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Conference Paper: Evaluation of short-term traffic forecasting algorithms in wireless networks

TitleEvaluation of short-term traffic forecasting algorithms in wireless networks
Authors
Papadopouli, MariaRaftopoulos, EliasShen, Haipeng
Issue Date2006
Citation
2006 2nd Conference on Next Generation Internet Design and Engineering, NGI 2006, 2006, p. 102-109 How to Cite?
DOI: http://dx.doi.org/10.1109/NGI.2006.1678229
AbstractOur goal is to characterize the traffic load in an IEEE802.11 infrastructure. This can be beneficial in many domains, including coverage planning, resource reservation, network monitoring for anomaly detection, and producing more accurate simulation models. We conducted an extensive measurement study of wireless users on a major university campus using the IEEE802.11 wireless infrastructure. This paper proposes and evaluates several traffic forecasting algorithms based on various traffic models that employ the periodicity, recent traffic history, and flow-related information. Finally, it discusses the impact of time-scale and history on the prediction accuracy. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/219540

 

DC FieldValueLanguage
dc.contributor.authorPapadopouli, Maria-
dc.contributor.authorRaftopoulos, Elias-
dc.contributor.authorShen, Haipeng-
dc.date.accessioned2015-09-23T02:57:20Z-
dc.date.available2015-09-23T02:57:20Z-
dc.date.issued2006-
dc.identifier.citation2006 2nd Conference on Next Generation Internet Design and Engineering, NGI 2006, 2006, p. 102-109-
dc.identifier.urihttp://hdl.handle.net/10722/219540-
dc.description.abstractOur goal is to characterize the traffic load in an IEEE802.11 infrastructure. This can be beneficial in many domains, including coverage planning, resource reservation, network monitoring for anomaly detection, and producing more accurate simulation models. We conducted an extensive measurement study of wireless users on a major university campus using the IEEE802.11 wireless infrastructure. This paper proposes and evaluates several traffic forecasting algorithms based on various traffic models that employ the periodicity, recent traffic history, and flow-related information. Finally, it discusses the impact of time-scale and history on the prediction accuracy. © 2006 IEEE.-
dc.languageeng-
dc.relation.ispartof2006 2nd Conference on Next Generation Internet Design and Engineering, NGI 2006-
dc.titleEvaluation of short-term traffic forecasting algorithms in wireless networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/NGI.2006.1678229-
dc.identifier.scopuseid_2-s2.0-34250173478-
dc.identifier.spage102-
dc.identifier.epage109-

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