Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
HiQ: A Hierarchical Q-Learning Algorithm to Solve the Reader Collision Problem
SAINT-W '06 Proceedings of the International Symposium on Applications on Internet Workshops
A dynamic channel assignment policy through Q-learning
IEEE Transactions on Neural Networks
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In dense radio frequency identification (RFID) system, reader collision problem is bottleneck of overall system peiformance. In this paper we propose a distributed multi-channel reader anti-collision MAC (ACMAC) protocol to mitigate the reader collision problem. The proposed probability based channel selection approach helps selecting channel efficiently and reduces the waiting time and beaconing method solves the hidden and exposed node problem. Also, channel utilization probability based random backoff mitigates the collision possibility in the control channel. Simulation results show our protocol is energy efficient and gives higher network throughputs than the existing MAC layer protocol in RFID.