QoS control scheme that uses back pressure traffic control for wireless LANs

2012 ◽  
Author(s):  
Fusao Nuno ◽  
Takatoshi Sugiyama ◽  
Masahiro Morikura
Keyword(s):  
Traffic Control ◽  
Wireless Lans ◽  
Back Pressure ◽  
Qos Control ◽  
Control Scheme

Utility-Based Distributed Association Control Scheme with User Guidance for IEEE802.11 Wireless LANs

2015 ◽  
Vol E98.B (8) ◽  
pp. 1700-1714 ◽  
Author(s):  
Takahiro IWAMI ◽  
Irda ROSLAN ◽  
Yumi TAKAKI ◽  
Kyoko YAMORI ◽  
Chikara OHTA ◽  
...  
Keyword(s):  
Wireless Lans ◽  
Control Scheme ◽  
User Guidance ◽  
Distributed Association

QRP05-5: Performance Study on DERACS: A Traffic Control Scheme for Video Multicast

2006 ◽  
Author(s):  
Jun Nishioka ◽  
Yoichiro Miyake ◽  
Hidenori Nakazato ◽  
Hideyoshi Tominaga
Keyword(s):  
Traffic Control ◽  
Performance Study ◽  
Video Multicast ◽  
Control Scheme

Optimal Signal Timing of Single Intersection for Traffic Emission Control

2014 ◽  
Vol 587-589 ◽  
pp. 2137-2140
Author(s):  
Xin Li ◽  
Feng Chen
Keyword(s):  
Reinforcement Learning ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Emission Control ◽  
Simulation Software ◽  
Atmospheric Environment ◽  
Traffic Signal Control ◽  
Traffic Emission ◽  
Average Delay ◽  
Control Scheme

Traffic emission is one of the main pollution sources of urban atmospheric environment. Traffic control scheme of intersection has important influence on vehicle emission. Research on low emission traffic signal control scheme has become one of focuses of Intelligent Transportation. Current typical control methods of traffic emission are based on optimizing the average delay and number of stops. However, it is extremely difficult to use mathematical formula to calculate the delay and the number of stops in the presence of initial queue length of intersection. In order to solve this problem, we proposed a traffic emission control algorithm based on reinforcement learning. The simulation experiments were carried out by using the microscopic traffic simulation software. Compared with the Hideki emission control scheme, the experimental results show that the reinforcement learning algorithm is more effective. The average vehicle emissions are reduced by 12.2% for high saturation of the intersection.

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