Inductive QoS Packet Scheduling for Adaptive Dynamic Networks

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. To solve this problem we use a multi-agent system in which each agent tries to optimize its own behaviour and communicate with other agents to make global coordination possible. This communication is done by mobile agents. In this paper, we adopt the framework of Markov Decision Processes applied to multi-agent system and present a pheromone-Q learning approach which combines the standard Q-learning technique with a synthetic pheromone that acts as a communication medium speeding up the learning process of cooperating agents.