Research Area: Wireless Sensor Networks
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.
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Author(s) Name: Ahmed Saeed; Mario Kolberg
Journal name: IEEE Access
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Publisher name: IEEE
DOI: 10.1109/ACCESS.2021.3120348
Volume Information: ( Volume: 9),Page(s): 141513 - 141523
