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Tracking and predicting link quality in wireless community networks
Millán, Pere; Molina Clemente, Carlos Maria; Medina Medina, Esunly; Vega d'Aurelio, Davide; Meseguer Pallarès, Roc; Braem, Bart; Blondia, Chris
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts
Community networks have emerged under the mottos of “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed and decentralized networks. We demonstrate that this type of prediction achieves about a success probability of about 98% in both the short and long term.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica::Sèries temporals
Wireless communication systems
Time-series analysis
Community networks
Link quality prediction
Link quality tracking
Time series analysis
Comunicació sense fil, Sistemes de
Sèries temporals -- Anàlisi
Attribution-NonCommercial-NoDerivs 3.0 Spain
Institute of Electrical and Electronics Engineers (IEEE)

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