An Approach of Minimizing Energy Consumption in the Machining System Using Job Sequences Varying Technique

2019 ◽  
pp. 395-405
Author(s):  
Shahzar Jawaid ◽  
S. C. Srivastava ◽  
S. Datta
Keyword(s):  
Energy Consumption ◽  
Machining System

scholarly journals Energy Consumption Modelling of the Machining System Based on Petri Net

2014 ◽  
Vol 6 ◽  
pp. 324819 ◽  
Author(s):  
Qiulian Wang ◽  
Xianglian Wang ◽  
Suihong Yang
Keyword(s):  
Energy Consumption ◽  
Petri Net ◽  
Machining System

A method for minimizing the energy consumption of machining system: integration of process planning and scheduling

2016 ◽  
Vol 137 ◽  
pp. 1647-1662 ◽  
Author(s):  
Zhongwei Zhang ◽  
Renzhong Tang ◽  
Tao Peng ◽  
Liyan Tao ◽  
Shun Jia
Keyword(s):  
Energy Consumption ◽  
Process Planning ◽  
System Integration ◽  
Planning And Scheduling ◽  
Machining System

Lay Perceptions of Energy Consumption

2009 ◽  
Author(s):  
Shahzeen Z. Attari ◽  
Michael L. DeKay ◽  
Cliff I. Davidson ◽  
Wandi Bruine de Bruin
Keyword(s):  
Energy Consumption ◽  
Lay Perceptions

Study on Energy Consumption by Land Cargo Transportation

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Shunquan Huang ◽  
Siqin Yu ◽  
Zhongmin Liu
Keyword(s):  
Energy Consumption ◽  
Cargo Transportation

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

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