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基于Q-learning的移动群智感知任务分配算法.docx

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基于Q-learning的移动群智感知任务分配算法 1.Introduction Mobilecrowdsensing(MCS)isapromisingparadigmthatleveragesthepowerofmobiledevicestocollectdatafromthephysicalworld.InMCS,agroupofmobileusersarerecruitedtoperformsensingtasksthatareeither(i)predefinedbyacentralauthority(e.g.,thedeploymentofmultiplesensorsinanareatomonitoranenvironmentalfactorlikepollution)or(ii)specifiedbyparticipatingusersthemselves(e.g.,reportingeventstheyhaveobservedsuchasaccidentsortrafficjams). ToensurethatMCScanprovidetheintendedbenefit,itisessentialtodevelopefficientalgorithmsfortaskassignment.Thisprocessinvolvesselectingthemostsuitableparticipantsforeachtask,takingintoaccountfactorssuchastheirlocation,expertise,andwillingnesstocontribute.Inthispaper,weproposeataskassignmentalgorithmbasedonQ-learning,apopularreinforcementlearningtechnique. 2.RelatedWork SeveralresearchershaveproposedtaskassignmentalgorithmsforMCS,includingapproachesthatusegeneticalgorithms,swarmintelligence,andgametheory.Thesealgorithmsaimtooptimizevariousmetrics,suchastheoverallqualityofcollecteddata,thetimerequiredtocompletetasks,andtheenergyconsumptionofparticipatingdevices. Oneofthemostpopulartechniquesfortaskassignmentisreinforcementlearning,whichenablesdevicestolearnfromtheirinteractionswiththeenvironment.Reinforcementlearningmodelsaretrainedtomaximizearewardsignalthatindicateshowwelltheyareperforminggiventhecurrentstateoftheenvironment. Q-learningisapopularreinforcementlearningalgorithmthathasbeenappliedtoawiderangeofproblems,includingroboticscontrol,gameplaying,andresourceallocation.Q-learningusesatabletotracktheexpectedrewardforeachpossibleactioninagivenstate.Overtime,thetableisupdatedastheagentinteractswiththeenvironmentandreceivesfeedbackintheformofrewardsorpenalties. 3.ProposedAlgorithm Inourproposedalgorithm,eachmobiledeviceismodeledasanagentthatlearnstoselectthemostappropriatetasksbasedonitscurrentstateandtheavailableinformationabouttheenvironment.Thestateofanagentisdefinedbyitslocation,energylevel,expertise,andhistoryoftask