Articles producció científica> Enginyeria Informàtica i Matemàtiques

Time Series Analysis to Predict Link Quality of Wireless Community Networks

  • Dades identificatives

    Identificador: imarina:5129027
    Autors:
    Millan, PereMolina, CarlosMedina, EsunlyVega, DavideMeseguer, RocBraem, BartBlondia, Chris
    Resum:
    © 2015 Elsevier B.V. Allrightsreserved. Community networks have emerged under the mottos 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 it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.
  • Altres:

    Autor segons l'article: Millan, Pere; Molina, Carlos; Medina, Esunly; Vega, Davide; Meseguer, Roc; Braem, Bart; Blondia, Chris;
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Millán Marco, Pedro / Molina Clemente, Carlos María
    Paraules clau: Time series analysis Link quality tracking Link quality prediction Community networks
    Resum: © 2015 Elsevier B.V. Allrightsreserved. Community networks have emerged under the mottos 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 it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.
    Àrees temàtiques: Telecommunications Matemática / probabilidade e estatística Interdisciplinar Engineering, electrical & electronic Engenharias iv Engenharias iii Engenharias i Computer science, information systems Computer science, hardware & architecture Computer networks and communications Ciências biológicas i Ciência da computação Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: carlos.molina@urv.cat pere.millan@urv.cat
    Identificador de l'autor: 0000-0003-1955-0128 0000-0002-4132-7099
    Data d'alta del registre: 2024-09-07
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/pii/S1389128615003412
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Computer Networks. 93 (2): 342-358
    Referència de l'ítem segons les normes APA: Millan, Pere; Molina, Carlos; Medina, Esunly; Vega, Davide; Meseguer, Roc; Braem, Bart; Blondia, Chris; (2015). Time Series Analysis to Predict Link Quality of Wireless Community Networks. Computer Networks, 93(2), 342-358. DOI: 10.1016/j.comnet.2015.07.021
    DOI de l'article: 10.1016/j.comnet.2015.07.021
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2015
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Networks and Communications,Computer Science, Hardware & Architecture,Computer Science, Information Systems,Engineering, Electrical & Electronic,Telecommunications
    Time series analysis
    Link quality tracking
    Link quality prediction
    Community networks
    Telecommunications
    Matemática / probabilidade e estatística
    Interdisciplinar
    Engineering, electrical & electronic
    Engenharias iv
    Engenharias iii
    Engenharias i
    Computer science, information systems
    Computer science, hardware & architecture
    Computer networks and communications
    Ciências biológicas i
    Ciência da computação
    Administração pública e de empresas, ciências contábeis e turismo
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