Energy efficient network path reconfiguration for industrial field data

The effect of applying an energy pulse to the heart during ventricular fibrillation is described by the probability of successful defibrillation or success rate. Seven to ten (8.60 +/- 0.84: mean +/- standard deviation) defibrillation trials per energy were randomly attempted at energies which span the defibrillation success rate versus energy curve. We obtained 70.0 +/- 8.4 episodes per dog. We fit the defibrillation success rate versus energy relationship from ten dogs (20.5 +/- 1.5 kg) to four types of curves: linear, exponential, probit transformed linear, and logit transformed linear. The correlation coefficients for each fit are 0.917 +/- 0.057, 0.944 +/- 0.014, 0.926 +/- 0.51, and 0.889 +/- 0.098, respectively. We therefore conclude that the exponential curve best describes the DSRE relationship. This suggests the existence of an energy below which defibrillation does not occur. At higher energies, the exponential curve asymptotically approaches a 100% success rate, which indicates that increasing the energy produces a diminishing benefit to defibrillation success rate. The estimated energies with a 0% defibrillation success rate are surprisingly consistent among dogs, with 2.072 +/- 0.553 J. The estimated energy with an 80% defibrillation success rate is 5.217 +/- 1.091 J. The estimated defibrillation success rate corresponding to the defibrillation threshold of 3.59 +/- 1.06 J is consistent with 0.516 +/- 0.144. The estimated energies with a 0% success rate correlate well with the defibrillation thresholds with R = 0.772; P = 0.0088. Since implantable defibrillators have a limited energy supply, we determined energy efficiency by dividing defibrillation success rate by the applied energy and energy consumption by dividing the applied energy by the defibrillation success rate. The most efficient defibrillation energy occurs at the maximum energy efficiency and the minimum energy consumption. The most efficient defibrillation energy of 4.34 +/- 0.97 J determined from the exponential fit has a success rate of 0.70 +/- 0.06. The most efficient defibrillation energy can be predicted from the defibrillation threshold. Clinically, a 70% success rate may not be adequate. We, therefore, compared the energy efficiency and consumption of energies with 90% and 95% success rates to the most efficient defibrillation energy. About a 50% increase in energy from the most efficient defibrillation energy is necessary for a 90% success rate which results in about a 13% loss in energy efficiency and about a 16% increase in energy consumption. About an 84% energy increase is necessary for a 95% success rate which results in about a 24% loss in energy efficiency and about a 33% increase in energy consumption.(ABSTRACT TRUNCATED AT 400 WORDS)

Wireless Sensor Network consists of a large population of sensor nodes, capable of computation, communication and sensing. Limited energy is the inherent limitation of wireless sensor network; most routing algorithms for sensor network focus only on energy efficient paths. In wireless sensor networks, unreliability of the links and the limited resources create considerable obstacles to routing. Even in the presence of static nodes, the channel conditions may vary because of multipath fading. Due to this, path break may occur more frequently and a considerable amount of energy is consumed to set-up new path. Ideally, routing algorithm should consider not only energy efficient path but also the robustness of path. In this paper, we are introducing two new metric like threshold and energy cost to find link quality and energy efficient path respectively. These two metric gives rise to the design of Distributed Link Quality Aware Routing (DLQAR) for wireless sensor network. DLQAR is designed to generate routing paths in a decentralized manner, while considering the energy efficiency and robustness of paths. Network lifetime, energy consumption and Quality of Service (QoS) parameters are used to analyze effectiveness of proposed algorithm. Comparative analysis of DLQAR with the widely used AODV shows that energy cost along with link quality should be considered to minimize energy consumption and maximize QoS parameters.

Two crucial challenges in Industry 4.0 involve maintaining critical latency requirements for data access and ensuring efficient power consumption by field devices. Traditional centralized industrial networks that provide rudimentary data distribution capabilities may not be able to meet such stringent requirements. These requirements cannot be met later due to connection or node failures or extreme performance decadence. To address this problem, this paper focuses on resource-constrained networks of Internet of Things (IoT) systems, exploiting the presence of several more powerful nodes acting as distributed local data storage proxies for every IoT set. To increase the battery lifetime of the network, a number of nodes that are not included in data transmission or data storage are turned off. In this paper, we investigate the issue of maximizing network lifetime, and consider the restrictions on data access latency. For this purpose, data are cached distributively in proxy nodes, leading to a reduction in energy consumption and ultimately maximizing network lifetime. To address this problem, we introduce an energy-aware data management method (EDMM); with the goal of extending network lifetime, select IoT nodes are designated to save data distributively. Our proposed approach (1) makes sure that data access latency is underneath a specified threshold and (2) performs well with respect to network lifetime compared to an offline centralized heuristic algorithm.

Traditionally, architectural innovations designed to boost single-threaded performance incur overhead costs which significantly increase power consumption. In many cases the increase in power exceeds the improvement in performance, resulting in a net increase in energy consumption. Thus, it is reasonable to assume that modern attempts to improve singlethreaded performance will have a negative impact on energy efficiency. This has led to the belief that "Big Cores" are inherently inefficient. To the contrary, we present a study which finds that the increased complexity of the core microarchitecture in recent generations of the IntelR Core™ processor have reduced both the time and energy required to run various workloads. Moreover, taking out the impact of process technology changes, our study still finds the architecture and microarchitecture changes ---such as the increase in SIMD width, addition of the frontend caches, and the enhancement to the out-of-order execution engine--- account for 1.2x improvement in energy efficiency for these processors. This paper provides real-world examples of how architectural innovations can mitigate inefficiencies associated with "Big Cores" ---for example, micro-op caches obviate the costly decode of complex x86 instructions--- resulting in a core architecture that is both high performance and energy efficient. It also contributes to the understanding of how microarchitecture affects performance, power and energy efficiency by modeling the relationship between them

This study addresses the pressing issue of energy consumption and efficiency in the Kingdom of Saudi Arabia (KSA), a region experiencing growing demand for energy resources. Temperature control plays a vital role in achieving energy efficiency; however, traditional control systems may struggle to adapt to the non-linear and time-varying characteristics of the problem. To tackle this challenge, a fuzzy petri net (FPN) controller is proposed as a more suitable solution that combines fuzzy logic (FL) and petri nets (PN) to model and simulate complex systems. The main objective of this research is to develop an intelligent energy-saving framework that integrates advanced methodologies and air conditioning (AC) systems to optimize energy utilization and create a comfortable indoor environment. The proposed system incorporates user identification to authorize individuals who can set the temperature, and FL combined with PN is utilized to monitor and transmit their preferred temperature settings to a PID controller for adjustment. The experimental findings demonstrate the effectiveness of integrating the FPN controller with a convertible frequency AC compressor in significantly reducing energy consumption by 94% compared to using the PN controller alone. The utilization of the PN controller alone resulted in a 25% reduction in energy consumption. Conversely, employing a fixed-frequency compressor led to a 40% increase in energy consumption. These results emphasize the advantages of integrating FL into the PN model, as it effectively reduces energy consumption by half, highlighting the potential of the proposed approach for enhancing energy efficiency in AC systems.

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

Research on the building energy efficiency design strategy of Chinese universities based on green performance analysis

Many cloud applications are data intensive requiring the processing of large data sets and the MapReduce/Hadoop architecture has become the de facto processing framework for these applications. Large data sets are stored in data nodes in the cloud which are typically SAN or NAS devices. Cloud applications process these data sets using a large number of application virtual machines (VMs), with the total completion time being an important performance metric. There are many factors that affect the total completion time of the processing task such as the load on the individual servers, the task scheduling mechanism, communication and data access bottlenecks, etc. One dominating factor that affects completion times for data intensive applications is the access latencies from processing nodes to data nodes. Ideally, one would like to keep all data access local to minimize access latency but this is often not possible due to the size of the data sets, capacity constraints in processing nodes which constrain VMs from being placed in their ideal location and so on. When it is not possible to keep all data access local, one would like to optimize the placement of VMs so that the impact of data access latencies on completion times is minimized. We address this problem of optimized VM placement - given the location of the data sets, we need to determine the locations for placing the VMs so as to minimize data access latencies while satisfying system constraints. We present optimal algorithms for determining the VM locations satisfying various constraints and with objectives that capture natural tradeoffs between minimizing latencies and incurring bandwidth costs. We also consider the problem of incorporating inter-VM latency constraints. In this case, the associated location problem is NP-hard with no effective approximation within a factor of 2 - ϵ for any ϵ > 0. We discuss an effective heuristic for this case and evaluate by simulation the impact of the various tradeoffs in the optimization objectives.

The article provides analysis of the influence of energy consumption on GDP in various regions and countries of the world (America, Europe, Post-Soviet space, Middle East, Africa, Asia-Pacific region). It is shown that: 1) there is a direct linear causal relationship between the growth of energy consumption and economic development in the absence of external effects in the field of energy efficiency; 2) external factors that contribute to the efficiency of energy consumption – ​institutional environment and scientific and technological achievements in the field of energy efficiency; change of the structure of the economy in favor of less energy-intensive industries. Models based on linear algebra, such as the Leontief model, can be used to describe the relationship between energy consumption and economic growth. Energy consumption in different sectors of the economy can have both positive and negative relationships with economic growth. A reduction of the share of industry and agriculture does not guarantee a simultaneous decline of energy consumption in the service sector. There is a direct linear relationship between energy consumption in the services sector as well as in the transportation sector and economic development of different countries around the world. Increased energy consumption in the transportation sector which supports economic development is connected with an increase of the well-being of the population in several countries. The inverse relationship shown by some of the study results does not indicate that all countries studied are more energy efficient. This outcome reflects only the presence of external influences that encourage energy efficiency. Energy saving is not a limiting factor for economic growth, but it does not occur in economic system without a set of external incentives in different sectors of economy.

The global energy crisis has been one of the significant challenges for decades, threatening the global economy, and the health of our environment. The government's efforts to enhance the welfare and lifestyle of citizens have been partially undermined by a significant rise in energy intensity, resulting in increased energy consumption. Over the years, researchers have utilized historical energy consumption data to enhance energy efficiency through various technologies. Innovative smart technologies drive energy efficiency, reducing energy usage throughout all areas of the energy industry, from production and supply to consumption. This creates a balance in all sectors and indicates a decrease in energy demand in all areas of building infrastructure. Green buildings that utilize Internet of Things (IoT) technology employ sensors and software to collect data, optimizing, and enhancing building performance. This includes reducing energy and electricity consumption, improving air quality, and optimizing lighting throughout the day. These buildings can contribute to reaching zero‐energy building targets. It becomes challenging to manage green buildings without using centralized control. Therefore, managing and integrating these buildings with intelligent technologies is vital in achieving environmentally friendly management. This study offers a broad overview of the green building concept and explores the use of the green IoT in enhancing services and conserving energy within green buildings. The article aims to deliver an extensive review of green buildings and their advantages, analyze the technology behind the IoT and its integration with solar panels to lower energy consumption, and ultimately identify the challenges present in this area of research.

Energy demand prediction and scenario analysis for vehicle traction: A bottom-up approach applied to the italian railway system

The healthcare sector greatly impacts the environment through energy, transport, product use, disposal and food service. This requires significant interventions to reduce energy consumption and waste generation and increase customer satisfaction simultaneously. Considering the need to investigate food waste amounts and energy consumption in hospitals, the present research has a triple goal. First, it evaluates energy consumption and efficiency by comparing cook-hold and cook-chill catering. Second, it estimates the customer satisfaction of the served meals and third, it assesses the food waste quantities at lunch and dinner among hospital patients. Specifically, the research combines (a) the evaluation of the energy consumption, based on primary data collected in a cooking center with a production capacity of 1590 meals per production cycle; (b) the investigation of the consumers’ behavior among 984 patients located in nine different hospital units in Southern Italy; (c) the measurement of food waste based on questionnaire-survey data. Results highlight the reduction in diesel consumption (– 42%) and in electricity consumption (– 93%), as well as the reduction in food waste (– 85%) when comparing cook-hold with cook-chill catering. This research illustrates a guideline in the field of efficient catering. It helps identify sustainable pathways and interventions toward energy efficiency, customer satisfaction, and food waste minimization for policymakers, healthcare professionals, catering companies or patients.

Influence of environmental variables on the energy efficiency of drinking water treatment plants

Improving energy efficiency (EE) is vital to ensure a sustainable, affordable, and secure energy system. The residential sector represents, on average, 18.6% of the total final energy consumption in the OECD countries in 2018, reaching 29.5% in the UK (IEA, 2020a). Using a staggered differences-in-differences approach with dynamic treatment effects, we analyse changes in residential gas consumption five years before and after the adoption of energy efficiency measures. The analysis includes energy efficiency interventions involving the installation of new heating-related insulation equipment—i.e., of loft insulation and cavity walls, supported by energy efficiency programmes in England and Wales between 2005 and 2017—using a panel of 55,154 households from the National Energy Efficiency Data-Framework (NEED). We control for, among other factors, energy prices and the extent to which gas consumption changes are dependent on household characteristics and variations in weather conditions. Our results indicate that the adoption of EE measures is associated with significant reductions in household residential gas consumption one year after their implementation. However, the effect does not last in the long run and energy savings disappear four years after the retrofitting of cavity wall insulation measures and after two years following the installation of loft insulation. The disappearance of energy savings in the longer run could be explained by the energy performance gap, the rebound effect and/or by concurrent residential construction projects and renovations associated with increases in energy consumption. Notably, for households in deprived areas, the installation of these efficiency measures does not deliver energy savings. These results confirm the existence of effects that reduce the energy savings from the adoption of these efficiency technologies over time and indicates that, for some groups, these net savings do not seem to materialize.

This study examines the energy intensity (EI), energy efficiency (EE), and economic growth, measured by the type of returns to scale (RTS), of 37 nations in the Organization for Economic Co-operation and Development (OECD) from 2000 to 2019. We apply a non-parametric approach to estimate the three measures from their consumption of four primary energy sources, such as coal, gas, oil, and zero emission (e.g., renewable and nuclear power) as inputs and gross domestic product (GDP) as an output. In this study, we have the two types of efficiency measures over time: window-based and cross-sectional-based measures. Three findings are identified from our empirical study. First, the operationally efficient group, including France, Iceland, Japan, Switzerland, UK, and USA, presented a stable status of full efficiency in the window-based efficiency measure. Iceland and Switzerland were also in the higher efficiency group based on the cross-sectional measure. Their efficiencies were high and stable over the observed periods. Second, zero-carbon-emission (e.g., renewable and nuclear) energies outperformed other energy sources (coal, gas, and oil) in terms of a potentiality of EI/EE improvement. In other words, OECD nations can improve on their EI/EE measures by reducing fuel consumption of coal, gas, and oil while maintaining their high GDP levels. Finally, four industrial nations (France, Japan, UK, and USA) had a status of unity in their EI/EE measures for zero-carbon-emission energies with decreasing RTS. These nations would increase zero-carbon emission for energy consumption to increase GDP while keeping optimal EI/EE because such changes in consumption would not largely affect EI/EE due to their constant RTS status. Iceland showed increasing RTS. The nation may improve the EI level by increasing zero-carbon-emission energy consumption and economic size. The four nations can increase zero-emission energy consumption to achieve further economic growth without observing a large deterioration of EI/EE because it is very close to constant RTS. The examination of RTS provides policy directions for the improvement of EI and EE. Switzerland showed decreasing RTS and may deteriorate the EI/EE by increasing energy consumption and the size of each economy. The remaining countries, whose degree of EI/EE measures was less than unity, showed increasing or decreasing RTS. The examination of RTS provides important implications for energy policy to enhance the degree of EI/ EE.