A Data Fusion Framework for Large-Scale Measurement Platforms

dc.cclicenceCC-BY-NCen
dc.contributor.authorRattadilok, Prapaen
dc.contributor.authorMcCall, Johnen
dc.contributor.authorBurbridge, Trevoren
dc.contributor.authorSoppera, Andreaen
dc.contributor.authorEardley, Philipen
dc.date.acceptance2015-08-27en
dc.date.accessioned2017-10-19T09:41:17Z
dc.date.available2017-10-19T09:41:17Z
dc.date.issued2015-12-28
dc.description.abstractThe need to assess internet performance from the user’s perspective grows, as does the interest in deployment of Large-Scale Measurement Platforms (LMAPs). The potential of these platforms as a real-time network diagnostic tool is limited by the volume, velocity and variety of the data they generated. Fusing this data from multiple sources and generating a single piece of coherent information about the state of the network would increase the efficiency of network monitoring. The current practice of visually analysing LMAPs’ data stream would certainly benefit from having automatically generated notifications in a timely manner alerting human controllers to the network’s conditions of interest. This paper proposed a data fusion framework for LMAPs that makes use of mathematical distribution based sensors to generate probabilistic sensor outputs which are fused using a Dempster- Shafer Theory.en
dc.funderBritish Telecomen
dc.identifier.citationRattadilok, P. et al. (2015) A data fusion framework for large-scale measurement platforms. Big Data (Big Data), 2015 IEEE International Conference on,en
dc.identifier.doihttps://doi.org/10.1109/BigData.2015.7364000
dc.identifier.isbn9781479999262
dc.identifier.urihttp://hdl.handle.net/2086/14658
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidBritish Telecomen
dc.publisherIEEEen
dc.subjectdata fusionen
dc.subjectsensorsen
dc.subjectactive measurementsen
dc.subjectlargescale measurement platformen
dc.titleA Data Fusion Framework for Large-Scale Measurement Platformsen
dc.typeConferenceen

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