scholarly journals Survey on Energy Efficient Cloud: A Novel Approach towards Green Computing

Helix ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 3976-3979 ◽  
Author(s):  
Anup Gade
Keyword(s):  
Energy Efficient ◽  
Green Computing ◽  
Novel Approach

An Energy Efficient Routing Approach to Enhance Coverage for Application Specific Wireless Sensor Networks using Genetic Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur Sohal ◽  
Ajay Kumar Sharma ◽  
Neetu Sood
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Cluster Formation ◽  
Green Computing ◽  
Wireless Sensor ◽  
Energy Efficient Routing

Background: An information gathering is a typical and important task in agriculture monitoring and military surveillance. In these applications, minimization of energy consumption and maximization of network lifetime have prime importance for green computing. As wireless sensor networks comprise of a large number of sensors with limited battery power and deployed at remote geographical locations for monitoring physical events, therefore it is imperative to have minimum consumption of energy during network coverage. The WSNs help in accurate monitoring of remote environment by collecting data intelligently from the individual sensors. Objective: The paper is motivated from green computing aspect of wireless sensor network and an Energy-efficient Weight-based Coverage Enhancing protocol using Genetic Algorithm (WCEGA) is presented. The WCEGA is designed to achieve continuously monitoring of remote areas for a longer time with least power consumption. Method: The cluster-based algorithm consists two phases: cluster formation and data transmission. In cluster formation, selection of cluster heads and cluster members areas based on energy and coverage efficient parameters. The governing parameters are residual energy, overlapping degree, node density and neighbor’s degree. The data transmission between CHs and sink is based on well-known evolution search algorithm i.e. Genetic Algorithm. Conclusion: The results of WCEGA are compared with other established protocols and shows significant improvement of full coverage and lifetime approximately 40% and 45% respectively.

Analysis of Fast Bipedal Walking Using Mechanism of Actively Controlled Wobbling Mass

2019 ◽  
Vol 31 (6) ◽  
pp. 871-881
Author(s):  
Yuta Hanazawa ◽  
Terumitsu Hayashi ◽  
Masaki Yamakita ◽  
Fumihiko Asano ◽  
◽  
...  
Keyword(s):  
Limit Cycle ◽  
Energy Efficient ◽  
Walking Speed ◽  
Control Method ◽  
Bipedal Walking ◽  
Bipedal Robots ◽  
Limit Cycle Walking ◽  
Novel Approach ◽  
Wobbling Mass

In this study, a novel approach was developed to achieve fast bipedal walking by using an actively controlled wobbling mass. Bipedal robots capable of achieving energy efficient limit cycle walking have been developed, and researchers have studied methods to increase their walking speed. When humans walk, their arm swinging is coordinated with the walking phases, generating a regular symmetrical motion about the torso. The bipedal robots with a wobbling mass in the torso mimicked the arm swinging by the proposed control method. We demonstrated that the proposed method is capable of increasing the bipedal walking speed.

A novel approach for making energy efficient PON

2011 ◽  
Vol 41 (4) ◽  
pp. 428-429 ◽  
Author(s):  
S.H. Shah Newaz ◽  
Ángel Cuevas ◽  
Gyu Myoung Lee ◽  
Noël Crespi ◽  
JunKyun Choi
Keyword(s):  
Energy Efficient ◽  
Novel Approach

scholarly journals Energy Efficiency Analysis Between Green Computing and Cloud Computing

2019 ◽  
Vol 8 (3S2) ◽  
pp. 254-258
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Data Center ◽  
Energy Efficient ◽  
Green Computing ◽  
Energy Efficient Computing ◽  
Green Data Center ◽  
Computer Resources ◽  
Server System ◽  
Energy Efficiency Analysis

Usually cloud computing is based under effective data computing but green cloud computing focus on energy efficiency of device and computing mainly based on computing architecture. Here cloud computing is also known as green computing due its better effective energy. The purpose of green computing is to decrease the usage of harmful resources, maximizing energy efficiency throughout the living span of the good and also to promote the recyclability or bio degradability of useless goods plus misuse materials of the factory. In order to simulate the hardware by utilizing software, green computing plays a major role for various technologies like virtualization, green data center, cloud and grid computing and power optimization. Here computer resources are effectively utilized by replacing the data center standalone server system by artificial server so as to run the software by less number for large computers called as virtualized. Finally this paper is fulfilled by the advancement of recognized energy efficient computing

scholarly journals A Review on Secure and Energy Efficient Approaches for Green Computing

2016 ◽  
Vol 138 (11) ◽  
pp. 25-32 ◽  
Author(s):  
Shivangi Sharma ◽  
Gaurav Sharma
Keyword(s):  
Energy Efficient ◽  
Green Computing

scholarly journals Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning

2019 ◽  
Author(s):  
Zhenshan Bing ◽  
Christian Lemke ◽  
Zhuangyi Jiang ◽  
Kai Huang ◽  
Alois Knoll
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Degrees Of Freedom ◽  
Bayesian Optimization ◽  
Control Task ◽  
Flexible Bodies ◽  
Model Free ◽  
Novel Approach ◽  
Wide Range ◽  
Policy Optimization

Similar to their counterparts in nature, the flexible bodies of snake-like robots enhance their movement capability and adaptability in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently. In this work, we present a novel approach for designing an energy-efficient slithering gait for a snake-like robot using a model-free reinforcement learning (RL) algorithm. Specifically, we present an RL-based controller for generating locomotion gaits at a wide range of velocities, which is trained using the proximal policy optimization (PPO) algorithm. Meanwhile, a traditional parameterized gait controller is presented and the parameter sets are optimized using the grid search and Bayesian optimization algorithms for the purposes of reasonable comparisons. Based on the analysis of the simulation results, we demonstrate that this RL-based controller exhibits very natural and adaptive movements, which are also substantially more energy-efficient than the gaits generated by the parameterized controller. Videos are shown at https://videoviewsite.wixsite.com/rlsnake .

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