Scheduling Method to Improve Energy Consumption in WSN

2022 ◽  
pp. 523-537
Author(s):  
J. Poornimha ◽  
A. V. Senthil Kumar ◽  
Ismail Bin Musirin
Keyword(s):  
Energy Consumption ◽  
Scheduling Method

Improving Energy-Efficiency of Scientific Computing Clusters

2012 ◽  
pp. 1916-1933
Author(s):  
Tapio Niemi ◽  
Jukka Kommeri ◽  
Ari-Pekka Hameri
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Operations Management ◽  
Scientific Computing ◽  
High Energy Physics ◽  
High Energy ◽  
Test Results ◽  
Processor Core ◽  
Processing Times ◽  
Scheduling Method

The authors applied operations management principles on scheduling and allocation to scientific computing clusters to decrease energy consumption and to increase throughput. They challenged the traditional one job per one processor core scheduling method commonly used in scientific computing with parallel processing and bottleneck management. The authors tested the effect of increased parallelism by using different test applications related to high-energy physics computing. The test results showed that at best their methods both decreased energy consumption down to 40% and increased throughput up to 100%, compared to the standard one task per CPU core method. The trade-off is that processing times of individual tasks get longer, but in scientific computing, the overall throughput and energy-efficiency are often more important.

An Energy Saving Scheduling Method for Just in Time Material Handling in Mixed-Model Assembly Line

2017 ◽  
Author(s):  
Liman Hu ◽  
Binghai Zhou ◽  
Yang Li
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Material Handling ◽  
Assembly Line ◽  
Mixed Model ◽  
Solution Procedure ◽  
Mixed Integer ◽  
Scheduling Method ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Driven by the green logistics, automated guided vehicle (AGV) has been widely accepted as a new transportation tool for in-house logistics, which enables a timely supply of parts to the designated workstations with less energy consumption. However, the existing scheduling methods for AGV scheduling are designed to minimize inventory or cost without explicitly considering energy saving. To fill the gap, this paper proposes an AGV scheduling model for energy saving in a mixed-model assembly line, where AGVs can have variable travel speeds. A mixed-integer model is constructed and an exact solution procedure is provided. Simulation studies are performed to investigate the main factors that determine the energy consumption and to demonstrate the effectiveness of the proposed method.

scholarly journals Joint Optimization of Energy Conservation and Migration Cost for Complex Systems in Edge Computing

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xiaolong Xu ◽  
Yuan Xue ◽  
Mengmeng Cui ◽  
Yuan Yuan ◽  
Lianyong Qi
Keyword(s):  
Energy Consumption ◽  
Complex Systems ◽  
Optimization Problem ◽  
Renewable Energy Sources ◽  
Resource Scheduling ◽  
Multi Objective Optimization ◽  
Vm Migration ◽  
Multi Objective ◽  
Migration Cost ◽  
Scheduling Method

By means of the complex systems, multiple renewable energy sources are integrated to provide energy supply for users. Considering that there are massive services needed to process in complex systems, the mobile services are offloaded from mobile devices to edge servers for efficient implementation. In spite of the benefits of complex systems and edge servers, massive resource requirements for implementing the increasing resource requests decrease the execution efficiency and affect the whole resource usage of edge servers. Therefore, it remains an issue to achieve dynamic scheduling of the computing resources across edge servers. With the consideration of this issue, a Balanced Resource Scheduling Method, named BRSM, for trade-offs between virtual machine (VM) migration cost and energy consumption of VM migrations for edge server management, named BRSM, is designed in this paper. Technically, we analyze the load conditions of edge servers and formulate the energy consumption of VM migrations and VM migration cost as a multi-objective optimization problem. Then, we propose a dynamic resource scheduling method for WMAN to deal with the multi-objective optimization problem. In addition, nondominated sorting genetic algorithm III (NSGA-III) is adopted to generate optimal resource scheduling strategies. Finally, we conduct experiment simulations to testify the efficiency of the proposed method BRSM.

Improving Energy-Efficiency of Scientific Computing Clusters

2013 ◽  
pp. 1-19 ◽  
Author(s):  
Tapio Niemi ◽  
Jukka Kommeri ◽  
Ari-Pekka Hameri
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Operations Management ◽  
Scientific Computing ◽  
High Energy Physics ◽  
High Energy ◽  
Test Results ◽  
Processor Core ◽  
Processing Times ◽  
Scheduling Method

The authors applied operations management principles on scheduling and allocation to scientific computing clusters to decrease energy consumption and to increase throughput. They challenged the traditional one job per one processor core scheduling method commonly used in scientific computing with parallel processing and bottleneck management. The authors tested the effect of increased parallelism by using different test applications related to high-energy physics computing. The test results showed that at best their methods both decreased energy consumption down to 40% and increased throughput up to 100%, compared to the standard one task per CPU core method. The trade-off is that processing times of individual tasks get longer, but in scientific computing, the overall throughput and energy-efficiency are often more important.

scholarly journals High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772094341
Author(s):  
Yajun Zhang ◽  
Gang Qiu ◽  
Meng Liu ◽  
Hongjun Wang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Generation Time ◽  
High Density ◽  
Wireless Sensor ◽  
Communication Model ◽  
Density Data ◽  
Energy Consumption Model ◽  
Scheduling Method

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

scholarly journals Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jordi Serra ◽  
David Pubill ◽  
Angelos Antonopoulos ◽  
Christos Verikoukis
Keyword(s):  
Energy Consumption ◽  
Smart Grids ◽  
Hvac Systems ◽  
Energy Price ◽  
Time Interval ◽  
Consumption Cost ◽  
Scheduling Method ◽  
Hvac Control ◽  
The Internet Of Things ◽  
Variable Energy

Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption management of heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user’s preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user’s device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost.

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