eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning

Guilherme Henrique Apostolo, Flavia Bernardini, Luiz C. Schara Magalhães and Débora C. Muchaluat-Saade
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Guilherme Henrique Apostolo: Institute of Computing, Universidade Federal Fluminense, Niterói 24210-240, Brazil
Flavia Bernardini: Institute of Computing, Universidade Federal Fluminense, Niterói 24210-240, Brazil
Luiz C. Schara Magalhães: MídiaCom Lab, Universidade Federal Fluminense, Niterói 24210-240, Brazil
Débora C. Muchaluat-Saade: Institute of Computing, Universidade Federal Fluminense, Niterói 24210-240, Brazil

Energies, 2022, vol. 15, issue 2, 1-23

Abstract: As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI network. The eSCIFI can cope with WLANs that cannot acquire data in a real time manner and/or possess a limited CPU power. The eSCIFI design also includes two clustering algorithms, named cSCIFI and cSCIFI+, that help to guarantee the network’s coverage. eSCIFI uses those network clusters and machine learning predictions as input features to an energy state decision algorithm that then decides which Access Points ( AP ) can be switched off during the day. To evaluate eSCIFI performance, we conducted several trace-driven simulations comparing the eSCIFI mechanism using both clustering algorithms with other energy saving mechanisms found in the literature using the UFF SCIFI network traces. The results showed that eSCIFI mechanism using the cSCIFI+ clustering algorithm achieves the best performance and that it can save up to 64.32% of the UFF SCIFI network energy without affecting the user coverage.

Keywords: WLAN energy saving mechanism; machine learning; RoD strategy mechanisms; smart buildings; Wi-Fi networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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