Learning nodes: machine learning-based energy and data management strategy

被引:0
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作者
Yunmin Kim
Tae-Jin Lee
机构
[1] Sungkyunkwan University,College of Information and Communication Engineering
关键词
Energy-harvesting; Transmission policy; Q-learning; IoT;
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摘要
The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput.
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