Energy Consumption Ratio Research Articles

Emergency Prioritized and Congestion Handling Protocol for Medical Internet of Things

Medical Internet of Things (MIoTs) is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body. The healthcare networks transmit continuous data monitoring for the patients to survive them independently. There are many improvements in MIoTs, but still, there are critical issues that might affect the Quality of Service (QoS) of a network. Congestion handling is one of the critical factors that directly affect the QoS of the network. The congestion in MIoT can cause more energy consumption, delay, and important data loss. If a patient has an emergency, then the life-critical signals must transmit with minimum latency. During emergencies, the MIoTs have to monitor the patients continuously and transmit data (e.g., ECG, BP, heart rate, etc.) with minimum delay. Therefore, there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability. The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data. In this paper, Emergency Prioritized and Congestion Handling Protocol for Medical IoTs (EPCP_MIoT) is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes. Whereas the emergency data transmissions are prioritized and transmit at SIFS time. The proposed technique is implemented and compared with the previous technique, the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput, end to end delay, energy consumption, and packet loss ratio.

Optimization of Resource Allocation Model With Energy-Efficient Cooperative Sensing in Green Cognitive Radio Networks

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users’ presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network’s performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

Evolution of Green Industrial Growth between Europe and China based on the Energy Consumption Model

Greenhouse gas (GHG) emissions are an important factor in the evaluation of green industrial growth, when low GHG emissions along with high industrial growth are expected. In this paper, the improvement of sustainable development of industry in China (2007–2015) was investigated via analysis of the relationships between the GHG emissions and energy consumption in comparison to European countries. A hierarchical cluster analysis (HCA) was conducted to distinguish industrial growth with GHG emission and energy consumption structures. The results of this research indicated that green industrial growth in Europe had a negative annual rate of GHG emissions. This contributed to the ratio of renewable energy consumption increasing to a maximum of 33% and an average of 16%. In comparison, the GHG emissions in China increased at a rate of 50% to 77% in the main industrial provinces since 2007 with their rapid industrial growth. The rate of GHG emissions decreased after 2012, which was 7% or less than the rate of emissions in the industrial provinces. Contrary to in Europe, the decreasing rate of GHG emissions in China was attributed to the improvement of fossil energy efficiency, as renewable energy consumption was less than 10% in most industrial provinces. Our data analysis identified that the two different energy consumption strategies improved green industrial growth in Europe and China, respectively. Our data analysis identified the two different energy consumption strategies employed by Europe and China, each of which promoted green industrial growth in the corresponding areas. We concluded that China achieved green industrial growth through an increase in energy efficiency through technology updates to decrease GHG emissions, which we call the “China Model.” The “Europe Model” proved to be quite different, having the core characteristic of increasing renewable energy use.

ВИЗНАЧЕННЯ ПОТУЖНОСТІ І РОБОТИ ВИМУШЕНИХ КОЛИВАНЬ ПРИ ВІДКЛЮЧЕННІ ЦИЛІНДРІВ В ДВИГУНІ КОЛІСНОЇ МАШИНИ

В даній статті наведені результати по потужності вимушених коливань двигуна та енергетичну діагра-му для вимушених коливань. Отримані залежності накладення вільних і вимушених коливань для різних умов спів падіння відносної частоти на величину потужності. Розглянуті перехідні процеси при вимушених коли-ваннях. Необхідність покращення рівномірності крутного моменту під час відключення циліндрів потребує впливу на показники потужності окремих циліндрів. Щоб знайти загальне рішення, потрібно додати вирази для вільних коливань, тобто загальне рішення однорідного рівняння.В цій роботі також виявляється можливим виділити активну і реактивну складові. Активна робота зро-стає лінійно з часом, в той час як реактивна робота є періодичною функцією часу. Поряд з зовнішньою збу-рюючою силою роботу здійснюють також і внутрішні сили коливальної системи. Робота сили інерції дорівнює кінетичної енергії маси, а робота поновлюючої сили дорівнює потенційної енергії напруженою пружини. При періодичних рухах обидві ці роботи також змінюються періодично і, маючи різні знаки, виключаються із зага-льного балансу енергії системи, якщо їх величина розраховується для одного повного коливання.Отримано відношення додаткових витрат енергії, зумовлених роботою стиснення робочих циліндрів у відключених циліндрах до зміни додаткових витрат енергії двигуна.Поряд з вібрацією двигуна роботу здійснюють також і внутрішні сили коливальної системи. Робота си-ли інерції дорівнює кінетичної енергії маси, а робота оновлюючої сили дорівнює потенційної енергії напруже-ною подушками двигуна. При періодичних рухах обидві ці роботи також змінюються періодично і, маючи різні знаки, виключаються із загального балансу енергії системи, якщо їх величина розраховується для одного повного коливання. Таким чином, обидві роботи є реактивними. Робота, чинена силами демпфування за один повний період, не виключається із загального балансу.Ключові слова: потужність, робота, вимушені коливання, відключення циліндрів, двигун, колісна машина, обурюючи сила.

Efficient Data Collection using Mobile Sink Scheme in IoT

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

The contributions of socioeconomic indicators to global PM2.5 based on the hybrid method of spatial econometric model and geographical and temporal weighted regression

PM2.5 pollution poses a negative effect on human health and economic growth. However, the major socioeconomic driving forces of global PM2.5 pollution during a long-term period remained unclear. In this study, we explored the potential association between socioeconomic indicators and the PM2.5 level worldwide using a spatial econometric model coupled with a geographical and temporal weighted regression (GTWR). The results suggested that renewable energy consumption ratio, per capita gross domestic production (GDP), per capita CO2 emission, urban population ratio, and fossil fuel consumption ratio were major factors responsible for the global PM2.5 pollution. The impacts of socioeconomic indicators on the PM2.5 level varied with the income-level and time. Fossil fuel consumption ratio, per capita CO2 emission, urban population ratio were major contributors for severe PM2.5 pollution in the developing countries (e.g., China and India). Further, these impacts have become more remarkable in recent years. Per capita GDP still played a crucial role on the PM2.5 pollution in India, indicating that energy-intensive industries were major contributors to its economic growth, thereby leading to the higher PM2.5 concentration in India. However, China has strode across the inflection of Environmental Kuznets Curve (EKC) as a whole and decreased the reliance on the secondary industries. Compared with the developing countries, the impacts of socioeconomic indicators on PM2.5 pollution in most of the developed countries remained relatively stable and weak, implicating that fossil fuel consumption and urbanization were not major contributors for local PM2.5 level. The findings of this study clarified major contributors for PM2.5 pollution, and provided scientific basis for mitigating the PM2.5 pollution.

Smart energy pricing for demand‐side management in renewable energy smart grids

AbstractSmart grids are expected to provide various benefits to society by integrating advances in power engineering with recent developments in the field of information and communications technology. One of the advantages is the support to efficient demand‐side management (DSM), for example, changes in consumer demands for energy based on using incentives. Indeed, DSM is expected to help grid operators balance time‐varying generation by wind and solar units, and the optimization of their usage. This paper focuses on DSM considering renewable energy generation and proposes an auction, in which consumers submit bids to renewable energy usage plans. An additional model is introduced to allow consumers to compute their bid for a given usage plan. Both models have been extended to include energy storage devices. The proposed model is compared to a system with time‐varying pricing for energy, where it is shown to allow consumers to use more appliances, to lead to a larger profit, and to reduce the peak‐to‐average ratio of energy consumption. Finally, the impact of the use of energy storage in households and in the energy provider is also considered.

Decomposition of CO2 emission intensity in Chinese MIs through a development mode extended LMDI method combined with a production-theoretical approach

An improved understanding of the influence of development mode on carbon intensity (CI) can aid China’s manufacturing industries (MIs) to reduce CI without affecting the development of Chinese MIs. To demonstrate the relationship between the development mode and CI well, this work provides the following contributions: (a) The driving factors of Chinese MIs’ CI are decomposed by a development mode extended Logarithmic Mean Divisia Index method (LMDI)); (b) The impact of the factors of industrial development mode is analyzed by comprehensively by the extended LMDI combined with production -theoretical decomposition analysis (PDA). (c) The disparities of the driving factors in different industrial classifications are evaluated by applying the development mode extended LMDI combined with PDA in Chinese MIs between 2000 and 2015. Results showed that, (1) at the entire MIs’ level, the effects of ratio of energy consumption to R&D (RI) and potential carbon intensity (PCI) were the two leading contributors to the CI decline in Chinese MIs. (2) In terms of the cumulative effects at the subsectoral level, PCI had the largest curbing effect on the CI of all subsectors, and investment intensity (II) had the greatest stimulating effect on the CI of all subsectors. (3) Among the three industrial classifications, the middle-end MIs experienced the largest carbon intensity decline from the 10th Five-Year Plan (FYP) to the 12th FYP, followed by the low- and high-end MIs. RI and PCI decreased the CI of Chinese MIs in all industrial classifications and economic development stages. Potential energy efficiency and II were the two major contributors to CI improvement in all industrial classifications and economic development stages.

Research on Seismic Assessment of Gravity Dam Based on Performance Analysis

Based on the incremental dynamic analysis method, a new index (the ratio of plastic energy consumption to total deformation energy) for evaluating the seismic performance of earthquake-resistant dams is proposed. Suggestions for three levels of earthquake damage and failure classification in two extreme states of gravity dam seismic performance evaluation are given. The IDA analysis of the concrete gravity dam was carried out by nonlinear numerical analysis model. Based on this, the quantile analysis and fragility analysis were carried out. The results show that ①the peak ground acceleration PGA of each limit state in the IDA curve of the quantile analysis is larger than the peak ground acceleration of the designed earthquake, which satisfies the level of functional support and safety assurance of the gravity dam. ②Under the fragility analysis, the probability of 65.92% is in the normal use stage of the 1.5 times design earthquake, and 100% can guarantee safety. The results show that the gravity dam has good seismic performance. The results of this study provide new ideas for the seismic performance design and safety risk assessment of gravity dams.

CARMA: Channel-Aware Reinforcement Learning-Based Multi-Path Adaptive Routing for Underwater Wireless Sensor Networks

Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.

Establishment of Clean Energy Demand Forecasting Model in China Based on Genetic Algorithms

In the future, energy development will face a series of severe challenges, such as insufficient domestic conventional energy resources, huge oil supply gap, large amount of clean energy needed by cities, and huge international pressure on global climate change. This paper uses historical data and genetic algorithm to improve it, and uses function expansion model to reveal the internal proportional connection between energy consumption and economic development. By comparing the forecasting methods of clean energy demand, it is found that the energy consumption elasticity coefficient method is used to measure and predict the growth ratio of energy consumption, and the figure in the next few years is measured. The software has self-built optimization model, reflects energy supply and demand fluctuations, and generates reports. It can make decision support for regional clean energy system planning, miscalculation and clean energy demand forecasting.

The impact of energy regulation on energy intensity and energy structure: Firm-level evidence from China

In this paper, we empirically investigate the impact of energy regulation on manufacturing firms' energy intensity and energy structure during 2003–2009. The identification uses the energy regulation of the 11th Five-Year Plan implemented in 2006. We show that tighter energy regulation leads to a significant energy intensity decrease and that firms switch their energy structure from using dirty fossil energy to a cleaner one. We also examine the mechanisms behind this phenomenon and find that the rising R&D inputs and increasing energy consumption ratio of high energy efficient firms are the mainly two reasons.

Field investigation on operation parameters and performance of air conditioning system in a subway station

Ventilation and air conditioning system in subway stations accounts for about 50% of the total energy consumption of the subway stations. The energy consumption ratio of public area to equipment room is about 6.5:3.5. In order to understand the energy saving potential of public areas and equipment rooms, a field measurement for a typical subway station in central China was conducted from 19 to 29 September 2018. The field testing parameters include weather conditions, indoor air temperature and humidity, supply and return air flow rate, chilled water temperature, and flow rate. Meanwhile, some parameters recorded by the system control panel, such as pump frequency, chiller power demand, and energy consumption, were also collected. The paper studied the operating characteristics of the ventilation and air conditioning system and the cooling load pattern of the subway station, focusing on the following three aspects: cooling capacity and power demand of the chiller under different conditions, dynamic cooling capacity of the air handling units, and cooling load characteristics of the equipment rooms. Different operation conditions influence the operation of chiller. It was found that the real power demands under two operation conditions, i.e. minimum and all fresh air modes, are 121 and 77 kW, and the corresponding coefficient of performances are 4.33 and 4.77, respectively. The actual cooling capacity at night is about 80% that of the daytime, and is about 40% of the design value, which indicates that the air handling units operate under partial load. During the testing period, the actual cooling load is about 10–50% of the design load, of which 50–95% is sensible cooling load. These data can help readers understand the overall situation of a typical subway station adequately and provide numerical basis for energy saving optimization of the ventilation and air conditioning system.

The influence of multi-speed transmissions on electric vehicles energy consumption

The electric vehicle (EV) represents an important option in the process of road transport decarbonization. The limited driving range and high speed performances are obstacles for EV customer acceptance. While until now the emphasis for the improvement was on battery, power electronics and motor technologies, it becomes obvious that the transmission must be also addressed. The extensively used single-ratio transmission can satisfy the demands of city use, but do not attain other important functional needs that are ensured by conventional and hybrid electric vehicles. The aim of this paper is to analyse the energy consumption of an EV with multi-speed transmission in five test cycles among which Worldwide Harmonized Light duty Test Cycle (WLTC) is considered. This study is done by means of simulation using a model specifically developed in a simulation environment. In the interest of analysing the performances in several test cycles, one needs to compare global parameters such as: vehicle speed fluctuation (VSF), overall transmission efficiency, energy consumption ratio (ECR). A comparison between test cycles is made taking into consideration a relative parameter (ECR). The correlation between ECR and VSF is investigated as well. The results are also compared with those produced for a vehicle with diesel engine.

Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony

Fog computing is a set of mobile cloudlets which can fulfil the demand of the user who is already considered a mobile job in this architecture. The main aim of Fog computing is to provide the user with an optimal solution which is quick and cost-efficient. This article focuses on a load balancing mechanism for cloudlets along with keeping the cost-effectiveness as an optimal selection parameter. This article utilizes the Artificial Bee Colony (ABC) in order to prioritize the user demand using a fitness function. This work evaluates quality of service (QoS) parameters such as schedule length runtime (SLR), schedule length vm ratio (SLVMR), energy consumed (EC) and energy consumption ratio (ECR) and shows the effectiveness of proposed work.

OPTIMUM DESIGN OF RETAIL AND WHOLESALE BUILDING FOR MINIMUM ENERGY CONSUMPTION AND TOTAL COST

The electric consumption in the building shares the highest portion in all user sectors. Therefore, energy conservation in building is important to get higher energy standard in the country. Retail and wholesale building consume high electricity. Energy consumption ratio in this building type is 40% Air conditioning, 28% Refrigeration, 12% lighting and 20% from the other. Those element need to be focused. This research is an optimization of air conditioning design and building envelope with operation schedule, refrigeration and lighting system. The objective is to find out optimization design to minimize total cost. The design criteria is to maintain requirement at day time whereas to response refrigeration system at all night. The developed designed is interacted between building system and building envelop. Case study is retail and wholesale building in Thailand. The design with life time of the machine, replacement cost and energy consumption of each option will be used to evaluate total life cycle cost. This study results in the total life cycle cost of new model of 1,014.14 M Baht comparing with 1,207.91 M Baht of conventional design. It has been saved 193.76 M Baht or 16% for 30 years. Therefore, the benefit of new improvement is very high and helps building owner to save a huge amount of money.

An optimal Security Mechanism to Preventmulti-threat in MANET using Bee Algorithm

The special characteristics of the Mobile and Ad hoc network (MANET) attract the users to access the services provided by the network. But, accessing these services such as high mobility also creates an issue of unstable routing which leads to packet dropping. To address this issue, the packet dump situation is considered in this document under normal and distortion mode. This paper also focused on black hole attack (BHA) and selective packet drop attack (SPDA) and has a threat prevention mechanism has been proposed. The whole paper is divided into three frames namely employing deployment model, the importance of intrusion and its prevention, and registration of new node through interpolation named as Chebyshev method. The prevention structure has been modeled using swarm and machine intelligence. In addition, the affected route has been detected using the Artificial Bee Colony (ABC) inspired Optimal Bee Behaviour (OBB) algorithm. The different parameters, such as, Delay, energy consumption,Throughput and Packet Delivery Ratio (PDR) are computed. The proposed work consumes less energy as the developed architecture compared with sate of art techniques which states that energy consumption revamped by 12%. The delay rate has been ameliorated by 69% and 10% in contrary to past studies. The proposed prevention structure provides 1.8% improvement for throughput in comparison to without prevention architecture. However, proposed model provides 0.94 PDR using the polynomial kernel in comparison to state of art techniques. The comparative analysis has been performed with the existing work and outperformance has been noticed in terms of QoS.

Energy efficient VM scheduling strategies for HPC workloads in cloud data centers

In virtualized environments, virtual machine (VM) scheduling strategies incorporated with energy efficient techniques are needed to reduce the operational cost of the system while delivering high Quality of Service (QoS). It is widely accepted that the cost of the energy consumption in the environment is a dominant part of the owner’s budget. However, when considering energy efficiency, VM scheduling decisions become more constrained, leading in the violation of job deadlines and hence compromising QoS. This paper studies energy efficient VM scheduling strategies in virtualized environments to minimize the queue time and makespan under the fulfillment of SLA requirements (i.e., deadline). Specifically, six energy efficient VM scheduling strategies are investigated incorporated with the dynamic voltage and frequency scaling (DVFS) power management technique. They consist of user-oriented and system-oriented policies. The strategies are extensively simulated and compared with three power management governing methods provided at hypervisor level (i.e., userspec, ondemand, and performance). To conduct simulation experiments, we employ real-world high performance computing (HPC) workloads collected from a production data center. For comparison and evaluation, we analyze the: (a) energy consumption, (b) runtime, (c) queue time, (d) makespan, and (e) slowdown ratio. Lastly, we highlight the strengths and weaknesses of the VM scheduling strategies that can help to choose the most appropriate VM scheduling strategy for a given scenario.

Energy efficient optimized rate based congestion control routing in wireless sensor network

A wireless sensor network is designed to facilitate various real time applications, constituting a wide range of sensor nodes. In order to provide energy efficient transmissions, a novel congestion control mechanism is proposed on optimized rate. Here, the rate-based congestion control algorithm based on cluster routing is introduced to reduce energy consumption throughout the network. Rate control process reduces the end to end delay to improve network life time for large simulation period. Initially, nodes are clustered by the hybrid K-means and Greedy best first search algorithms. After that, the rate control is performed using firefly optimization strategy which is suitable for high packet delivery ratio. Finally, packets are sent with maximum throughput using Ant Colony Optimization-based routing. The simulation is performed on the MATLAB simulation platform. Finally, performances are evaluated with respect to, average delay in end to end node, delivery ratio of packets, throughput, energy efficiency, energy consumption and reliability.

Analysis of Ratios of Propulsion Energy Demand for Transport by Bulk Carriers

Abstract The article presents the results of calculations and analysis of the eM ratio of the propulsion energy consumption per the nautical mile and the eMt ratio of the propulsion energy demand for the transport of 1-ton cargo per the nautical mile for bulk carriers. The relationship between eM and eMt indicators with the EIV (Estimated Index Value) ratio is shown. The output data for the determination of eM and eMt indicators was taken from the quoted MAN publication, reading (reproducing) the values of power, velocity v and DWT tonnage from the charts included in this publication. Calculations and analyses were made for velocity v between 11-15 knots and for DWT from 5-400 kt (kilo tonnes). The graphs of eM and eMt ratios in the function of speed v and load capacity DWT are presented. Using the stepwise method of least squares, models of statistical dependence of indicators on velocity v and load capacity of bulk carriers were developed. They were based on generalized polynomials with integer exponents. Derivatives deM / dv and deMt / dv and derivatives deM / dDWT and deMt / dDWT were determined. The main observations resulting from the conducted analyses are observed with the increase of DWT (within the range of 80-100 kt) a significant decrease in the value of the eM index. For DWT values> 100 kt, the decrease in eMt is slow − eMt asymptotically tends to a constant value. The results of the analyses carried out can be used at the stage of designing the transport capacity (tonnage) and nominal speed of the ship as well as selection of ship tonnage and travel speed in a given transport situation.