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Journal of Network and Systems Management

Erschienen in:

13.04.2020

An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking

verfasst von: Lucio Henrik A. Reis, Luiz Claudio S. Magalhães, Dianne Scherly V. de Medeiros, Diogo M. F. Mattos

Erschienen in: Journal of Network and Systems Management | Ausgabe 4/2020

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Abstract

Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries.

Vorheriger Artikel ORCHESTRA: Supercharging Wireless Backhaul Networks Through Multi-technology Management

Nächster Artikel Fuzzy, Dynamic and Trust Based Routing Protocol for IoT

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Titel: An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking
verfasst von: Lucio Henrik A. Reis
Luiz Claudio S. Magalhães
Dianne Scherly V. de Medeiros
Diogo M. F. Mattos
Publikationsdatum: 13.04.2020
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 4/2020
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-020-09530-3

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