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Open AccessArticle

CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks

1
School of Computer Scienece, Semyung University, Chungbuk 27136, Korea
2
Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Chungbuk 28644, Korea
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 143; https://doi.org/10.3390/electronics9010143
Received: 30 November 2019 / Revised: 8 January 2020 / Accepted: 9 January 2020 / Published: 11 January 2020
Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols to increase system performance. A collision occurs in wireless channel when two or more nodes try to access the channel at the same time. In this paper, a reinforcement learning-based MAC protocol was proposed to provide high throughput and alleviate the collision problem. A collaboratively predicted Q-value was proposed for nodes to update their value functions by using communications trial information of other nodes. Our proposed protocol was confirmed by intensive system level simulations that it can reduce convergence time in 34.1% compared to the conventional Q-learning-based MAC protocol. View Full-Text
Keywords: MAC protocol; IoT networks; reinforcement learning; Q-learning MAC protocol; IoT networks; reinforcement learning; Q-learning
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Lee, T.; Jo, O.; Shin, K. CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks. Electronics 2020, 9, 143.

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