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1、ANewDeterministicDataAggregationMethodForWirelessSensorNetworksHuseyinAkcanandHerv¨eBr´onnimann¨Computer&InformationScienceDepartmentPolytechnicUniversity,Brooklyn,NY11201hakcan01@cis.poly.eduandhbr@poly.eduAbstractTheprocessingcapabilitiesofwirelesssensornodesenabletoaggregater
2、edundantdatatolimittotaldataflowoverthenetwork.Themainpropertyofagoodaggregationalgorithmistoextractthemostrepresentativedatabyusingminimumresources.Fromthispointofview,samplingisapromisingaggregationmethod,thatactsassurrogateforthewholedata,andonceextractedcanbeusedtoanswermulti
3、plekindsofqueries(suchasAVG,MEDIAN,SUM,COUNT,etc.),atnoextracosttothesensornetwork.Additionally,samplingalsopreservescorrelationsbetweenattributesofmulti-dimensionaldata,whichisquitevaluableforfurtherdatamining.Inthispaper,weproposeanovel,distributed,weightedsamplingalgorithmtos
4、amplesensornetworkdataandcomparetoanexistingrandomsamplingalgorithm,whichistheonlyalgorithmtoworkinthiskindofsetting.Weperformpopularqueriestoevaluateouralgorithmonarealworlddataset,whichcoversclimatedataintheU.S.forthepast100years.Duringtesting,wefocusonissuessuchassamplequalit
5、y,networklongevity,energyandcommunicationcosts.Keywords:Sampling;datareduction;in-networkdataaggregation;datastreams;sensornetworks0ResearchoftheauthorshasbeensupportedbyNSFCAREERGrantCCR-0133599PreprintsubmittedtoElsevier15May20071IntroductionInthispaper,wefocusonsamplingdatafr
6、omasetofsensornodeslinkedbyanetworkandconsidertheproblemofextractingthesamplefromthenetworktoacentralDBMSandlaterqueryingittofindanswerstoconditionsinsidethesensornetwork.Thesequeriesmaybebasedonsnapshots,ormaybecontinuous.Thedatacollectedbythesensorsisusuallyhighlyredundant,andt
7、husoneneednotcollectandprocessallofit,approximatequeryresultsareusuallysufficient.Hence,substantialsavingsmaybeobtainedbyeitheraggregatingorprocessingthedatain-network,orbyobtainingamuchsmallerbutrepresentativesamplewhichcanthenbeprocessedbyacentralizedmorepowerfulunit.Oneparticu
8、larkindofdatawefocusonisthemulti-dimensionalcou
