摘要：Wireless Sensor Network (WSN) for environment monitoring, which typically has heavy data transmission load, has critical real-time requirement. Thus the time delay at relay-nodes should be reduced. This paper models queue scheduling as a reinforcement learning process and presents a scheduling algorithm of relay-node based on self-adaptive weighted learning. The presented algorithm schedules queues dynamically. Simulation results under two circumstances (with sufficient bandwidth and limited bandwidth) show that the algorithm can improve the real-time performance and maintain fairness.
