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Article

greenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings

1
School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
2
Faculty of Engineering Science, Technology and Management, Ziauddin University, Karachi 74700, Pakistan
3
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
4
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Korea
5
Department of Software and Communications Engineering, Hongik University, Sejong 30016, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2020, 9(10), 1720; https://doi.org/10.3390/electronics9101720
Received: 19 September 2020 / Revised: 13 October 2020 / Accepted: 14 October 2020 / Published: 19 October 2020
(This article belongs to the Section Networks)
We have seen a promising acceptance of wireless local area networks (WLANs) in our day-to-day communication devices, such as handheld smartphones, tablets, and laptops. Energy preservation plays a vital role in WLAN communication networks. The efficient use of energy remains one of the most substantial challenges to WLAN devices. Several approaches have been proposed by the industrial and institutional researchers to save energy and reduce the overall power consumption of WLAN devices focusing on static/adaptive energy saving methods. However, most of the approaches save energy at the cost of throughput degradation due to either increased sleep-time or reduced number of transmissions. In this paper, we recognize the potentials of reinforcement learning (RL) techniques, such as the Q-learning (QL) model, to enhance the WLAN’s channel reliability for energy saving. QL is one of the RL techniques, which utilizes the accumulated reward of the actions performed in the state-action model. We propose a QL-based energy-saving MAC protocol, named greenMAC protocol. The proposed greenMAC protocol reduces the energy consumption by utilizing accumulated reward value to optimize the channel reliability, which results in reduced channel collision probability of the network. We assess the degrees of channel congestion in collision probability as a reward function for our QL-based greenMAC protocol. The comparative results show that greenMAC protocol achieves enhanced system throughput performance with additional energy savings compared to existing energy-saving mechanisms in WLANs. View Full-Text
Keywords: WLANs; MAC protocols; energy saving; green communication; Q learning WLANs; MAC protocols; energy saving; green communication; Q learning
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MDPI and ACS Style

Ali, R.; Sohail, M.; Almagrabi, A.O.; Musaddiq, A.; Kim, B.-S. greenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings. Electronics 2020, 9, 1720. https://doi.org/10.3390/electronics9101720

AMA Style

Ali R, Sohail M, Almagrabi AO, Musaddiq A, Kim B-S. greenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings. Electronics. 2020; 9(10):1720. https://doi.org/10.3390/electronics9101720

Chicago/Turabian Style

Ali, Rashid, Muhammad Sohail, Alaa O. Almagrabi, Arslan Musaddiq, and Byung-Seo Kim. 2020. "greenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings" Electronics 9, no. 10: 1720. https://doi.org/10.3390/electronics9101720

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