors, this paper presents different methods for quantifying the energy savings using ex-ante and ex-post approaches: deemed savings (DES), dedicated measurements (DMs), and monthly and yearly billing analysis (SMBA and ABA). These methods were tested using information based on measurements (water flow, temperatures), historical billing analysis, a survey among inhabitants, and interviews with field experts. While measurements made at individual faucets or showerheads show significant water savings (20% and 33% respectively), energy savings associated with hot water production in the boiler (final energy) are significantly lower (around 10%) but far from being negligible. The main reasons for the difference are thermal losses related to hot water distribution in central heating systems, usages not affected by flow restrictors, and inhabitants removing them. We conclude that flow restrictors offer promising potential for short- to medium-term implementation. Given the simplicity of this solution, we recommend including it systematically in energy efficiency programs, as well as implementing a ban on fixtures with flow rates beyond a predefined level. [ABSTRACT FROM AUTHOR]

With the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme. [ABSTRACT FROM AUTHOR]

Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes' scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios. [ABSTRACT FROM AUTHOR]

This study addresses the energy efficiency challenge in cloud data centers by examining the Virtual Machine Placement (VMP) problem. VMP involves mapping virtual machines (VMs) to physical machines (PMs) under capacity constraints. The paper focuses on the bin packing with linear usage cost (BPLUC) variant of bin packing, which includes fixed and variable costs in the calculation of the cost of a used bin. We prove that every approximation algorithm for the bin and vector bin packing can be used for BPLUC and VBPLUC, respectively. We propose a more power-efficient approach to VMP by applying a vector bin packing algorithm to minimize power consumption in data centers. We test the proposed algorithm on various synthetic and real workloads, and the experimental results demonstrate that it is more power-efficient than existing algorithms for VMP. The findings suggest that the proposed algorithm has significant implications for energy-efficient strategies in cloud data centers. Generally, this study makes contributes to the development of energy-efficient approaches to VMP that can help reduce power consumption and improve the sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) are a low-cost solution for healthcare applications. However, in these networks, a huge amount of transmission is required to record the patient's status, leading to reduce energy rapidly. To solve this problem, adjusting the sampling rate based on the patient's condition is among efficient methods to save energy. However, most sampling rate methods have two drawbacks: 1-They do not estimate adaptive sampling rate for multiple sensors based on the patient's condition simultaneously. 2-They are not high-level data fusion methods. To solve these problems, a fuzzy data fusion method called fuzzy adaptive sampling rate with local emergency detection (FASR-LED) is proposed in this paper. FASR-LED has two stages: (1) At the node level, the local emergency detection system deletes redundant data that considers patient conditions. (2) At the coordinator level, a fuzzy adaptive sampling rate is proposed as an inference engine to reduce energy consumption. The coordinator level by a fuzzy inference engine gives vital signs of the sensors as inputs and estimates the sampling rate as output. The main advantages of FASR-LED are: (1) Data fusion is done by a fuzzy high-level method to estimate the sampling rate. Therefore, data validity is increased by a fuzzy approach. (2) Medical diagnoses based on analyzing the vital signs are made faster due to the same sampling rate of all sensors. Since WBAN's sensors with different sampling rates need more complex synchronization methods, FASR-LED does not require synchronizing the sensors. (3) FASR-LED reduces the energy consumption of WBAN by creating high-level data. Simulations are performed on MIMIC I and MIMIC II datasets to evaluate the proposed method. Results show that FASR-LED, compared to other methods, overcomes others in terms of energy consumption by 50% and data reduction by 30%. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

Energy consumption reduces Wireless Sensor Network's (WSN's) lifetime. Hence, this paper addresses energy saving problematic in cooperative Orthogonal Frequency Division Multiplexing Access (OFDMA) systems for WSNs (COFDMA-WSN). Analytical handlings are implemented. The performance improvement of COFDMA-WSNs is executed by considering two different COFDMA-WSNs schemes. These schemes represent classical COFDMA-WSNs and Relay Supported (R-S) COFDMA-WSNs. Additionally, there are different pondered configurations due to these schemes. These configurations denote classical OFDMA network with classical WSN, R-S OFDMA network with classical WSN, classical OFDMA network with R-S WSN, and R-S OFDMA network with R-S WSN. Moreover, each configuration is applied with four different fractional frequency reuse (FrFR) techniques. These techniques represent strict (St) FrFR technique with Frequency Reuse Factor (FReF) = 3, St FrFR technique with FReF = 4, sectored (Sc) FrFR technique and soft frequency reuse (SoFR) technique. Consequently, there are sixteen different patterns of COFDMA-WSNs are considered. Moreover, closed-form terms (CFTs) for cluster-head's (C-H's) signal to interference ratio (SIR) and sensor node's (SN's) SIR are presented. Additionally, different metrics are evaluated to contrast the performance of altered patterns using the obtained CFTs. The outcomes demonstrate, that St FrRF4 system outperforms other systems in prime and ensuing links. The cause of this outcome can be credited, to the frequency reuse process decrement due to FReF increment. As a result, the interference sources decrease. Hence, the interfering signals drop. Consequently, St FrRF4 system achieves the highest values of SIR. Moreover, St FrRF3 technique has the second usage priority in the prime link. But, it losses this preference in the ensuing link and SoFR technique that applied in the fourth configuration takes this significance. The work outcomes attain much higher C-H's and SN's SIR improvements. Accordingly, the packet transmission and protocol behaviour are enhanced. So, the energy consumption is reduced. Consequently, WSN's lifetime is maximized. [ABSTRACT FROM AUTHOR]