Energy Efficiency Balance Research Articles

Energy Efficient Hardware Loop Based Optimization for CGRAs

Research interest and industry investment in edge computing solutions have increased dramatically in recent years. Consequent quest for balanced performance, energy efficiency and flexibility bestowed surging popularity on Coarse Grained Reconfigurable Array (CGRA) architectures. To further improve the performance and energy efficiency, several hardware and software-based loop optimizations are adopted for CGRAs. In this paper, we propose a centralized hardware-based loop optimization technique to achieve better area and energy results compared to the previously implemented distributed version. Without incurring any performance degradation, area overhead against the reference architecture is reduced down to \(1.5\%\) for a 4\(\times\)2 CGRA configuration. A maximum of \(47.3\%\) and an arithmetic mean of \(27.2\%\) reduction in energy consumption is attained by the centralized version of hardware loop compared to the baseline model employing software loop. Furthermore, the paper explores the co-existence of CGRA-specific hardware and software optimizations and their impact on loop efficiencies. Enhanced results are obtained by coupling loop unrolling with centralized hardware loop support. The combination allows achieving up to \(68.7\%\) reduction in energy consumption and 5.46\(\times\) speed-up against the baseline model with no optimizations applied.

Ivabradine in the Management of COVID-19-related Cardiovascular Complications: A Perspective.

Besides acute respiratory distress syndrome, acute cardiac injury is a major complication in severe coronavirus disease 2019 (COVID-19) and is associated with a poor clinical outcome. Acute cardiac injury with COVID-19 can be of various etiologies, including myocardial ischemia or infarction and myocarditis, and may compromise cardiac function, resulting in acute heart failure or cardiogenic shock. Systemic inflammatory response increases heart rate (HR), which disrupts the myocardial oxygen supply/demand balance and worsens cardiac energy efficiency, thus further deteriorating the cardiac performance of the injured myocardium. In fact, the combination of elevated resting HR and markers of inflammation synergistically predicts adverse cardiovascular prognosis. Thus, targeted HR reduction may potentially be of benefit in cardiovascular pathologies associated with COVID-19. Ivabradine is a drug that selectively reduces HR via If current inhibition in the sinoatrial node without a negative effect on inotropy. Besides selective HR reduction, ivabradine was found to exert various beneficial pleiotropic effects, either HR-dependent or HR-independent, including anti-inflammatory, anti-atherosclerotic, anti-oxidant and antiproliferative actions and the attenuation of endothelial dysfunction and neurohumoral activation. Cardioprotection by ivabradine has already been indicated in cardiovascular pathologies that are prevalent with COVID-19, including myocarditis, acute coronary syndrome, cardiogenic shock or cardiac dysautonomia. Here, we suggest that ivabradine may be beneficial in the management of COVID-19- related cardiovascular complications.

Cardiometabolic effects of DOCA-salt in male C57BL/6J mice are variably dependent on sodium and nonsodium components of diet.

Hypertension characterized by low circulating renin activity accounts for roughly 25%-30% of primary hypertension in humans and can be modeled experimentally via deoxycorticosterone acetate (DOCA)-salt treatment. In this model, phenotypes develop in progressive phases, although the timelines and relative contributions of various mechanisms to phenotype development can be distinct between laboratories. To explore interactions among environmental influences such as diet formulation and dietary sodium (Na) content on phenotype development in the DOCA-salt paradigm, we examined an array of cardiometabolic endpoints in young adult male C57BL/6J mice during sham or DOCA-salt treatments when mice were maintained on several common, commercially available laboratory rodent "chow" diets including PicoLab 5L0D (0.39% Na), Envigo 7913 (0.31% Na), Envigo 2920x (0.15% Na), or a customized version of Envigo 2920x (0.4% Na). Energy balance (weight gain, food intake, digestive efficiency, and energy efficiency), fluid and electrolyte homeostasis (fluid intake, Na intake, fecal Na content, hydration, and fluid compartmentalization), renal functions (urine production rate, glomerular filtration rate, urine Na excretion, renal expression of renin, vasopressin receptors, aquaporin-2 and relationships among markers of vasopressin release, aquaporin-2 shedding, and urine osmolality), and blood pressure, all exhibited changes that were subject to interactions between diet and DOCA-salt. Interestingly, some of these phenotypes, including blood pressure and hydration, were dependent on nonsodium dietary components, as Na-matched diets resulted in distinct phenotype development. These findings provide a broad and robust illustration of an environment × treatment interaction that impacts the use and interpretation of a common rodent model of low-renin hypertension.

Application of Game Theory to Decision Making in Green Energy Promotion

With the promotion and use of intelligent decision systems, intelligent green energy choice decision systems are also maturing. This paper uses the game theory approach of cost- benefit analysis basic local social and energy environment characteristics to analyse how to balance energy efficiency and economic benefits when choosing green energy by guiding and motivating building owners’ decisions to achieve the best environmental energy economic benefits. This paper uses green roof technology as a case study. Because it not only protects the natural environment and improves human living standards, but also creates economic benefits in many ways, it is important to incorporate such a decision theory into an intelligent decision-making system to improve the efficiency and intelligence of the decision and to provide a realistic proposal for the government to choose subsidies for environmental, economic and social benefits.

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e. cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.

Largely Improved Breakdown Strength and Discharge Efficiency of Layer‐Structured Nanocomposites by Filling with a Small Loading Fraction of 2D Zirconium Phosphate Nanosheets

AbstractDielectric film capacitors have aroused considerable attention on account of the fast development of pulsed power systems. However, enhanced energy density is always acquired at the cost of deteriorated charge/discharge efficiency. Herein, well balanced energy density and efficiency are achieved in a series of reasonably designed bilayer composites consisting of a ferroelectric layer and a paraelectric layer at the meantime. It is interesting to find that, when merely 1.6 wt% Zr(HPO4)2 nanosheets are introduced into the ferroelectric layer, a substantially improved energy density of 11.22 J cm−3, which is about 165% that of the bilayer composite without Zr(HPO4)2 nanosheets, is achieved at 650 kV mm−1. Meanwhile, a high charge/discharge efficiency of 89.8% and a low loss tangent of 0.024@10 kHz which is much lower than the pristine ferroelectric polymer layer (0.058@10 kHz) is maintained. Furthermore, finite element simulation reveals that the electric breakdown paths will develop along the macroscopical‐interfaces between adjoining layers and the microcosmic‐interfaces between the Zr(HPO4)2 nanosheets and polymer matrix, which can effectively increase the length of breakdown paths and contribute to improved breakdown strength. This work demonstrates that the Zr(HPO4)2 nanosheets can be promising fillers for other high‐performance dielectric composites.

Heat Balance Calculation and Energy Efficiency Analysis for Building Clusters Based on Psychrometric Chart

How to perform accurate calculation of heat balance and quantitative analysis of energy efficiency for building clusters is an urgent problem to be solved to reduce building energy consumption and improve energy utilization efficiency. This article proposes a method for the heat balance calculation and energy efficiency analysis of building clusters based on enthalpy and humidity diagrams and applies it to the energy management of building clusters containing primary return air systems and heating pipe networks. Firstly, the basic structure and energy management principle of building clusters with a primary return air system and a heating pipe network were given, and the heat balance calculation and energy efficiency analysis method based on i-d diagram was proposed to realize the accurate calculation of heat load and the quantification of energy utilization. Secondly, the energy management model of the building cluster with a primary return air system and a heating pipe network was established to efficiently manage the indoor temperature and the heating schedule of ASHP, HN and HI. Finally, the proposed method was validated by calculation examples, and the results showed that the proposed method is beneficial for improving the energy economy and energy efficiency of building clusters.

Towards Differentiated Energy Renovation Strategies for Heritage-Designated Multifamily Building Stocks

The historic building stock is not homogeneous, which implies a need for differentiated energy renovation strategies in order to balance energy efficiency requirements and building conservation goals. This paper presents a new method for developing a base for differentiated energy renovation strategies for heritage-classified multifamily building stocks. Our suggested method combines different building databases using an extract, transform and load (ETL) technology. The method for this study was tested on the available information for heritage-designated and -classified multifamily buildings in the municipality of Stockholm, Sweden, and in the county of Halland, Sweden. The two cases reflect the heterogeneity of the Swedish Building stock. An important achievement is that the results visualise the relationship, not detectable before, between energy use, energy performance, year of construction and heritage classification within each of the selected building stocks. A specific result is that the energy-saving potential in the older building stock is insignificant in relation to the entire stock. The results contribute to an improved understanding of relationships both within and between the two historic building stocks, which is useful for developing differentiated energy renovation strategies.

Energy and daylight performance of a smart window: Window integrated with thermotropic parallel slat-transparent insulation material

With the increasing awareness of building energy efficiency, indoor environment quality for human wellbeing and working efficiency, efforts have intensified in to inventing intelligent building components. This paper provides a first step in developing a novel multi-effect smart window system, which achieves enhanced energy efficiency and an improved indoor luminous environment by integrating a Transparent Insulation Material (TIM) structure incorporating a Thermotropic material. This system automatically regulates the admittance of solar heat and natural light into the building by responding to a changing environment while taking advantage of the increased thermal resistance and scattered daylight of window integrated TIM. A comprehensive workflow via EnergyPlus and RADIANCE was used to accurately predict the luminous and energy performance of applying the smart window system on a typical south-facing office under selected climates (London, Stockholm, Rome and Singapore). The effect of the optical properties and transition temperature of thermotropic material on building performance was explored in detail. Annual simulation results predict that, with a careful selection of the Thermotropic material properties, installing the TT PS-TIM window system is able to yield up to a 27.1% energy saving when compared with a conventional double glazed window, under the modelled Rome climate. TT PS-TIM windows also provide dynamic daylight control, resulting in increased daylight availability with the percentage of working hours that fall into the UDI500–2000 lx range increasing to 62.3%. The results of this research provide guidance for the next step of the material design and development that seek to balance energy efficiency and solar and daylight control through the use of thermotropic materials.

SCEER: Secure cluster based efficient energy routing scheme for wireless sensor networks

Balancing network integrity and energy efficiency is the one of major tasks in the network. In existing schemes, it is failed to balance both the metrics. In this research work, the Secure cluster based efficient energy routing is adopted to attain the more network lifetime. In first phase of the research, network and routing assumptions are made to initialize the effective packet transmission. In second phase, stability metric is determined for the cluster to maximize the energy efficiency. There are two cluster metrics used for the analysis of network stability. In third phase, the optimal cluster design model is initialized to balance the network integrity and energy efficiency. The proposed scheme is evaluated with network simulation tool in terms of node stability ratio, node reliability ratio, network lifetime, packet delay and energy efficiency.

Edge computing assisted privacy-preserving data computation for IoT devices

Along with the ubiquitous deployment of IoT devices, requirements on sensing data computation and analysis increase rapidly. However, the traditional cloud-based architecture is no longer sustained the computation load from these tremendous IoT devices, which bring the paradox of delay tolerance and bandwidth insufficiency. Fortunately, the edge computing is emerged and incorporated with the IoT network. Meanwhile, new questions arises. When and how to select among edge computing servers, and also achieve a well balance between consumed energy, transmission delay and data privacy. In this paper, we consider the problem that how IoT devices allocate their computation loads among edge computing servers and their on-chip computation units, to balance energy efficiency and data privacy in physical layer. Firstly, the optimization function of IoT devices is derived which reflects the energy consumption, transmission delay and also privacy requirement; Secondly, the direct transmission scenario is analyzed, and optimal transmit power are derived with or without privacy factors; Thirdly, we extend the model to relay transmission scenario when edge computing servers are far away, and propose the relay selection algorithm for IoT devices; Finally, by extensive simulations, two main conclusions are verified: the energy consumption remains the same with data privacy protection, energy saved 54.9% on average using relay IoT devices compared to direct transmission case.

Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study

Modern literature exhibits numerous centralized control approaches—event-based or model assisted—for tackling poor energy performance in buildings. Unfortunately, even novel building optimization and control (BOC) strategies commonly suffer from complexity and scalability issues as well as uncertain behavior as concerns large-scale building ecosystems—a fact that hinders their practical compatibility and broader applicability. Moreover, decentralized optimization and control approaches trying to resolve scalability and complexity issues have also been proposed in literature. Those approaches usually suffer from modeling issues, utilizing an analytically available formula for the overall performance index. Motivated by the complications in existing strategies for BOC applications, a novel, decentralized, optimization and control approach—referred to as Local for Global Parameterized Cognitive Adaptive Optimization (L4GPCAO)—has been extensively evaluated in a simulative environment, contrary to previous constrained real-life studies. The current study utilizes an elaborate simulative environment for evaluating the efficiency of L4GPCAO; extensive simulation tests exposed the efficiency of L4GPCAO compared to the already evaluated centralized optimization strategy (PCAO) and the commercial control strategy that is adopted in the BOC practice (common reference case). L4GPCAO achieved a quite similar performance in comparison to PCAO (with 25% less control parameters at a local scale), while both PCAO and L4GPCAO significantly outperformed the reference BOC practice.

Data Forwarding in Wireless Body Area Networks

One of the most crucial application of Wireless Body Area Networks in healthcare applications is the process of monitoring human bodies and gather physiological data. Network performance degradation in the form of energy efficiency and latency are caused because of energy depletions which arises due to limited energy resource availability. The heterogeneity of body sensors will lead to variation in the rate of energy consumption. Based on this, a novel Data Forwarding Strategy is presented in this research work to enhance collaborative WBAN operations, improve network lifetime and restrict energy consumption of the sensors. In this paper, we have contributed towards reducing the size of data to be transmitted by compressed sensing and selection of relay sensor based on sampling frequency, energy levels and sensor importance. Using the proposed methodology, it is possible to improve both reliability and energy-efficiency of WBAN data transmission. moreover, it is also possible to adapt to the changing WBAN topologies when the proposed methodology is used, balancing energy efficiency and consumption.

Multi-perspective design of energy efficiency policies under the framework of national energy and climate action plans

Towards the fulfilment of Paris Agreement, European Union aims to achieve a set of ambitious climate and energy targets, with energy efficiency pillar having the protagonist role. The paper proposes a multi-perspective approach towards designing a balanced, realistic and cost-optimal energy efficiency policy portfolio, in the extended framework of the NECP, taking into account a set of targets, constraints, particularities and political priorities. In this respect, going from measures to policies, the proposed methodology achieves to design an optimal energy efficiency portfolio, balancing the scale between the diversification of different financial sources and the associated risk. The proposed methodology is applied for supporting the Greek NECP. Greek State has declared its financial limitations towards the fulfillment of its EE targets. The results of the application show that the mobilisation of additional public-private co-investments, through the use of appropriate financial mechanisms, appears to be a feasible model, and reveals a high cost effectiveness improvement potential against the existing policies.

When Wireless Crowd Charging Meets Online Social Networks: A Vision for Socially Motivated Energy Sharing

Individual users of a social crowd are bound by finite resource capacity. Other users (such as online friends) may have surplus capacity or resources that, if shared, could be used to meet fluctuating demand. A particularly important resource in modern societies is the energy supplies of mobile portable devices like smartphones; quick energy depletion is an everyday problem in the lives of billions of smartphone users worldwide. Peer-to-peer wireless crowd charging has recently emerged as an alternative energy replenishment option. Although there have been some recent advances on this type of wireless energy sharing, to the best of our knowledge, none of the related works has considered using online social network information as input. In this paper, for the first time in the state of the art, we introduce the vision of socially motivated wireless energy sharing and we provide a holistic framework for socially aware wireless crowd charging. We present a taxonomy of the existing representative use cases, and we define a model which takes into account not only the wireless power transfer specifics, but also the online social information. After having selected a target use case (balanced crowd energy efficiency), we design two socially aware wireless energy sharing protocols. The design of each protocol is inspired by the social topology and structural characteristics of diverse datasets of online social relationships among real users. In fact, aligning to studies on psychology which conclude that users typically favor friends over strangers in allocating resources (especially when the sharing task is costly), our protocols favor energy exchanges among friends or friendly groups. For the purposes of the protocol evaluation, we simulate the energy sharing interactions based on the user encounters, as reported in the datasets. In order to demonstrate the effects of the social component, we compare the performance of our protocols to the performance of another state of the art protocol that does not use online social information. Interestingly enough, we demonstrate that online social network information can indeed influence the selected use case, in terms of energy losses, as well as level of and time elapsed until a balanced energy distribution in the crowd. We conclude the paper by providing some interesting open research directions.

A Reinforcement Learning-Based Duty Cycle Adjustment Technique in Wireless Multimedia Sensor Networks

Multimedia delivery support has recently been added to Wireless Sensor Networks (WSN) and has led to increased interest in Wireless Multimedia Sensor Networks (WMSN). WMSNs are expected to be crucial to the success of applications related to the Internet of Things (IoT), such as smart health, smart surveillance, smart homes, etc. Alongside their improved multimedia capabilities, WMSNs inherit WSN limitations such as energy and processing constraints. Additionally, WMSNs have significant Quality of Service (QoS) requirements, since multimedia delivery requires increased network performance in terms of bandwidth, latency, etc. Balancing energy efficiency and QoS is a fundamental challenge for WMSN users and operators alike. This paper proposes Reinforcement Learning based Duty Cycle (rlDC), an innovative learning-based scheme to adjust the duty cycle and contention window of WMSN nodes in order to meet energy efficiency and QoS targets. By employing rlDC, WMSN sensor nodes intelligently adapt their operation according to network delivery performance and application requirements. The proposed rlDC scheme was evaluated under different use cases in a simulation environment, and testing results show it outperforms other state-of-the-art duty-cycle-based protocols for WMSNs.

Cross-Layer Optimized Energy-Balanced Topology Control Algorithm for WSNs

Because of the complexity of the environment and the limited resources of nodes, there will be an imbalanced energy consumption and a short life among nodes in the wireless sensor network. In this paper, by introducing the concept of game theory and supermodel game theory, we solve the challenge of a wireless sensor network topology control method based on cross-layer information design. The cross-layer information such as node degree, network connectivity, and MAC layer interference is integrated into the design of utility function to establish a new topological game model. Then, based on this topology control model, we propose a cross-layer optimized energy-balanced topology control algorithm (COETC). Compared with other algorithms, our COETC algorithm not only guarantees the network connectivity and robustness while reducing the required node transmitting power but also achieves good energy balance and high energy efficiency. Ultimately, our method effectively prolongs network lifetime and improves network performance.

A Feasibility Study on the Waste-to-Biogas SOFC-Based Multi-Generation with Energy Storage System for Building Applications in China

The Waste-to-Energy Multi-generation system provides a feasible distributed waste recycling and energy saving solution for green buildings. However, the fluctuations in electricity demands and dynamic built environment obstruct its marketization. This study optimizes the existing waste-biogas-SOFC multi-generation configuration with energy storage systems. The research employs the operation data from an international airport in Southern China as a case study. The study compares the proposed system and that of previous SOFC-based systems in terms of economic and environmental benefits. The results show that the demand and supply of energy is balanced and higher energy efficiency and a significant reduction in the Greenhouse Gas (GHG) emission is achieved. The system presents a solid performance in payback and lifecycle cost (LCC) analysis and also satisfactory resistance to financial risks. Finally, this study draws the conclusion that the energy storage system could be utilized to optimize the existing SOFC multi-generation system.

Comparative study of energy efficiency and environmental impact in magnetic field assisted and conventional electrical discharge machining

Electrical discharge machining is a significant non-traditional technique, widely used in the precise manufacturing field. However, the electrical discharge machining process possesses plenty of energy consumption and hazardous exhaust emissions. Since a sustainable machining technique, external magnetic field assisted, could dramatically develop sustainable machining performance of electrical discharge machining process, this paper presents a comparative study energy efficiency and environmental impact in magnetic field assisted electrical discharge machining and conventional electrical discharge machining. A novel thermo-physical model was proposed to calculate energy distribution under the single/continuous discharge, which was verified by a series of corresponding experiments. Then, based on simulation and experimental methods, a comparative study on the magnetic field assisted electrical discharge machining and conventional electrical discharge machining was conducted to analyze the discharge energy flowing and temperature distribution of workpiece, electrode tool and debris. Moreover, the effects of external magnetic field on the machining performance, environmental impacts and energy efficiency were presented. Eventually, it can be found out that the effect of magnetic field contributes to increasing energy utilization efficiency by 15.2%, material erosion efficiency by 22.6%, and material removal rate by 21.9%. To balance energy efficiency and surface roughness (Ra), the optimal parameter combination is obtained as below: magnetic field intensity: 0.2T, discharge current: 7–9A, and pulse duration: 300s.

Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks

In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) by trading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays under two scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation, and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power, and power splitting ratio.