Differentiated Service Based on Reinforcement Learning in Wireless Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


In this paper, we propose a global quality of service management applied to DiffServ environments and IEEE 802.11e wireless networks. Especially, we evaluate how the IEEE 802.11e standard for Quality of Service in Wireless Local Area networks (WLANs) can interoperate with the Differentiated Services (DiffServ) architecture for end-to-end IP QoS. An Architecture for the integration of traffic conditioner is then proposed to manage the resources availability and regulate traffic in congestion situation. This traffic conditioner is modelled as an agent based on reinforcement learning.


Wireless networks IEEE 802.11e DiffServ end-to-end QoS Traffic conditioner Reinforcement learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of Es-SeniaOranAlgeria
  2. 2.Industrial Computing and Networking Laboratory (LRIIR)ParisFrance

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