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Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

  • Dades identificatives

    Identificador: imarina:5809701
    Autors:
    Milian, PereAliagas, CariesMolina, CarlosDimogerontakis, EmmanouilMeseguer, Roc
    Resum:
    Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free “do it yourself” Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.
  • Altres:

    Autor segons l'article: Milian, Pere; Aliagas, Caries; Molina, Carlos; Dimogerontakis, Emmanouil; Meseguer, Roc
    Departament: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2079-9292
    Autor/s de la URV: Aliagas Castell, Carlos / Millán Marco, Pedro / Molina Clemente, Carlos María
    Paraules clau: Time-series analysis End-to-end quality prediction Community networks
    Resum: Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free “do it yourself” Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.
    Àrees temàtiques: Signal processing Physics, applied Hardware and architecture Engineering, electrical & electronic Engenharias iv Electrical and electronic engineering Control and systems engineering Computer science, information systems Computer networks and communications
    Accès a la llicència d'ús: thttps://creativecommons.org/licenses/by/3.0/es/
    ISSN: 08834989
    Adreça de correu electrònic de l'autor: carlos.molina@urv.cat pere.millan@urv.cat carles.aliagas@urv.cat carles.aliagas@urv.cat
    Identificador de l'autor: 0000-0003-1955-0128 0000-0002-4132-7099
    Data d'alta del registre: 2024-09-07
    Volum de revista: 8
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2079-9292/8/5/578
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Electronics. 8 (5): 578-
    Referència de l'ítem segons les normes APA: Milian, Pere; Aliagas, Caries; Molina, Carlos; Dimogerontakis, Emmanouil; Meseguer, Roc (2019). Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks. Electronics, 8(5), 578-. DOI: 10.3390/electronics8050578
    DOI de l'article: 10.3390/electronics8050578
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Networks and Communications,Computer Science, Information Systems,Control and Systems Engineering,Electrical and Electronic Engineering,Engineering, Electrical & Electronic,Hardware and Architecture,Physics, Applied,Signal Processing
    Time-series analysis
    End-to-end quality prediction
    Community networks
    Signal processing
    Physics, applied
    Hardware and architecture
    Engineering, electrical & electronic
    Engenharias iv
    Electrical and electronic engineering
    Control and systems engineering
    Computer science, information systems
    Computer networks and communications
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