Application of reinforcement learning to medium access control for wireless sensor networks

This paper presents a novel approach to medium access control for single-hop wireless sensor networks. The ALOHA-Q protocol applies Q-Learning to frame based ALOHA as an intelligent slot selection strategy capable of migrating from random access to perfect scheduling. Results show that ALOHA-Q significantly outperforms Slotted ALOHA in terms of energy-efficiency, delay and throughput. It achieves comparable performance to S-MAC and Z-MAC with much lower complexity and overheads. A Markov model is developed to estimate the convergence time of its simple learning process and to validate the simulation results.