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Open AccessArticle

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

1
Department of Information and Communication Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
2
School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4158; https://doi.org/10.3390/s20154158
Received: 7 July 2020 / Revised: 23 July 2020 / Accepted: 24 July 2020 / Published: 26 July 2020
(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)
The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms. View Full-Text
Keywords: Internet of Things; IEEE 802.15.4; MAC protocols; RPL; reinforcement learning; Q-learning Internet of Things; IEEE 802.15.4; MAC protocols; RPL; reinforcement learning; Q-learning
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MDPI and ACS Style

Musaddiq, A.; Nain, Z.; Ahmad Qadri, Y.; Ali, R.; Kim, S.W. Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns. Sensors 2020, 20, 4158.

AMA Style

Musaddiq A, Nain Z, Ahmad Qadri Y, Ali R, Kim SW. Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns. Sensors. 2020; 20(15):4158.

Chicago/Turabian Style

Musaddiq, Arslan; Nain, Zulqar; Ahmad Qadri, Yazdan; Ali, Rashid; Kim, Sung W. 2020. "Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns" Sensors 20, no. 15: 4158.

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