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In this paper we propose a scheduling scheme in wireless networks based on energy-efficient retransmission strategy. In our approach, the scheduling algorithm traits specially the neighboring nodes of source which get involved in the retransmission, in order to reduce the consumed energy. We use the Markov Decision Process frameworks to model this problem.
This paper describes an approach which uses a machine learning model in a packet scheduling problem to improve the end-to-end delay. A good Packet scheduling discipline allows to achieve QoS differentiation and to optimize the queuing delay. In a dynamically changing environment this discipline should be also adaptive to the new traffic conditions. We model this problem as a multi-agent system which...
This paper deals with the problem of packet scheduling in routers. The scheduling envisaged allows to achieve QoS differentiation and to reduce the queuing delay in order to optimize the end to end delay. We model this problem as a multi-agent system which consists of a whole of autonomous and independently learning agents, where each agent learns through continual interaction with the environment...
The packet scheduling in routing base station plays an important role in the sense to achieve QoS differentiation and to optimize the queuing delay, in particular when this optimization is accomplished on all base stations of a path between source and destination. In a dynamically wireless changing environment, a good scheduling discipline should be also adaptive to the dynamic traffic conditions...
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