A Constrained Learning Approach to the Prediction of Reliability Ranking for WSN Services

WirelessSensorNetworkServiceApplications(WSAs)areplayinganimportantroleinWireless SensorNetwork(WSN),whichbridgethegapbetweenWSNandexistingwidelydeployedServiceOrientedArchitecture(SOA)technologies.FunctionpropertiesofWSNservicesareimportant,which assurecorrectfunctionalityofWSA.Meanwhile,nonfunctionalpropertiessuchasreliabilitymight significantlyinfluencetheclient-perceivedqualityofWSA.Thus,buildinghigh-reliabilityWSAis acriticalresearchproblem.Reliabilityrankingsprovidevaluableinformationformakingoptimal WSNserviceselectionfromfunctionallyequivalentservicecandidates.Thereexistedseveralmethods thatcanconductreliabilityrankingpredictionofWSNservices.However,itisdifficulttoevaluate whichoneisbetterthananother,becausethoseacquiredifferentrankingswithdifferentpreference functions.Thispaperproposesaconstrainedlearningpredictionofreliabilityrankingapproachfor WSNservicesonpastserviceusageexperiencesofotherWSAs,whichcanachievehigheraccuracy andimprovetheperformancebypruningcandidateservices.Tovalidatetheauthors’approach,largescaleexperimentsareconductedbasedonareal-worldWSNservicedataset.Theresultsshowthat theirproposedapproachachieveshigherpredictionaccuracythanotherapproaches. KeyWoRdS Quality-of-Service, Ranking Prediction, Reliability, WSN Service

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