localized multisensor multitarget tracking framework.docx

localized multisensor multitarget tracking framework.docx

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localizedmultisensormultitargettrackingframework

Across-layerresourceallocationschemeforICICinLTE-Advanced  OriginalResearchArticle

JournalofNetworkandComputerApplications,InPress,CorrectedProof,Availableonline18January2011

ZhaomingLu,YanYang,XiangmingWen,YingJu,WeiZheng

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AbstractAbstract|Figures/TablesFigures/Tables|ReferencesReferences

Abstract

Asanewtechnology,inter-eNBcoordinationhasbeenincludedinLTE-Advancedstudyitems.Moreover,thenetworkarchitectureinLTE-Advancedsystemismodifiedtotakeintoaccountcoordinatedtransmission.Inourstudy,weexploretheproblemofjointlyoptimizingthepowerlevelandschedulingofresourceblocksforLTE-Advancednetworkbasedonorthogonalfrequencydivisionmultiplexing（OFDM）.Weproposeadistributedoptimizationschemebasedonevolutionarypotentialgames,andintheprocessofobjectivefunctionmodelingweemploytheLagrangianmultipliermethodtosolvetheconstraintobjectiveoptimizationproblem.Thenparticleswarmoptimization（PSO）methodisadoptedtofindtheoptimalpowerallocationandschedulingforeachresourceblockinthemulti-cellframework.Numericalresultsprovethatproposedalgorithmnotablyimprovestheoverallthroughput,whileuserfairnessisguaranteed.Importantly,additionalcomputationandcommunicationcostintroducedbycross-layeroptimizationisalsoevaluated.

ArticleOutline

1.Introduction

2.Inter-cellinterferenceanalysis

3.Potentialgames

3.1.Definition

3.2.ExistenceanduniquenessofNE

4.ALagrangianmultipliermethodforconstraintobjectivefunctionoptimization

4.1.Systemthroughput

4.2.Fairnesscriterion

4.3.ALagrangianmultipliermethod

5.Cross-layeroptimizationalgorithmbasedonpotentialgames

5.1.Cooperationgamesmodeling

5.2.PSObasedmethodtosolveextremeproblemofmultivariatefunction

5.3.ConvergencetoNashequilibrium

6.Simulationanalysis

6.1.Performanceanalysis

6.2.Costanalysis

7.Conclusionsandfuturework

Acknowledgements

References

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127

Prediction-baseddataaggregationinwirelesssensornetworks:

CombininggreymodelandKalmanFilter  OriginalResearchArticle

ComputerCommunications,Volume34,Issue6,3May2011,Pages793-802

GuiyiWei,YunLing,BinfengGuo,BinXiao,AthanasiosV.Vasilakos

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Abstract

Inmanyenvironmentalmonitoringapplications,sincethedataperiodicallysensedbywirelesssensornetworksusuallyareofhightemporalredundancy,prediction-baseddataaggregationisanimportantapproachforreducingredundantdatacommunicationsandsavingsensornodes’energy.Inthispaper,anovelprediction-baseddatacollectionprotocolisproposed,inwhichadouble-queuemechanismisdesignedtosynchronizethepredictiondataseriesofthesensornodeandthesinknode,andtherefore,thecumulativeerrorofcontinuouspredictionsisreduced.Basedonthisprotocol,threeprediction-baseddataaggregationapproachesareproposed:

Grey-Model-basedDataAggregation（GMDA）,Kalman-Filter-basedDataAggregation（KFDA）andCombinedGreymodelandKalmanFilterDataAggregation（CoGKDA）.ByintegratingthemeritofgreymodelinquickmodelingwiththeadvantageofKalmanFilterinprocessingdataseriesnoise,CoGKDApresentshighpredictionaccuracy,lowcommunicationoverhead,andrelativelowcomputationalcomplexity.Experimentsarecarriedoutbasedonarealdatasetofatemperatureandhumiditymonitoringapplicationinagranary.Theresultsshowthattheproposedapproachessignificantlyreducecommunicationredundancyandevidentlyimprovethelifetimeofwirelesssensornetworks.

ArticleOutline

1.Introduction

2.Relatedwork

3.Prediction-baseddatacollectionprotocol

4.Greymodelbaseddataaggregation（GMDA）

5.Kalman-Filter-basedDataAggregation（KFDA）

5.1.Kalman-Filter-basedpredictionmodel

5.2.Kalman-Filter-basedpredictionalgorithm

6.CoGKDA

6.1.Modeling

6.2.Combinationweights

6.3.CoGKDAalgorithm

7.Experimentandperformanceevaluation

7.1.Experimentsetup

7.2.GMDA

7.3.KFDA

7.4.CoGKDA

7.5.Complexityandscalability

8.Conclusion

Acknowledgements

References

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128

EnergyefficientandperceivedQoSawarevideoroutingoverWirelessMultimediaSensorNetworks  OriginalResearchArticle

AdHocNetworks,Volume9,Issue4,June2011,Pages591-607

DionisisKandris,MichailTsagkaropoulos,IliasPolitis,AnthonyTzes,StavrosKotsopoulos

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Abstract

WirelessSensorNetworks（WSNs）haveaneverincreasingvarietyofmultimediabasedapplications.Ιnthesetypesofapplications,networknodesshouldideallymaximizeQoSandminimizeenergyexpendituresinvideocommunication.ThisarticlepresentsPEMuR,anoveldualschemeforefficientvideocommunication,whichaimsatbothenergysavingandhighQoSattainment.Toachieveitsobjectives,PEMuRproposesthecombineduseofanenergyawarehierarchicalroutingprotocolwithanintelligentvideopacketschedulingalgorithm.Theadoptedroutingprotocolenablestheselectionofthemostenergyefficientroutingpaths,managesthenetworkloadaccordingtotheenergyresiduesofthenodesandpreventsuselessdatatransmissionsthroughtheproposeduseofanenergythreshold.Inthisway,anoutstandinglevelofenergyefficiencyisachieved.Additionally,theproposedpacketschedulingalgorithmenablesthereductionofthevideotransmissionratewiththeminimumpossibleincreaseofdistortion.Inordertodoso,itmakesuseofananalyticaldistortionpredictionmodelthatcanaccuratelypredicttheresultedvideodistortionduetoanyerrorpattern.Thus,thealgorithmmaycopewithlimitedavailablechannelbandwidthbyselectivelydroppinglesssignificantpacketspriortotheirtransmission.Simulationresultsdemonstratetheeffectivenessoftheproposedscheme.

ArticleOutline

1.Introduction

2.Relatedwork

2.1.Hierarchicalroutingprotocols

2.2.Routeselectionschemes

2.3.Wirelessvideocommunication

2.4.Packetlossmodels

3.Proposedschemeoverview

3.1.Energyefficienthierarchicalroutingprotocol

3.2.Videopacketscheduling

3.2.1.Videodistortionmodel

3.2.2.Videotransmissionrateadaptation

3.3.PEMuRalgorithmoverview

4.Simulationsetupdescription

5.Simulationresultspresentation

6.Simulationresultsevaluation

7.Conclusions

References

Vitae

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Adaptivepower-controlledMACprotocolsforimprovedthroughputinhardware-constrainedcognitiveradionetworks  OriginalResearchArticle

AdHocNetworks,InPress,CorrectedProof,Availableonline7January2011

HaythemBanySalameh,MarwanKrunz

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Abstract

Cognitiveradios（CRs）areemergingasapromisingtechnologytoenhancespectrumutilizationthroughopportunisticon-demandaccess.ManyMACprotocolsforcognitiveradionetworks（CRNs）havebeendesignedassumingmultipletransceiversperCRuser.However,inpractice,suchanassumptioncomesatthecostofextrahardware.Inthispaper,weaddresstheproblemofassigningchannelstoCRtransmissionsinsingle-hopandmulti-hopCRNs,assumingonetransceiverperCR.TheprimarygoalofourdesignistomaximizethenumberoffeasibleconcurrentCRtransmissions,andconserveenergyasasecondaryobjective,withrespecttobothspectrumassignmentandtransmissionpowersubjecttointerferenceconstraintanduserratedemands.Theproblemisformulatedunderbothbinary-levelandmulti-levelspectrumopportunityframeworks.Ourformulationappliestoanypower-raterelationship.Forsingle-hopCRNs,acentralizedpolynomial-timealgorithmbasedonbipartitematchingthatcomputestheoptimalchannelassignmentisdeveloped.WethenintegratethisalgorithmintodistributedMACprotocolsthatpreservefairness.Formulti-hopadhocCRNs,weproposeanoveldistributedMACprotocol（WFC-MAC）thatattemptstomaximizetheCRNthroughput,assumingsingletransceiverradiosbutwith“dual-receive”capability.WFC-MACusesacooperativeassignmentthatreliesonlyoninformationprovidedbythetwocommunicatingusers.ThemainnoveltyinWFC-MACliesinrequiringnoactivecoordinationwithlicensedusersandexploitingthedual-receivecapabilityofradios,thusalleviatingvariouschannelaccessproblemsthatarecommontomulti-channeldesigns.WeconducttheoreticalanalysisofourMACprotocols,andstudytheirperformanceviasimulations.TheresultsindicatethatcomparedwithCSMA/CAvariants,ourprotocolssignificantlydecreasetheblockingrateofCRtransmissions,andhenceimprovenetworkthroughput.

ArticleOutline

1.Introduction

1.1.Previousresearch

1.2.Contributions

1.3.Organization

2.Problemformulationanddesignconstraints

2.1.Networkmodel

2.2.Feasibilityconstraints

2.3.Problemformulation

3.Optimalchannelassignment

3.1.Proposedalgorithm

3.2.Channelaccessprotocolforsingle-hopCRNs

3.2.1.Assumptions

3.2.2.Operationaldetails

3.3.Remarksanddesignvariants

3.3.1.Granularityofchannelassignment

3.3.2.FairnesspropertiesofAW-MAC

3.3.3.RTS/CTShandshakeinAW-MAC

3.3.4.Channelassignmentwithamulti-levelfrequency-dependentpowerconstraint

3.3.5.AW-MACwithtwotransceiver