Energy Consumption Ratio Research Articles

A Secure LEACH Protocol for Efficient CH Selection and Secure Data Communication in WSNs

Due to the applications of wireless sensor networks (WSNs) in distinct fields, like civilian and military, environmental monitoring, and target tracking, etc., it became the important research area for researchers. Therefore, an efficient organization of network topology is needed for multi-hop data aggregation based on the energy constrained WSNs with large scale deployment for making the routing task simpler, balancing the load efficiently, and enhancing the network lifetime. The low-energy adaptive clustering hierarchy (LEACH) is an architecture of application-specific protocol, which is used for WSNs. However, this LEACH protocol suffered from more energy consumption in network without considering the cluster heads (CHs) distribution on a rotation basis. In addition, the complex security mechanisms are not sufficient due to limited bandwidth and other restrictions that the sensor nodes have. Thus, it is essential to improve the energy efficiency, CH stability and secure data transmission in WSN’s, which is the main objective of this work. Motivated from these three challenges, this article proposes a stable and secure LEACH (SS-LEACH), in which a new and improved protocol of LEACH is proposed for enhancing the stability of CH and energy efficiency as it considers the nodes’ consumed energy ratio (CER) for CH selection and random number generation, here after the proposed LEACH is named as CER-LEACH. In addition, it aims to prevent the previously elected CH node and it will get another chance in the current round. This method correlates the threshold used in traditional LEACH with the energy consumption ratio of each node. Further, it also introduces a hybrid reputation-based data transmission (HRDT) scheme for secure data transmission. With the proposed CER-LEACH protocol, WSNs achieves better performance in terms of secured communication, network lifetime, and energy consumption based on the analysis of simulation results as compared to state-of-art approaches.

Numerical simulation of heat pump drying system for paper exhaust hood

Abstract In view of the present situation that the exhaust waste heat of the hot air hood in the paper drying section is too much and the environment is not friendly, a closed heat pump drying system model applied to the exhaust air hood is proposed and established. Effects of evaporation temperature and condensation temperature on supply air temperature, supply air moisture content, system performance coefficient (COP) and dehumidification energy consumption ratio (SMER) were studied. Results show that with the increase of condensation temperature, the supply air temperature is unchanged, and the moisture content of the supply air increase, while the COP and SMER of the system show a downward trend. The moisture content of supply air decrease with the increase of evaporation temperature, while the supply air temperature, COP and SMER all show an upward trend. Therefore, in order to improve the drying characteristics and improve the performance of the heat pump system, the condensation temperature can be reduced and the evaporation temperature can be increased.

Optimal Design of Failure Mode Control for Semi-rigid Steel Frame Based on Elitist Retained Genetic Algorithm

The post-earthquake reports show that failure mode control design is of great significance to improve the seismic performance of structures. To realize the optimal design of failure mode in semi-rigid steel frame, a failure mode optimisation design method based on an elite retained genetic algorithm was developed in this study. Firstly, a database of 96 extended end-plate connections was established using the ABAQUS finite element software to obtain the initial rotational stiffness and yield moment under failure criteria. Secondly, with section size as the optimisation variable, failure mode as the constraint condition, and total steel consumption as the objective function, elite gene transfer was realized through gene sequence replication, hybridisation and mutation. With a 10-story, 3-span, semi-rigid steel frame as an example is proposed to demonstrate the application of the proposed semi-rigid design method, and pushover and time history analyses were conducted on the optimized results to verify the accuracy of the proposed method. The analysis results showed that the stable and reliable optimal design results were obtained through 1317 generation genetic iterative calculations, and the energy consumption ratio of the semi-rigid connection (including the beam ends) was 95.82% and that of the column was 4.18%; in addition, 76% of the beam ends had plastic hinges to dissipate energy in the pushover analysis. This finding indicates that the optimized frame can achieve the global failure mode during earthquakes, and the design method can effectively improve the seismic performance of the frame.

Production of animal feed from food waste or corn? Analyses of energy and exergy

The aim of the study was to investigate the energy and exergy consumption of the food waste dryer and then to compare the energy required to produce animal feed with the waste drying method and conventional methods. The required waste from the kitchens has been collected so that each collection period was considered as a block due to having different moisture content. The results showed that the amount of energy consumed increased along with increasing temperature to 29.23 kJ, also wasted exergy and the rate of recovery was significantly increased, but the efficiency of the device decreased significantly. Over time, energy consumption, energy consumption ratio, improvement potential rate have increased, but exergy efficiency reduced. Obtained results suggest that the conversion of food waste into animal feed requires 1.8 MJ per kg less energy. Finally, feed production from food waste is more affordable than agriculture because not only we need less energy than but also we don't spend money to get initial materials.

Efficient and sustainable short electric arc machining based on SKD-11 material

SKD11 has a wide range of applications in the tooling industry. However, due to its high mechanical strength and hardness, it is a difficult material to machine. To address the issues of low machining efficiency, severe tool electrode wear, and environmental pollution caused by traditional EDM, this paper proposes a more efficient and sustainable method of EDM production called short electric arc machining (SEAM). This paper discussed the mechanism of material removal by a short electric arc and then conducted experiments on SKD11 machining with various voltage parameters using a self-designed short electric arc milling machine, analysing the material removal rate (MRR), relative tool electrode wear ratio (REWR), and specific energy consumption (SEC) of the machined workpiece. The experimental results indicate that the maximum removal rate is 15,745 mm3/min, the REWR is significantly reduced to 1.4 %, and the energy consumption ratio is 76.84 KJ/cm3. Finally, the microscopic morphology and elemental composition of the workpiece and tool electrode were analyzed by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS), and the variation of cross-sectional grains was analyzed by electron backscatter diffraction (EBSD).It can be concluded that the short-arc milling method discussed in this paper can provide an efficient machining method for difficult-to-machine materials.

Simulation study on the injection performance of the gas-solid injectors for large particles

Gas-solid injectors are a power source of particles in pneumatic conveying systems. The momentum and energy exchange between particles and the flow field occurs for the first time in a gas-solid injector. The structural parameters of gas-solid injectors have a direct impact on the state of motion of particles. In the previous experimental study [1], we obtained the optimal structural parameters of a gas-solid injector under experimental conditions. However, there were many problems, such as the high cost of experimental equipment, high consumption of human and material resources, and long experimental time. In particular, the amount of reference data obtained from the experiment was limited, and the experimental results had certain manufacturing, human and accidental errors. Therefore, in this paper, the CFD-DEM coupling method is used to simulate the structural parameters affecting the performance of gas-solid injectors. The ejection ratio, energy consumption ratio, residue particle mass, particle outlet velocity, particle conveying ratio, percentage of total particle energy and total energy consumption ratio are adopted as the corresponding indicators to explore the relevant evaluation parameters that are difficult to obtain by experiment. The optimized structural parameters of the gas-solid injector under the corresponding indicators are obtained, which provides a design reference for the gas-solid injector and pneumatic conveying system under different working conditions. • Evaluation indicators obtained by CFD-DEM are difficult to obtain experimentally. • Optimal structure parameters are obtained under different evaluation indicators. • Nozzle length has the most significant effect on ejection performance. • Residual particle mass should be taken as primary indicator to avoid blockage. • Energy consumption should be taken as important indicator to improve efficiency.

Learning-Based Resource Management for Low-Power and Lossy IoT Networks

Internet of Things (IoT) networks are key to the realization of modern industries and societies. A key application of IoT is in smart-grid communications. Smart-grid networks are resource constrained in terms of computing power and energy capacity. Similarly, the wireless links between devices are typically associated with high packet-loss rates, low throughput, and instability. To provide a sustainable communication mechanism, an IoT network stack is proposed for these devices. However, each network stack layer has its own constraints. For example, to facilitate the operation of these low-power and lossy network (LLN) devices, the international engineering task force (IETF) standardized a network-layer protocol called a routing protocol for low-power and lossy networks (RPLs). RPL often creates an inefficient network in densely deployed and varying traffic load conditions. Future dense IoT-based networks are expected to automatically optimize the reliability and efficiency of communication by inferring the diverse features of both the environments and actions of the devices. Machine learning (ML) provides a promising framework for such a dense network environment. In this study, we examine the underlying perspective of ML for such systems. We utilize the multiarmed bandit (MAB)-based expected energy count (BEEX) technique, which provides nodes the ability to effectively optimize their operation. Using the proposed mechanism, nodes can intelligently adapt their network-layer behavior. The performance of the proposed (BEEX) algorithm is evaluated through a Contiki 3.0 Cooja simulation. The proposed method improves the energy consumption and packet delivery ratio and produces a lower control overhead than other state-of-the-art mechanisms.

Numerical investigation on particles separation using an enhanced cyclone with shunt device: Effect of vortex finder lengths

To improve the performance of the enhanced cyclone with shunt device (ECSD), the vortex finder lengths optimization for the ECSD is carried out by numerical investigation. Reynolds stress model and discrete phase model are adopted. The results show that the separation ability of the ECSD can be improved by longer vortex finder, which is contrary to the generally accepted conclusion that the vortex finder length of the traditional cyclone should not be longer than cylindrical section length. When the ratio of vortex finder length to cylindrical section length reaches 1.5 (225 mm vortex finder), the total separation efficiency, reduced separation efficiency and comprehensive separation efficiency are 90.7%, 89.7%, and 80.6%, respectively, which are the highest. The tangential velocity distribution is one of the most important reasons that the separation efficiency increases with the increase of the vortex finder length in general. The tangential velocity in the conical section of the ECSD increased with the increase of vortex finder length. When the length of vortex finder is the same as that of cylindrical section, the overall energy consumption and split ratio of the ECSD are the largest of 1.88 kPa and 12.4%, respectively, which is detrimental to the overall performance of ECSD.

Low-Power Low-Area Near-Lossless Image Compressor for Wireless Capsule Endoscopy

The paper presents the concept of a low-power, low-area, near-lossless image compressor for resource-constrained devices such as wireless capsule endoscopy (WCE). The compressor directly processes the raw data from the Bayer Color Filter Array (CFA) imager to avoid the high cost of color interpolation. To improve the efficiency of the compressor in terms of energy consumption, silicon area and compression ratio, the main part of the compressor, i.e., the entropy encoder, uses the existing correlations between the color components of a captured CFA image. The proposed image compressor requires only 12.4% of the memory needed by other high-quality CFA compressors based on the JPEG-LS standard. Despite this significant reduction in memory size, the proposed image compressor outperforms other state-of-the-art coding schemes on capsule endoscopy images. At the same time, it offers only slightly lower performance on standard test images. The proposed image compressor has been implemented as an intellectual property (IP) core using two different low-cost CMOS processes. The design, implemented in UMC 180 nm CMOS process, requires a very low silicon area (534 times 426 upmu m^{2}) and consumes very low energy (22 upmu J per a single 512 times 512 image frame). Even higher energy efficiency (12 upmu J per the same image frame) has the IP core implemented in the TSMC 130 nm CMOS process. Both of the selected technologies are low-cost and well-suited to implement a radio frequency transmitter and a low-power successive approximation register analog-to-digital converter in addition to the compressor to provide a cost-effective System on Chip for resource-constrained devices like WCE or wireless camera sensor network.

Application placement with shared monitoring points in multi-purpose IoT wireless sensor networks

The main function of a wireless sensor network (WSN) is to gather data from a certain region and transfer the data to a center or remote locations for further processing. The collected data can be of interest for many applications. Therefore, a physical WSN owned by a single provider can be utilized by many customer applications. Additionally, the data of a particular point or sub-region can satisfy the need of multiple applications. Hence, sensing the data only once in such cases is beneficial to reduce the energy consumption, network traffic and acceptance ratio of the applications. We call this as monitoring point based shared data approach. In this paper, we focus on the placement of applications each of which requires several points to be monitored in an area a WSN covers. We first propose such a monitoring point based shared data approach for WSNs that will serve multiple dynamic applications. We also propose two methods for application placement over a shared physical WSN: one greedy method and one genetic algorithm based method called GABAP. We did extensive simulation experiments to evaluate our algorithms. The results show the effectiveness of our methods in fast and close-to-optimum placement of applications over a single network.

A Robust, Scalable, and Energy-Efficient Routing Strategy for UWSN Using a Novel Vector-Based Forwarding Routing Protocol

The Underwater Wireless Sensor Network (UWSN) is capable of supporting a wide range of low-data-rate acoustic sensor networks, as well as scalability and energy-efficient routing algorithms. Furthermore, because of the lower bandwidth and longer propagation delays, energy consumption is a major concern with underwater networks. Because radio transmissions are unstable in deep water, a UWSN often communicates via acoustic channels. UWSN features create constraints on data packet transmission and energy-efficient routing. The hardest challenge is creating an effective routing protocol for UWSNs that uses sensor node localization. There have been several routing algorithms reported for identifying nodes via a localization process. This paper proposes a novel vector-based forwarding (NVBF) and efficient depth-based routing (DBR) protocol to provide robust, scalable, and energy-efficient routing. Efficient DBR is a stochastic model that can survive even in acoustic channels with substantial transmission loss. An adjacent node table is created to decrease the energy consumption and end-to-end delay by limiting the adjacent node request generated every single time. The redundant packet transmission is reduced by creating a packet queue which also minimizes the energy. The NVBF algorithm uses a random waiting time to overcome the collision that occurs while sending the acknowledgment packets. Based on the previous sender’s depth and the depth of the own node, the DBR makes packet transmission decisions. The experiments are conducted using the NS3 simulator and the efficiency of the proposed approach is evaluated in terms of propagation delay, network lifetime, energy consumption, and delivery ratio. When compared to the existing techniques such as DBR, VBF, and energy-efficient DBR (EDBR), the proposed methodology offers improvement up to 40% and 30% in terms of propagation delay and delivery ratio.

Energy evolution properties and strength failure criterion of coal-fired slag concrete based on energy dissipation

Coal-fired slag concrete is gradually used in underground projects such as coal mines. Due to the differences between concrete composites and rocks, the failure of coal-fired slag concrete cannot be predicted from the essence through the stress–strain curve. However, studying the deformation and failure law of concrete from the perspective of energy can be closer to the essence of concrete deformation and failure. Therefore, in this paper, with the help of RLJW-2000 testing machine, the uniaxial compression test of C15 strength grade coal-fired slag concrete is carried out, the energy evolution law in the process of deformation and failure of coal-fired slag concrete is analyzed, and the failure criterion based on energy consumption ratio is established. The results show that with the increase of slag-cement ratio, the total energy and elastic strain energy at the peak point gradually decrease. The evolution of peak energy density satisfies the exponential function relation with e as the base. The process that the elastic energy consumption ratio changes from the stable state tending to 0 to the beginning of growth, or the process that the elastic energy consumption ratio changes from negative value to 0, and then gradually increases to positive value can be used as the energy criterion for the strength failure of coal-fired slag concrete. The research results can essentially analyze the instability and failure of coal-fired slag concrete, which is of great significance to ensure the safety of coal-fired slag concrete in engineering application.

Direct reduction of copper slag using rubber seed oil as a reductant: Iron recycling and thermokinetics

Copper slag has potential economic and environmental benefits and there is a need for further clean utilization. The traditional recovery methods have the disadvantages of high energy consumption and low Fe recovery ratio. A strategy is proposed for the clean and efficient recovery of Fe from copper slag by deep reduction using rubber seed oil as a reductant. The reduction experiments and thermokinetics analysis are conducted to explore the reaction behavior between rubber seed oil and copper slag. The results show that the Fe recovery ratio of the copper slag is 91.85% and the Fe grade is 95.66% under the optimal conditions for roasting reduction. The process of recycling Fe from copper slag produces new secondary waste residue. Secondary waste residue can be used as a partial starting material for the synthesis of glass-ceramics. The reduction reaction mechanism of copper slag is transformed with increasing temperature. Limiting surface reaction (n = 2) is the restrictive step for the direct reduction reaction region and one-dimension diffusion is the restrictive step for the smelting reduction reaction region. The activation energy of the direct reduction reaction region and smelting reduction reaction region are 16.64 kJ/mol and 13.68 kJ/mol, respectively. This research provides an important approach to the clean utilization of copper slag.

Analysis of energy consumption in poultry management for table egg production in Nigeria

Energy audit and mass flow studies of commercial agricultural systems are increasingly becoming of utmost importance, due to high operation costs and dependence on energy. This research was designed to study energy input, output and efficiency for daily table egg production from commercially managed poultry birds. Three commercially operated poultry farms in Ibadan, Nigeria were visited for assessment of management procedures, data collection, equipment observation and personnel interview. The energy required for each management procedure was calculated from standard methods. Each farm housed average of 25,000 actively laying birds and had average daily egg production of 21,250 egg pieces. This amounted to 1169 kg egg and 3000 kg faecal materials production per day from the average energy input of 122,461.12 MJ/day. The highest energy consumption was biological energy which resulted from daily feed consumption of 3000 kg at the rate of 120 g per bird per day. This made up 83.81% of the total energy consumed. These resulted in an energy consumption ratio of 1.05, energy productivity of 0.034 kg/MJ, specific energy of 29.29 MJ/kg and net energy of 6,569.09 MJ/day, respectively. Faecal materials constituted the bulk of the output from the system. Making use of the faecal material in its treated form for the production of feed components would reduce energy costs, increase farmers’ net income and also encourage environmentally efficient processes.

Privacy preserving monitoring protocol for Cyber–Physical System

Cyber–Physical Systems integrate computational and physical systems that can interact with humans in various ways. Due to less research on location privacy, building a monitoring protocol for the CPS system is challenging. Location privacy is becoming a major issue in the CPS, where the actual source and its messages are kept avoided by an attacker. The attacker follows the incoming message’s route to reach the source’s location. To hide the location of the source from attackers and increase their safety period, this research proposes a Multi-Ring protocol. A complete theoretical and mathematical analysis is discussed to check the model’s complexity and performance. The message delivery delay, throughput, energy consumption, and safety time ratio metrics are used for performance comparison. The simulation results confirmed that the proposed protocol increases the source safety period and network lifetime compared to the existing protocols.

Energy optimization using swarm intelligence for IoT-Authorized underwater wireless sensor networks

Several data forwarding and clustering techniques have been proposed to improve Underwater Wireless Sensor Networks (UWSNs) performance, but void communications, packet collisions, and energy efficiency remain unresolved. We introduce a novel routing solution for energy and QoS-efficient data transmission from the underwater sensor node to the surface sink using Swarm Intelligence (SI). The protocol Energy Optimization using the Routing Optimization (EORO) is proposed to overcome the existing challenges. We design Effective Fitness Function-based Particle Swarm Optimization (EFF-PSO) to choose the optimal forwarder node for UWSN data delivery. In EORO, forwarding relay nodes are discovered by the intended source node using location information first. Then EFF-PSO algorithm is applied to select the optimal relay node considering the rich set of parameters. Four parameters of each forwarder node are used for fitness computation as residual energy, packet transmission ability, node connectivity, and distance. These parameters are deliberately chosen to reduce energy consumption, latency, and throughput by avoiding packet collisions. EORO surpassed underlying routing solutions in throughput, energy consumption, latency, and Packet Delivery Ratio (PDR).

Strength and Energy Evolution Law of Deep-Buried Granite Under Triaxial Conditions

With the increasing global demand for clean and renewable energy sources, many underground hydropower caverns are built in deep mountain valleys in high-stress regions. The evolution of the mechanical properties of the surrounding rock of underground caverns under high-stress excavation requires urgent investigation. According to the deep-buried granite in the underground caverns of the Shuangjiangkou hydropower station, triaxial tests under confining pressures of 10, 30, 40, and 50 MPa were conducted by the MTS815 rock mechanics test system. Based on the stress–strain curve, the evolution law of the strength parameters of rock samples with the crack volume strain and energy with the energy consumption ratio under different confining pressures was analyzed. Our results showed that the stress–strain curve of the sample is divided into five stages with four characteristic points: the closed point, initiation point, volume expansion point, and peak point. The strength of each stage increases with an increase in the confining pressure. In addition, the failure of this granite is characterized by apparent shear failure. The internal friction angle and the cohesion increase rapidly with the increase in the crack volume strain, and they gradually tend to be constant. Furthermore, the confining pressure profoundly influences energy evolution during the loading in the stable and unstable crack growth stages. In these stages, total energy, dissipated energy, and elastic strain energy increase with an increase in the confining pressure. Finally, the energy consumption ratio can represent the preliminary criterion of rock failure in terms of energy. With the increase in the confining pressure, the energy consumption ratio of rock samples gradually increases to approximately 1.0 at the peak stress point. The research results can provide a reference for the instability prediction of surrounding rock masses of high-stress underground caverns.

Designing Deep Learning Hardware Accelerator and Efficiency Evaluation.

With the swift development of deep learning applications, the convolutional neural network (CNN) has brought a tremendous challenge to traditional processors to fulfil computing requirements. It is urgent to embrace new strategies to improve efficiency and diminish energy consumption. Currently, diverse accelerator strategies for CNN computation based on the field-programmable gate array (FPGA) platform have been gradually explored because they have edges of high parallelism, low power consumption, and better programmability. This paper first illustrates state-of-the-art FPGA-based accelerator design by emphasizing the contributions and limitations of existing research works. Subsequently, we demonstrated significant concepts of parallel computing (PC) in the convolution algorithm and discussed how to accomplish parallelism based on the FPGA hardware structure. Eventually, with the proposed CPU+ FPGA framework, we performed experiments and compared the performance against traditional computation strategies in terms of the operation efficiency and energy consumption ratio. The results revealed that the efficiency of the FPGA platform is much higher than that of the central processing unit and graphics processing unit.

Investigation of a low-pressure flash evaporation desalination system powered by ocean thermal energy

To alleviate the consequences of the freshwater crisis and fully utilize marine resources in tropical coastal areas, this study presents a low-pressure flash evaporation desalination system driven by ocean thermal energy (OTE). Through gravity and atmospheric pressure, a natural vacuum can be formed in the evaporator, which achieves flash evaporation for warm surface seawater. Then, the vapour is condensed into freshwater via an inner coil condenser with flowing cold deep seawater. Using a water pump to fill the evaporator intermittently, the non-condensable gas accumulated in the desalination process can be thoroughly discharged. Based on the process of mass and heat transfer inside the system, a thermodynamic model was developed. Several performance evaluation indices, including water productivity, specific electrical energy consumption (SEEC) and recovery ratio (RR), were experimentally investigated under different parameters. The results indicate that productivity and SEEC are higher with increasing seawater flow rate and decreasing deep seawater temperature. Under a warm seawater temperature of 30 °C and a deep cold seawater temperature of 8 °C, the system obtained a maximum water productivity of 5.3 kg/h, and the corresponding SEEC and RR were 0.126 kWh/kg and 1.5%, respectively. Finally, compared with solar-driven desalination systems, the proposed OTE desalination system requires less energy consumption, which shows an attractive application prospect.

Research on Privacy Protection Based on Joint Learning in Power Industry Big Data Analysis

In the era of big data, protecting the privacy of smart grid data is critical in ensuring the integrity and confidentiality of that data. Utilizing large amounts of energy data to gain insight into electricity consumers’ consumption patterns helps develop power supply strategies. This article presents a Big Data-assisted Joint learning process (BDA-JLP), taking data security issues posed by big data in the electric power industry into consideration for privacy protection using K-anonymity and L-diversity as a foundation. A blockchain with JLP electric utility investigation is being conducted, part of the existing trading model split into phases. To begin, an attribute is chosen to categorize the input database. The comparable class number K and sensitive attribute value category L are limited by the number of original predecessors in the source data table, simplifying the calculation. A mathematical equation is then developed to determine the distance between first cousins multiplied by their combined weight. Linear and clustering with binary K are used to categorize data tables. Cluster and generalize initial data sets, considering how the attribute values’ internal range changes. The asymmetric encryption method uses two distinct keys for encryption and decryption ensuring that the blockchain system is completely secure. Simulated data show that the BDA-JLP mechanism proposed here has a privacy ratio of 98.3 percent, scalability of 97.0%, improved data management and data protection ratio of 98.2 percent, customer satisfaction ratio of 98.4 percent, and a low energy consumption ratio of 23.9% when compared to other methods currently available.