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

  • Identification data

    Identifier: imarina:5809701
    Authors:
    Milian, PereAliagas, CariesMolina, CarlosDimogerontakis, EmmanouilMeseguer, Roc
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Milian, Pere; Aliagas, Caries; Molina, Carlos; Dimogerontakis, Emmanouil; Meseguer, Roc
    Department: Enginyeria Informàtica i Matemàtiques
    e-ISSN: 2079-9292
    URV's Author/s: Aliagas Castell, Carlos / Millán Marco, Pedro / Molina Clemente, Carlos María
    Keywords: Time-series analysis End-to-end quality prediction Community networks
    Abstract: 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.
    Thematic Areas: 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
    licence for use: thttps://creativecommons.org/licenses/by/3.0/es/
    ISSN: 08834989
    Author's mail: carlos.molina@urv.cat pere.millan@urv.cat carles.aliagas@urv.cat carles.aliagas@urv.cat
    Author identifier: 0000-0003-1955-0128 0000-0002-4132-7099
    Record's date: 2024-09-07
    Journal volume: 8
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2079-9292/8/5/578
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Electronics. 8 (5): 578-
    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
    Article's DOI: 10.3390/electronics8050578
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2019
    Publication Type: Journal Publications
  • Keywords:

    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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