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Investigation of distributed optimization methods with coordination constraints
KTH, School of Electrical Engineering (EES), Network and Systems engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Of fundamentalimportanceinnetworkedsystemsisrealtimeresourceallocationpolicies.Forexample,inwaterdistributionsystems,waterhastobeoptimallyallocatedfromtheproducestotheusers.Inwirelesscommu-nication systems,radioresourceshavetobeoptimallymanagedatthebasestations andaccesspointsoruserequipmentsandclients.InSmartGrids,electrical owshavetobeoptimallyallocatedfromtheproducestothecon-sumers. Inallthesesystems,theallocationsaredonebycommunicatinginformation overInternetofThings(IoT)networks,servingtheCyberphys-ical systems,thathavelimitationssuchasbandwidth,delay,andlosses.Although resourceallocationschemesareoptimalintheory,inpracticetheyare challengedbytheIoTnetworklimitationsthatcaneasilycauseinaccu-rate andthusnotoptimalallocationresults.Motivatedbytheseschallenges,westudyhowthenegativeeectsoftimedelaycanbereducedwhensolv-ing distributedresourceallocationproblemsbyusingestimationtechniques.In particular,weapplymethodsusedfordatatting,toestimatethede-layedcurrentvalue,basedonalreadyarrivedsignals.Thesethreemethodsare, respectively,interpolation,leastsquaresandarticialneuralnetworks.Starting fromtheoreticalanalysisofsignalsequenceregularity,interpolationand leastsquaresareproposed.Ontopofthat,thearticialneuralnetworkfurther enablesustopreformthepredictionwithoutpreknowledgeofthereg-ularity.Weshowinnumericalsimulationsthatallthreemethodscanlargelyimprovetheconvergencerateofstandardresourceallocationalgorithmswhenthe communicationisdelayed.Specically,theconvergencerateisremark-ably faster,comparedwithusingthelatestreceiveddatapurelytosubstitutethe delayedone,eventhantheidealcasewherethereisnodelay.Thesere-sults implythatmethodsfromnumericalanalysisandmachinelearningcanbeusefultoolsforpredictingdelayedsignals,whenimplementingresourceallocationalgorithmsinreal-worldinfrastructureswithimperfectcommuni-cation networks.Thethesiscontainsthefollowingparts:motivationandliterature review,backgroundtheories,thenweapplythethreeestimationtechniquesforLagrangiandualandprimalvariablesprediction,inaspecicdistributed resourceallocationmodel,andnumericalsimulation.

Abstract [sv]

Av avgorandebetydelseinatverkssystemaregensressursallokeringpoli-tik forattanvandareskadelagemensammakommunikationsresurser,sasombandbredd ochelkraft.Saledesresursallokeringirealtidalgoritmerkom-mer attvaragrundlaggandefordessasystem.Dockartidsfordrojningenoundviklig forverkligakommunikationsnat. Avenomresursallokeringsys-tem aroptimalaiteorin,eftersomdesannanuvardenasomuppdateringenberorpaarforsenade,detkanlattorsakafelaktigaochdarmedinteoptimaltfordelningsresultat.Motiveradavdessautmaningarstuderarvihurdenega-tivaeekternaavtidenforseningarkanminskasnarmanloserproblemmeddistribuerade resursfordelningargenomattanvandauppskattningstekniker.I synnerhettillamparvimetodersomanvandsfordataforattuppskattadet forsenadenuvardet,baseratparedananlantsignaler.Dessatremetoderarrespektiveinterpolering,minstakvadraterochkonstellaneuralanatverk.Utgaendefranteoretiskanalysavsignalsekvensregelbundenhet,interpola-tion ochminstakvadraterforeslas.Ovanpadet,articialneuralanatverketgordetmojligtforossattpreformaforutsagelsenutanforkunnelseomregel-bundenhet. Vivisarinumeriskasimuleringarattallatremetodernaistorutstrackningkanforbattraskonvergenshastighetenforstandardresursal-lokeringsalgoritmernarkommunikationenarforsenad.Speciktarkon-vergensfrekvensenanmarkningsvartsnabbare,jamfortmedattanvandadesenaste mottagnauppgifternabaraforattersattadenfordrojda,avenomdet aridealisktfalldardetintennsnagonfordrojning.Dessaresultatinnebarattmetoderfrannumeriskanalysochmaskininlarningkanvaraanvandbaraverktygforattforutsagafordrojdasignalernarduimplementerarresursen allokeringsalgoritmeriverkligainfrastrukturermedofullkomligkom-munikationnatverk.Avhandlingeninnehallerfoljandedelar:motivationochlitteraturoversyn,bakgrundsteorier,datillamparvidetreuppskattning-steknikernaforLagrangiandubblaochprimalavariablerforutsagelse,ienspecidistribueradresursfordelningsmodellochnumerisksimulering.

Place, publisher, year, edition, pages
2017. , p. 62
Series
TRITA-EE, ISSN 1653-5146 ; 2017:081
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-214944OAI: oai:DiVA.org:kth-214944DiVA, id: diva2:1144261
Educational program
Master of Science - Aerospace Engineering
Supervisors
Examiners
Available from: 2017-10-11 Created: 2017-09-26 Last updated: 2017-10-11Bibliographically approved

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