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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval
Author(s): Saeed, Ahmed
Kolberg, Mario
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Keywords: 802.11
energy consumption
power save mode (PSM)
wireless local area network (WLAN)
machine learning (ML)
Issue Date: 2021
Date Deposited: 26-Oct-2021
Citation: Saeed A & Kolberg M (2021) Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval. IEEE Access, 9, pp. 141513-141523.
Abstract: Despite the rapid growth of Wireless Local Area Networks (WLANs), the energy consumption caused by wireless communication remains a significant factor in reducing the battery life of power-constrained wireless devices. To reduce the energy consumption, static and adaptive power saving mechanisms have been deployed in WLANs. However, some inherent drawbacks and limitations remain. We have developed the concept of Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. In this paper we develop the power saving modes of CALI. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to Pyles’ et al. SAPSM power saving approach, which also employs an ML classifier.
DOI Link: 10.1109/ACCESS.2021.3120348
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see
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