Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

Thinking of Comprehensive Energy-Saving System Construction for Urban Rail Transit

2014 ◽  
Vol 505-506 ◽  
pp. 405-409
Author(s):  
Jun Ke Liang ◽  
Zhi Gang Liu ◽  
Yuan Chun Huang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Design ◽  
High Efficiency ◽  
High Energy ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
System Construction ◽  
Planning Period ◽  
Urban Rail

The High Energy Consumption of the Current Urban Rail Transit Industry, High Efficiency Energy Saving Measures must be Taken. this Paper Entity from the Traction Energy Consumption, Building Structure and Operating Equipment Aspects of the Current Situation, Described the Energy Saving Strategies. Aiming at the Present Problems Existing in Energy Saving Practice, this Article Puts Forward the Comprehensive Energy Saving System which Contains Optimization Design in Planning Period, Low Resource Consumption in Construction Period, Energy Saving Work in Operation Period. above all, Implement Energy Saving Practice at Every Concrete Work of Reaching.

scholarly journals Analysis of the Indoor Thermal Environment and Passive Energy-Saving Optimization Design of Rural Dwellings in Zhalantun, Inner Mongolia, China

2020 ◽  
Vol 12 (3) ◽  
pp. 1103 ◽  
Author(s):  
Teng Shao ◽  
Wuxing Zheng ◽  
Hong Jin
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Optimization Design ◽  
Optimal Parameter ◽  
Economic Benefits ◽  
Cold Climate ◽  
Design Parameters ◽  
Orthogonal Experiments ◽  
Mechanical Methods ◽  
Design Factors

Zhalantun city is located in a severely cold region of China. The cold climate and long winter bring challenges to the energy-saving design of rural dwellings in this area, while the poor economic conditions restrict the application of energy-saving technology. This paper aims to propose an optimal combination of passive design parameters by investigating, testing, and analyzing simulations of Zhalantun rural dwellings, which have a particular architectural pattern. Field measurements during winter show that the indoor temperature of a traditional house is low and fluctuates greatly, and the inner surface is prone to easy condensation. Through thermal comfort surveys, neutral and acceptable temperature ranges were obtained to provide indoor calculation parameters for an energy-saving design. Numerical simulations of heating energy consumption were conducted on the typical building models using DesignBuilder. The influence of different design factors on energy consumption was evaluated. Orthogonal experiments were designed to optimize a series of design parameter combinations to reduce the energy consumption of Zhalantun rural houses and to determine the sequence and significance of the effect of these design factors on energy consumption. Results show that the optimal parameter combination based on orthogonal experiments can obviously reduce energy consumption and have better economic benefits without considering mechanical methods. This can provide a basis for improved energy-saving designs and indoor thermal environments in such rural dwellings.

Acceleration Components in Heterogeneous Supercomputers

2011 ◽  
Vol 204-210 ◽  
pp. 765-768
Author(s):  
Xian Min Wei
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Low Power ◽  
Control Unit ◽  
General Purpose ◽  
Low Power Consumption ◽  
Computing Power ◽  
General Purpose Processor ◽  
Computing Performance ◽  
Good Expansion

General-purpose processors continue to improve computing performance, but its energy consumption has far exceeded the increase in the proportion of performance. Specifically designed for the special purpose processor in the case of relatively low power consumption, can provide better performance. The general-purpose processor as the main control unit, special purpose processors as accelerators consisting of heterogeneous supercomputers will become a trend in supercomputer development. This paper introduces and analyzes several heterogeneous supercomputers used to build the acceleration components.While accelerate parts based on such components have obvious calculation advantages (high computing power, low power consumption), but with less application and difficult programming bottlenecks still exist. After all, the application number is limited which using hundreds, thousands or even more parallel computing for good expansion performance, and the workload of migration, development and optimization remains high.

scholarly journals Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 404-408
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Piecewise Linear Approximation ◽  
Compression Algorithms

Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption

scholarly journals A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877692
Author(s):  
Shaoqiang Liu ◽  
Yanfang Liu ◽  
Xiang Chen ◽  
Xiaoping Fan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Data Communication ◽  
Wireless Sensor ◽  
Compression Algorithms ◽  
Wireless Module

Data communication incurs the highest energy cost in wireless sensor networks, and restricts the application of wireless sensor networks. Data compression is a promising technique that can reduce the amount of data exchanged between nodes and results in energy saving. However, there is a lack of effective methods to evaluate the efficiency of data compression algorithms and to increase nodes’ energy efficiency. The energy saving of nodes is related to both hardware and software, this article proposes a new scheme for evaluating energy efficiency of data compression in wireless sensor networks according to the node’s hardware and software. The relationship between the energy efficiency and the hardware and software factors is expressed by a formula. In this formula, energy efficiency can be improved by increasing the compression ratio and decreasing the ratio of s/ k, in which k represents the node’s hardware factor related to energy consumption of processor, wireless module, and so on and s represents the software factor that reflects the energy consumption of the algorithm. Based on the scheme, a mechanism is proposed to improve the node’s energy efficiency by selecting effective algorithms in accordance with the node’s radio frequency power. The feasibility of the scheme is demonstrated with lossless data compression algorithms on the MSP430F2618 processor.

scholarly journals Data Uploading Strategy for Underwater Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5265 ◽  
Author(s):  
Huang ◽  
Sun ◽  
Yang
Keyword(s):  
Decision Making ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Wireless Sensor ◽  
High Similarity ◽  
Underwater Wireless Sensor Networks ◽  
Data Upload

Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.

The Research of Measuring Approach and Energy Efficiency for Hadoop Periodic Jobs

2015 ◽  
Vol 8 (1) ◽  
pp. 206-210 ◽  
Author(s):  
Yu Junyang ◽  
Hu Zhigang ◽  
Han Yuanyuan
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Estimate ◽  
Cloud Platform ◽  
Periodic Tasks ◽  
Current Consumption ◽  
Reduce Energy Consumption

Current consumption of cloud computing has attracted more and more attention of scholars. The research on Hadoop as a cloud platform and its energy consumption has also received considerable attention from scholars. This paper presents a method to measure the energy consumption of jobs that run on Hadoop, and this method is used to measure the effectiveness of the implementation of periodic tasks on the platform of Hadoop. Combining with the current mainstream of energy estimate formula to conduct further analysis, this paper has reached a conclusion as how to reduce energy consumption of Hadoop by adjusting the split size or using appropriate size of workers (servers). Finally, experiments show the effectiveness of these methods as being energy-saving strategies and verify the feasibility of the methods for the measurement of periodic tasks at the same time.

scholarly journals Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 81
Author(s):  
Rongjiang Ma ◽  
Shen Yang ◽  
Xianlin Wang ◽  
Xi-Cheng Wang ◽  
Ming Shan ◽  
...  
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Energy Saving ◽  
Air Conditioning ◽  
Storage System ◽  
Operation Mode ◽  
Systematic Method ◽  
Potential Analysis ◽  
Energy Saving Potential ◽  
Air Conditioning Systems

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

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