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Tracking and Predicting End-to-End Quality in Wireless Community Networks

  • Dades identificatives

    Identificador: imarina:5657912
    Autors:
    Millan PMolina CDimogerontakis ENavarro LMeseguer RBraem BBlondia C
    Resum:
    © 2015 IEEE. Community networks are an emergent model with mottos like 'a free net for everyone is possible' or 'don't buy the network, be the network'. Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-to End quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of 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 End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.
  • Altres:

    Autor segons l'article: Millan P; Molina C; Dimogerontakis E; Navarro L; Meseguer R; Braem B; Blondia C
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Millán Marco, Pedro / MOLINA LLAURADÓ, CRISTINA MISERICÒRDIA
    Paraules clau: Time-series analysis End-to-end quality prediction Community networks
    Resum: © 2015 IEEE. Community networks are an emergent model with mottos like 'a free net for everyone is possible' or 'don't buy the network, be the network'. Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-to End quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of 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 End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.
    Àrees temàtiques: Electrical and electronic engineering Artificial intelligence
    ISSN: 9781467381031
    Adreça de correu electrònic de l'autor: pere.millan@urv.cat
    Identificador de l'autor: 0000-0002-4132-7099
    Data d'alta del registre: 2024-10-12
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Proceedings - 2015 International Conference On Future Internet Of Things And Cloud, Ficloud 2015 And 2015 International Conference On Open And Big Data, Obd 2015. 794-799
    Referència de l'ítem segons les normes APA: Millan P; Molina C; Dimogerontakis E; Navarro L; Meseguer R; Braem B; Blondia C (2015). Tracking and Predicting End-to-End Quality in Wireless Community Networks.
    DOI de l'article: 10.1109/FiCloud.2015.96
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2015
    Tipus de publicació: Proceedings Paper
  • Paraules clau:

    Artificial Intelligence,Electrical and Electronic Engineering
    Time-series analysis
    End-to-end quality prediction
    Community networks
    Electrical and electronic engineering
    Artificial intelligence
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