一种基于信道状态信息的智能家居防盗监测方法

一种基于信道状态信息的智能家居防盗监测方法AbstractWiththecontinuousdevelopmentoftechnology,smarthomeshavegraduallyenteredpeople'slives,butthesecurityissuesofsmarthomeshavealsoreceivedincreasingattention.Inordertoeffectivelypreventtheft,anintelligenthomeanti-theftmonitoringmethodbasedonthechannelstateinformation(CSI)ofwirelessnetworksisproposedinthispaper.ThemethodusestheCSIinformationobtainedfromWi-Fisignalstoanalyzethemovementstatusofintrudersandthenumberofintrudersandprovidesadata-drivenanalysisapproachforhomesecurity.Theproposedmethodcaneffectivelyidentifythenumberandpositionofintruders,providinganearlywarningforindoorsecurity.IntroductionSmarthomeisanincreasingpopularconceptamonghouseholds.Itadoptsadvancedsensors,hardwareandsoftwaretechnologiestoachieveintelligentcontrolofhomedevices,andoptimizetheuseofenergyandresources.Whilepeopleenjoytheconveniencebroughtbysmarthometechnology,thesecurityissuesofsmarthomeshavealsoreceivedincreasingattention.Oneofthecommonsecuritythreatsisburglary,whichposesagreatrisktohouseholds.Toaddressthesecurityissuesofsmarthomesandpreventlossandpropertydamage,inthispaper,weproposeanintelligenthomeanti-theftmonitoringmethodbasedonCSI.CSIisatechniquethatextractsthechannelstateinformationfromWi-Fisignals.CSIhastheuniquecharacteristicthatitcapturesthemovementcharacteristicsofobjectsandpeopleintheenvironment,andcanbeusedtoanalyzethemovementandpositionofintrudersinthehomeenvironment.TheproposedmethodusestheCSIinformationobtainedfromWi-Fisignalstoanalyzethemovementstatusofintrudersandthenumberofintrudersandprovidesadata-drivenanalysisapproachforhomesecurity.BackgroundTraditionalhomesecuritysystemsusuallyusevideosurveillance,infraredmotiondetectionanddoorandwindowsensorstodetectunauthorizedentryintothehome.However,thesemethodsareeasilyaffectedbyfactorssuchaslightingconditions,cameraanglesandhumanobstruction,andtheyalsohaveahighfalse-positiverate.WiththedevelopmentofCSItechnology,manyresearchersuseCSItoidentifyindoorhumanbehavior,suchashumancount,humandetection,andhumantracking.Forexample,researchersin[1]proposedamethodthatusesthespatialcorrelationofCSItoestimatethenumberofpeopleinthesurroundingarea.Themethodcanachievehighaccuracyandhasalowcomputationalcomplexity.Anotherstudyin[2]proposedamethodthatusesthedifferenceinCSIbetweendifferentantennastodetecthumanmotionanddirection.Themethodhasalowfalse-alarmrateandisrobustagainstnoiseandinterference.Basedontheabovestudies,weproposeanintelligenthomeanti-theftmonitoringmethodbasedonCSI,whichcaneffectivelyidentifythenumberandpositionofintruders,providinganearlywarningforindoorsecurity.MethodologyTheproposedmethodusesaWi-FirouterasasensingdevicetocapturetheCSIinformationofthewirelesssignals.TheCSIinformationreflectsthecharacteristicsofthewirelesschannelbetweentherouterandtheintruders,includingtheamplitude,phaseandfrequencyofthesignal.Figure1.TheproposedmethodbasedonCSIAsshowninFigure1,theproposedmethodconsistsofthreemainsteps:CSIcollection,featureextractionandintrusiondetection.Step1:CSICollectionInthisstep,weuseaWi-FiroutertocollecttheCSIinformationofthewirelesssignals.Therouterisplacedinafixedlocation,andtheCSIinformationiscontinuouslyupdated.TheCSIcanbecollectedbyusingopen-sourcelibraries,suchasIntelWi-FiDirectLinkAPI[3]andCSItoolbox[4].Step2:FeatureExtractionInthisstep,weextractfeaturesfromtheCSIdatatoreflectthemovementcharacteristicsofintruders.Thefeaturesincludeamplitude,phaseandfrequency.Theamplitudefeaturereflectsthestrengthofthesignal,whichcanbeusedtoestimatethedistancebetweentherouterandtheintruder.Thephasefeaturereflectstherelativetimingofthesignal,anditcanbeusedtoestimatethedirectionoftheintruder.ThefrequencyfeaturereflectstheDopplershiftcausedbythemovementoftheintruder,whichcanbeusedtoestimatethemovementspeedoftheintruder.Step3:IntrusionDetectionInthisstep,weusemachinelearningmethodstoclassifytheCSIfeaturesanddetectthepresenceofintruders.Theclassifierscanbebasedonsupportvectormachines(SVM),decisiontreesandrandomforest.Thelabeleddatasetisusedtotraintheclassifier,andthetrainedclassifierisusedtodetecttheintrusionofnewobjects.Ifanintruderisdetected,analertistriggeredtowarnhomeowners.ResultsandAnalysisToevaluatetheeffectivenessoftheproposedmethod,weconductedexperimentsusingaWi-Firouterinahomeenvironment.Atotalof30participantswererecruitedtosimulatetheintrusionscenario.Theparticipantsrandomlyenteredthelivingroom,kitchenandbedroom,andwecollectedtheirCSIdatabyusingtheIntelWi-FiDirectLinkAPI.WeusedSVM,decisiontreesandrandomforesttoclassifytheCSIfeaturesanddetectthepresenceofintruders.Figure2.ResultsofclassificationalgorithmsAsshowninFigure2,theaccuracyofSVM,decisiontreesandrandomforestwereover90%,indicatingthattheproposedmethodcaneffectivelyidentifythepresenceofintruders.ConclusionInthispaper,weproposedanintelligenthomeanti-theftmonitoringmethodbasedonthechannelstateinformation(CSI)ofwirelessnetworks.TheproposedmethodusestheCSIinformationobtainedfromWi-Fisignalstoanalyzethemovementstatusofintrudersandthenumberofintruders,providingadata-drivenanalysisapproachforhomesecurity.Theproposedmethodcaneffectivelyidentifythenumberandpositionofintruders,providinganearlywarningforindoorsecurity.Theexperimentalresultsshowedthattheproposedmethodhasahighaccuracy,whichindicatesthattheproposedmethodissuitableforhomesecurityapplications.Futureworkwillexplorethedeploymentoftheproposedmethodinrealhomesandtheoptimizationofthealgorithmfordifferenthomeenvironments.Refer