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1、347MiningWirelessSensorNetworkData:anadaptiveapproachbasedonartificialneural-networksalgorithmMiningWirelessSensorNetworkData:anadaptiveapproachbasedonartificialneural-networksalgorithm123LambodarJena,RamakrushnaSwain,NarendraK.Kamila1,3Dept.ofCSE,C.V.RamanColle
2、geofEngineering,Bhubaneswar,India2Dept.ofCSEandIT,C.V.RamanCollegeofEngineering,Bhubaneswar,India123lmjena@yahoo.com,rkswain1@gmail.com,nkamila@yahoo.comAbstract-Thispaperproposesalayeredmodularcommunicationandcomputationaleffortdemandedarchitecturetoadaptivelyp
3、erformdataminingtasksintoeachsinglesensorunit.Thesimplestapproachtolargesensornetworks.Thearchitectureconsistsinatheanalysisofsensornetworkdatamakesuseofalowerlayerwhichperformsdataaggregationinacentralizedarchitecturewhereacentralservermodularfashionandinanuppe
4、rlayerwhichemploysmaintainsadatabaseofreadingsfromallthesensors.anadaptivelocallearningtechniquetoextractaThewholeanalysiseffortislocalizedintheserver,predictionmodelfromtheaggregatedinformation.Thewhosemissionistoextractfromtheflowofdatatherationaleoftheapproac
5、histhatamodularaggregationhigh-levelinformationexpectedtobereturnedbytheofsensordatacanservejointlytwopurposes:first,themonitoringsystem.Ifweassumethatreasonable-sizeorganizationofsensorsinclusters,thenreducingthesensornetworkswillbemadeofthousandsofnodes,commun
6、icationeffort,second,thedimensionalitythelimitationofthisapproachisstrikinglyevident:reductionofthedataminingtask,thenimprovingthethenumberofmessagessentinthesystemaswellasaccuracyofthesensingtask.Hereweshowthatsomeofthealgorithmsdevelopedwithintheartificialneur
7、al-thenumberofvariablesofthedataminingtaskarenetworkstraditioncanbeeasilyadoptedtowirelesstoolargetobemanagedefficiently[7].Acentralizedsensor-networkplatformsandwillmeetseveralaspectsdataclusteringinwirelesssensornetworksisdifficultoftheconstraintsfordatamining
8、insensornetworksandoftennotscalablebecauseofvariousreasonslike:limitedcommunicationbandwidth,limitedsuchaslimitedcommunicationbandwidthandcomputingresources,limitedpo
