Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
