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1、LocalizationofWirelessSensorsusing1CompressiveSensingforManifoldLearningChenFeng1,2,ShahrokhValaee1,ZhenhuiTan21DepartmentofElectricalandComputerEngineering,UniversityofToronto2StateKeyLaboratoryofRailTraf?cControlandSafety,BeijingJiaotongUniversityEmail:{chenfeng,valaee}@co
2、mm.utoronto.ca,zhhtan@center.njtu.edu.cnAbstractInthispaper,anovelcompressivesensingwithothermeasurement-basedalgorithms(e.g,time-of-formanifoldlearningprotocol(CSML)isproposedforarrival(TOA)orangle-of-arrival(AOA)measurementslocalizationinwirelesssensornetworks(WSNs).Inter-
3、ofultra-wideband(UWB)[6]),ML-basedalgorithmssensorcommunicationcostsarereducedsigni?cantlybyavoidexpensivedevices,sincetheonlyrequirementforapplyingthetheoryofcompressivesensing,whichindicatesthatsparsesignalscanberecoveredfromfarfewersampleslearninginacentralnodeispair-wise
4、measurements,thanthatneededbytheNyquistsamplingtheorem.Wewhichcouldbeanyofthephysicalreadingsthatindicaterepresentthepair-wisedistancemeasurementasasparsedistanceinformationamongsensors,suchasthereceivedmatrix.Insteadofsendingfullpair-wisemeasurementsignalstrength(RSS)ortheh
5、op-count.However,withdatatoacentralnode,eachsensortransmitsonlyatheincreasingnumberofsensornodes,thescaleofpair-smallnumberofcompressivemeasurements.Andthefullpair-wisedistancematrixcanbewellreconstructedfromwisemeasurementsbecomesverylarge.Communicationthesenoisycompressive
6、measurementsinthecentralnode,costbetweeneachsensornodeandthecentralnodeisaonlythroughan?1-minimizationalgorithm.Theproposedbottleneckinthesecases.Patwarietal.[7]showedthemethodreducestheoverallcommunicationbandwidthre-accuracyandrobustnessofMLalgorithms,butneglectedquirement
7、persensorsuchthatitincreaseslogarithmicallythelargecommunicationcostswhenobtainingthepair-withthenumberofsensorsandlinearlywiththenumberofneighbors,whileachieveshighlocalizationaccuracy.wisemeasurementsbyassumingthemtobeknown.CSMLisespeciallysuitableformanifoldlearningbasedI
8、n[8],ithasbeenrealizedthat,withthehelplocalizationalgorithms.Simulationresu
