TY - CPAPER
KW - Algorithm design and analysis
KW - community networks
KW - End-to-End Quality Prediction
KW - Measurement
KW - Prediction algorithms
KW - Routing
KW - Routing Protocols
KW - Support vector machines
KW - Time series analysis
KW - Time-Series Analysis
AU - Pere Millan
AU - Carlos Molina
AU - Emmanouil Dimogerontakis
AU - Leandro Navarro
AU - Roc Meseguer
AU - Bart Braem
AU - Chris Blondia
AB - 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.
DA - 08/2015
DO - 10.1109/FiCloud.2015.96
N2 - 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.
PY - 2015
TI - Tracking and Predicting End-to-End Quality in Wireless Community Networks
ER -