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FOG and Cloud computing has established itself as an important component of the computational domain, providing a wide range of server proficiencies as virtualized scalable services. Cloud datacenters utilize VM consolidation to consolidate VMs to a smaller number of physical servers in order to enhance resource utilization and energy efficiency. Incorrect VM placement, on the other hand, might result in frequent VM migrations and continual on-off switching on physical machines (PMs), lowering service quality and increasing energy usage. To address this issue, we present a VM consolidation strategy based on Sailfish Optimization that is both effective and efficient. The performance evaluation of the proposed method observed a significant reduction in power consumption, SLA violation and execution time up to 25%, 22% and 6%, respectively, and increased resource utilization up to 17% as compared to existing models.

Traditional sensors require large power inputs and accuracy cannot be guaranteed. In this study, we introduce a revolutionary technique to enhance the accuracy of node measurements in Wi-Fi sensor networks (WSNs) while minimizing power consumption. Our approach focuses on a mixture of key attributes, including overall strength performance, landmark strength, and node geolocation. We have set quantitative measures for these attributes, which is the foundation of our sizing method. The basic framework for evaluating node allocation indicators is superior, mainly based on the multifaceted attributes of individual nodes and adjacent nodes, promoting selection in data transmission and processing. This framework has a powerful statistical collection mechanism that can accumulate every intrinsic attribute, including external attributes such as overall power performance, signal energy, and geographic region. Using these collected records, our techniques include selecting methods for metric calculations. It is worth noting that we have merged a set of adaptive strategies that can dynamically adjust measurement parameters based on discovered community conditions. The adaptability of this strategy ensures normal operational performance in various community states, achieving a balance between measurement accuracy and energy conservation. This method ensures a significant improvement in the operational performance of wireless sensor networks and has broad applicability in the field of reliable high-intensity sensor networks.

Today, heavy machinery can be equipped with robots operated by artificial intelligence (AI) to streamline operations, decrease human labor, and increase efficiency. Intelligent technologies like these can boost productivity by carrying out routine tasks with extreme accuracy. Since robots reduce risks to human workers, robots with AI algorithms and sensors could be used in hazardous environments. In certain contexts, including building site management, mining, or handling hazardous materials, safety must take precedence above everything else. Analytics from this study, aided by AI, have opened the door to predictive maintenance procedures. To detect equipment problems, the Hybrid Artificial Intelligence Framework (HAIF) examines sensor data in conjunction with historical patterns. The objective is to avoid expensive failures and downtime. Optimization of equipment utilization, energy consumption, and fuel consumption can be achieved by applying AI-driven machine learning algorithms. Several advantages arise from energy waste, including cost avoidance and enhanced operational efficacy. Robots can do exact alignments, measurements, and operations with the help of AI vision technologies. This precision significantly impacts the manufacturing, agricultural, and construction industries. Artificial intelligence has the potential to optimize the allocation of resources, the use of heavy equipment, and supply networks by evaluating massive amounts of data.

As a new way of promoting high-quality economic development, whether the digital economy can enhance energy use efficiency by improving resource allocation is of great practical significance to global energy conservation and emission reduction efforts. This research employs principal component analysis and slacks-based measure data envelopment analysis in a case study of 30 provinces in China between 2014 and 2020 to evaluate the digital economy and energy efficiency. The analysis explores how the digital economy impacts energy efficiency, considering direct impacts, mediating effects, and their nonlinear relationship. The findings indicate a declining trend in China’s energy efficiency before it starts to moderate. The digital economy can boost energy efficiency by encouraging industry upgrades and reducing labor mismatches. The threshold regression model reveals that when the capital mismatch level exceeds 0.8871, the digital economy adds considerably to energy efficiency. The study’s findings confirm the linear and nonlinear energy efficiency impact trajectories of the digital economy, offering useful references for sustainable development policymaking.

In this paper, we leverage a reinforcement learning approach to address the motion control problem of Four In-Wheel Motor Actuated Vehicles aimed at achieving precise control while optimizing energy efficiency. Our control architecture consists of four adaptive Proportional-Integral-Derivative controllers, each assigned to an independent vehicle wheel. We train these controllers using an actor-critic framework in two standard driving scenarios: acceleration and braking, as well as a double lane-change maneuver. This method eliminates the need for a detailed mathematical model of the complex vehicle dynamics. Moreover, the adaptive mechanism enables controllers to dynamically adapt to varying operating conditions. After training, the resulting controllers are tested in unseen scenarios to validate their robustness and adaptability beyond the training environment. The testing results show that our controllers achieve precise velocity and trajectory tracking while maintaining low energy consumption.

In this paper, we study the problem of maintaining sensing coverage by keeping a small number of active sensor nodes and using a small amount of energy consumption in wireless sensor networks. This paper extends a result from ²² where only uniform sensing range among all sensors is used. We adopt an approach that allows non-uniform sensing ranges for different sensors. As opposed to the uniform sensing range node scheduling model in ²², two new energy-efficient models with different sensing ranges are proposed. Our objective is to minimize the overlapped sensing area of sensor nodes, thus to reduce the overall energy consumption by sensing and communication to prolong the whole network's life time, and at the same time to achieve the high ratio of coverage. Extensive simulation is conducted to verify the effectiveness of our node scheduling models.

This paper presents the design of an underwater energy harvesting system, which would provide persistent and sustainable power supply for remote underwater sensing and surveillance devices. The system consists of Distributed Benthic Microbial Fuel Cell (DBMFC) and the associated power management integrated circuit. The DBMFC exploits bacterial metabolic activities associated with the redox reaction to generate electrical energy directly from biodegradable substrates. The power management circuit collects the energy harvested by the DBMFC and boosts the output voltage to a sufficient and stable level for loads such as sensor devices. Simulation results of the power management system in a 90nm CMOS process demonstrate the expected functions and the significant improvement in energy conversion efficiency.

While an extensive set of research projects deal with the issue of power-saving for battery-based electronic devices, few have an interest in permanently-plugged Large-Scale Experimental Distributed Systems (LSEDS). However, a rapid study shows that each computer, member of a distributed system platform, consumes a substantial quantity of power, especially when those resources are idle. Today, given the number of processing resources involved in large-scale computing infrastructures, we are convinced that we can save a lot of electric power by proposing and applying "green policies". Introduced in this article, those policies propose to alternatively switch computer nodes On and Off in a clever way. Based on the analysis of some experimental large-scale system's usage, we propose a resource-reservation infrastructure which takes the energy issue into account. We validate our infrastructure on the large-scale experimental Grid'5000^a platform and present the energy gains obtained.

At the age of petascale machines, cloud computing and peer-to-peer systems, large-scale distributed systems need an ever-increasing amount of energy. These systems urgently require effective and scalable solutions to manage and limit their electrical consumption. As of now, most efforts are focused on energy-efficient hardware designs. Thus, the challenge is to coordinate all these low-level improvements at the middleware level to improve the energy efficiency of the overall systems. Resource-management solutions can indeed benefit from a broader view to pool the resources and to share them according to the needs of each user. In this paper, we propose ERIDIS, an Energy-efficient Reservation Infrastructure for large-scale DIstributed Systems. It provides a unified and generic framework to manage resources from Grids, Clouds and dedicated networks in an energy-efficient way.

Accelerators offer a substantial increase in efficiency for high-performance systems offering speedups for computational applications that leverage hardware support for highly-parallel codes. However, the power use of some accelerators exceeds 200 watts at idle which means use at exascale comes at a significant increase in power at a time when we face a power ceiling of about 20 megawatts. Despite the growing domination of accelerator-based systems in the Top500 and Green500 lists of fastest and most efficient supercomputers, there are few detailed studies comparing the power and energy use of common accelerators. In this work, we conduct detailed experimental studies of the power usage and distribution of Xeon-Phi-based systems in comparison to the NVIDIA Tesla and an Intel Sandy Bridge multicore host processor. In contrast to previous work, we focus on separating individual component power and correlating power use to code behavior. Our results help explain the causes of power-performance scalability for a set of HPC applications.

It is widely accepted that a market-based instrument such as a tax on greenhouse gas emissions is effective in motivating firms to improve energy efficiency, environmental management and invest in environmentally-related research and development (R&D). However, modern corporations tend to separate ownership and management, and decision-making executives who are myopic may not share the firm owners’ concern about their firm’s exposure to long-run costs and risks associated with climate change. Hence, executive wage contracts should include rewards for environmental performance, particularly in energy efficiency and developing R&D to reduce emissions. This paper examines the effect of implementing executive compensation that is partially indexed to abatement in a monopolist firm, in which decision-making is delegated to a manager under an emissions tax policy. In equilibrium, it is shown that the new wage compensation leads to more abatement, greater production of output, and higher wages for the manager compared with a conventional wage scheme where wages are solely indexed to profits. Hence, the government imposes a lower emissions tax on the firm. More importantly, this public–private joint mechanism results in net social welfare improvement in equilibrium. However, whether the monopolist’s profit is higher in the new wage scheme depends non-monotonically on the abatement efficiency technology and the extent of wage indexation to profitability.

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

In living cells, molecular motors convert chemical energy into mechanical work. Its thermodynamic energy efficiency, i.e. the ratio of output mechanical work to input chemical energy, is usually high. However, using two-state models, we found the motion of molecular motors is loosely coupled to the chemical cycle. Only part of the input energy can be converted into mechanical work. Others are dissipated into environment during substeps without contributions to the unidirectional movement.

Energy consumption of numerical control (NC) workshop has lots of characteristics, such as hierarchy, multi-sources and time-varying. These characteristics make the modeling and evaluation of energy consumption in NC workshop very difficult. In this paper, a novel hierarchical model of the energy consumption in NC workshop is presented. Then, the calculation methods of energy efficiency in each layer are given. Furthermore, the acquisition method of the energy consumption data which is easily implemented is put forward and an experiment in NC workshop was made to illustrate the effectiveness of the proposed energy consumption model. The experimental results showed that the model cannot only describe the energy consumption effectively but also provide a way to identify the bottleneck of energy consumption in the workshop.

The difficulty in the energy efficiency analysis of discrete manufacturing system is the lack of evaluation index system. In this paper, a novel evaluation index system with three layers and 10 indexes was presented to analyze the overall energy consumption level of the discrete manufacturing system. Then, with the consideration of the difficulties in directly obtaining machine energy efficiency, a prediction method based on recursive variable forgetting factor identification was put forward to calculate it. Furthermore, a comprehensive quantitative evaluation method of rough set and attribute hierarchical model was designed based on the index structure to evaluate the energy efficiency level. Finally, an experiment was used to illustrate the effectiveness of our evaluation index system and method.

In wireless sensor network (WSN), most of the devices function on batteries. These nodes or devices have inadequate amount of initial energy which are consumed at diverse rates, based on the power level and intended receiver. In sleep scheduling algorithms, most of the sensor nodes are turned to sleep state to preserve energy and improve the network lifetime (NL). In this paper, an energy-efficient dynamic cluster-based protocol is proposed for WSN especially for physics-based applications. Initially, the network is divided into small clusters using adaptive clustering. The clusters are managed by the cluster heads. The cluster heads are elected based on the novel dynamic threshold. Afterwards, general variable neighborhood search is used to obtain the energy-efficient paths for inter-cluster data aggregation which is used to communicate with the sink. The performance of the proposed method is compared with competitive energy-efficient routing protocols in terms of various factors such as stable period, NL, packets sent to base station and packets sent to cluster head. Extensive experiments prove that the proposed protocol provides higher NL than the existing protocols.

In this paper, a hybrid soft computing technique-based energy efficient protocol is proposed to improve the inter-cluster data aggregation in clustering based general self-organized tree based energy balance (GSTEB) routing protocol. Initially, improved ant colony optimization-based technique is used to select optimal cluster heads. Afterwards, a hybrid soft computing technique is utilized to communicate the data from cluster heads to sink. Extensive experiments have been done by considering the existing and proposed technique. Experimental results indicate that the proposed technique provides better network lifetime as compared to existing techniques.

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.

China accounts for more than 22% of the total energy consumption worldwide. Building energy consumption, among which consumption in public buildings was about 40% took the second place. With the problems of high energy waste, error rate and complexity of the control systems available, an indoor intelligent lighting system based on occupants’ location is proposed in this paper to improve the energy efficiency of the current lighting systems indoors. The transmission model of electromagnetic wave in free space is optimized in both aspects of reference signal strength and attenuation coefficient radiation in complex environment dynamically based on which occupants’ positions are obtained. The smart lighting system will turn on or off corresponding lights adaptively to provide a more energy efficient platform. Experimental results show that the proposed system is able to improve the energy efficiency of indoor lighting by at least 15%, with a lower error rate below 2% compared with the existing lighting systems based on voice control.

Performance-enhancement techniques improve CPU speed at the cost of other valuable system resources such as power and energy. Software prefetching is one such technique, tolerating memory latency for high performance. In this article, we quantitatively study this technique's impact on system performance and power/energy consumption. First, we demonstrate that software prefetching achieves an average of 36% performance improvement with 8% additional energy consumption and 69% higher power consumption on six memory-intensive benchmarks. Then we combine software prefetching with a (unrealistic) static voltage scaling technique to show that this performance gain can be converted to an average of 48% energy saving. This suggests that it is promising to build low power systems with techniques traditionally known for performance enhancement. We thus propose a practical online profiling based dynamic voltage scaling (DVS) algorithm. The algorithm monitors system's performance and adapts the voltage level accordingly to save energy while maintaining the observed system performance. Our proposed online profiling DVS algorithm achieves 38% energy saving without any significant performance loss.