Learning nodes: machine learning-based energy and data management strategyopen access
- Authors
- Kim, Y.[Kim, Y.]; Lee, T.-J.[Lee, T.-J.]
- Issue Date
- Sep-2021
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Energy-harvesting; IoT; Q-learning; Transmission policy
- Citation
- Eurasip Journal on Wireless Communications and Networking, v.2021, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- Eurasip Journal on Wireless Communications and Networking
- Volume
- 2021
- Number
- 1
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/91254
- DOI
- 10.1186/s13638-021-02047-6
- ISSN
- 1687-1472
- Abstract
- 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. © 2021, The Author(s).
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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