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

作者：Chu Yi^*; Kosunalp Selahattin; Mitchell Paul D; Grace David; Clarke Tim

来源：Engineering Applications of Artificial Intelligence, 2015, 46: 23-32.

DOI：10.1016/j.engappai.2015.08.004

• Reinforcement learning
• Wireless sensor networks
• Medium access control

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-Qsignificantly 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.

• 出版日期2015-11