WSN中利用XGBoost和加权自适应HFLMS的数据约减组合预测方法

WSN中利用XGBoost和加权自适应HFLMS的数

据约减组合预测方法

Introduction

Wirelesssensornetworks(WSNs)arewidelyusedinavarietyofapplicationssuchasenvironmentalmonitoring,industrialautomation,andhealthcare.InWSNs,sensornodesareresponsibleforsensing,processing,andcommunicatingdatatoacentralnode.However,duetolimitedresourcessuchasenergy,memory,andprocessingpower,theamountofdatathatcanbetransmittedfromthesensornodestothecentralnodeisrestricted.Therefore,datareductiontechniquesarerequiredforefficientandeffectivecommunicationinWSNs.

Inthispaper,weproposeadatareductiontechniquethatcombinestheXGBoostalgorithmandweightedadaptiveHFLMSalgorithmtopredictmissingorerroneousdatavaluesinWSNs.Theproposedmethodisbasedonensemblelearning,wheremultiplemodelsarecombinedtoimprovetheaccuracyoftheprediction.

XGBoostAlgorithm

XGBoostisasupervisedlearningalgorithmthatisusedforclassificationandregressiontasks.Itisatree-basedmodelthatoptimizesthegradientboostingalgorithm.XGBoostusesaseriesofdecisiontreestomakepredictionsbycombiningthepredictionsofeachtree.Themodelistrainedusingagradientdescentoptimizationalgorithmthatminimizesthelossfunction.

TheadvantageofXGBoostisitsabilitytohandlemissingvaluesandoutliers,whicharecommoninWSNs.XGBoostalsohastheabilitytohandledifferenttypesofdatasuchascategorical,numerical,andbinarydata.

WeightedAdaptiveHFLMSAlgorithm

TheweightedadaptiveHFLMSalgorithmisarecursiveleast-squaresalgorithmthatisusedtoestimatetheparametersofalinearmodel.Itupdatesthemodelparametersasnewsamplesbecomeavailableandusesaweightingfunctiontogivemoreimportancetorecentsamples.

InWSNs,theweightedadaptiveHFLMSalgorithmcanbeusedtoestimatethemissingorerroneousdatavaluesbyusingtheavailable

datafromtheneighboringnodes.Thealgorithmtakesintoaccountthespatialandtemporalcorrelationsofthedataandadaptstheweightingfunctionaccordingly.

DataReductionTechniquebyEnsembleLearning

TheproposeddatareductiontechniquecombinesthestrengthsoftheXGBoostalgorithmandtheweightedadaptiveHFLMSalgorithmbyusingensemblelearning.Ensemblelearningisamachinelearningtechniquethatcombinesmultiplemodelstoimprovetheaccuracyoftheprediction.

Inthistechnique,XGBoostisusedtogeneratetheinitialpredictionsforthemissingorerroneousdatavalues.ThesepredictionsarethenfedintotheweightedadaptiveHFLMSalgorithm,whichusestheavailabledatafromtheneighboringnodestorefinetheinitialpredictions.TheweightedadaptiveHFLMSalgorithmadaptstheweightingfunctionbasedonthespatialandtemporalcorrelationsofthedata.

ThefinalpredictionisobtainedbycombiningthepredictionsofXGBoostandweightedadaptiveHFLMSusingaweightedaverage.Theweightsareassignedbasedontheaccuracyofeachmodelandareupdateddynamicallytoensurethatthemostaccuratemodelsaregivenmoreweight.

ExperimentalResults

Toevaluatetheperformanceoftheproposeddatareductiontechnique,weconductedexperimentsonaWSNdataset.Thedatasetconsistsoftemperatureandhumidityreadingsfromsensornodesinagreenhouse.

Wecomparedtheperformanceoftheproposedmethodwithotherstate-of-the-artdatareductiontechniquessuchaslinearregression,supportvectorregression,andk-nearestneighborregression.

Theresultsshowthattheproposedmethodoutperformstheothertechniquesintermsofpredictionaccuracy,rootmeansquarederror,andmeanabsoluteerror.Theproposedmethodalsohastheadvantageofbeingabletohandlemissingvaluesandoutliers,whicharecommoninWSNs.

Conclusion

Inthispaper,weproposedadatareductiontechniquethatcombinestheXGBoostalgorithmandweightedadaptiveHFLMSalgorithmtopredictmissingorerroneousdatavaluesinWSNs.The

proposedmethodisbasedonensemblelearning,wheremultiplemodelsarecombinedtoimprovetheaccuracyoftheprediction.

Theresultsoftheexperimentsshowthattheproposedmethodoutperformsotherstate-of-the-artdatareductiontechniquesintermsofpredictionaccuracy,rootmeansquarederror,andmeanabsoluteerror.Theproposedmethodhastheadvantageofbeingabletohandlemissingvaluesan