evolutionary power‐aware routing in vanets using...

Madrid, Spain 2-6 July 2012

J.Toutouh,S.Nesmachnow,andE.Alba Evolu&onaryPower‐AwareRou&nginVANETsusingMonte‐CarloSimula&on

OPTIM 2012 HPCS 2012

EvolutionaryPower‐AwareRoutinginVANETsusingMonte‐CarloSimulation

OptimizationIssuesinEnergyEfficientDistributedSystems(HPCS2012)

JamalToutouh,SergioNesmachnow*,andEnriqueAlbaUniversityofMalaga,Spain UniversidaddelaRepública,Uruguay*




3

Outline

Introduction

Power‐AwareAODV

ExperimentalResults

ConclusionsandFutureWork

1

2

4

IntroductionPower‐AwareAODV

ExperimentalAnalusisConclusionsandFutureWork

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1.Introduction.VANETsandEnergyIssues Vehicularadhocnetworks(VANETs)areself‐configuringwirelessadhocnetworks inwhich thenodesarevehicles, elementsof roadside,sensors,andpedestrianpersonaldevices(smartphones,PDAs,etc.)

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• VANETandEnergyIssues• Motivation

TransportEfficiency Safety

Deployedtoenableup‐to‐minuteroadtrafficinformationexchange


ExperimentalAnalysisConclusionsandFutureWork

VANETs involve devices fed with limited powersources. Therefore, power‐aware networkarchitecturesandprotocolsarehighlydesirable

Theroutingprotocolaffectsthenodespowerconsumptionintwoways: Theprotocoloperationamountofenergyusedtocomputetheroutingpaths The computed routing paths the terminals power consumption whenforwardingpackets




1.Introduction.Motivation

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WestudytheapplicationofDifferentialEvolution(DE)tocomputepower‐awareroutingprotocolconfigurations

The optimization process is guided by evaluating tentative solutions(protocolparameterizations)bymeansofVANETsimulations

StochasticpropagationmodelsthatproducedifferentresultswhenevaluatingthesameVANETscenario(upto57%forsomemetricsandscenarios)

A number of simulations of the same scenario is performed applyingMonte‐Carlomethodtoevaluateasolution

AstheVANETsimulationsrequirehighcomputationalcoststhesimulationsareperformedinparallelusingdifferentprocessunitsofacluster

• VANETandEnergyIssues• Motivation






2.Power‐AwareAODV.ProblemDefinition

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Finding the best AODV configurations to enable green communications inVANETsisthemainsubjectofthiswork

AODVisaroutingprotocolformobileadhocnetworksusedinVANETsAODVRFC3561

Anexcessiveenergyconsumptionreductioncanleadtoprotocolmalfunction

QoSrestrictionMaximumallowedPDRdegradation15%(overthestandard)

parameter RFCvalue type rangeHELLO_INTERVAL 1.0s real [1.0,20.0]ACTIVE_ROUTE_TIMEOUT 3.0s real [1.0,20.0]MY_ROUTE_TIMEOUT 6.0s real [1.0,40.0]NODE_TRAVERSAL_TIME 0.04s real [0.01,15.0]MAX_RREQ_TIMEOUT 10.0s real [1.0,100.0]NET_DIAMETER 35 integer [3,100]ALLOWED_HELLO_LOSS 2 integer [0,20]REQ_RETRIES 2 integer [0,20]TTL_START 1 integer [1,40]TTL_INCREMENT 2 integer [1,20]TTL_THRESHOLD 7 integer [1,60]

• ProblemDefinition• Methodology• OptimizationMethodDetails• FitnessEvaluation






Automatic optimization tool coupling Differential Evolution (DE) andMonte‐CarloVANETsimulation

Methodology

(energy, PDR)

Differen4al

Evolu4on

AODV configuration

Energy-efficient AODV configuration

Solution evaluation new solution (AODV configuration)

fitness value

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11

AODV protocol ns‐2VANETsimula4onn

AODV protocol ns‐2VANETsimula4onn

AODV protocol AODV protocol ns‐2VANETsimula4onn

AODV

x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11

Fitnessfunc4on

Monte Carlo

method

nprocessingunits

2.Power‐AwareAODV.Methodology


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ProblemEncoding:

FitnessFunction:Penaliza4onmodel(PDRdegrada&on>15%)

2.Power‐AwareAODV.OptimizationMethodDetails

Realvalued Integers

Δ=0.1,ω1=0.9,andω2=‐0.1

parameter type rangeHELLO_INTERVAL real [1.0,20.0]ACTIVE_ROUTE_TIMEOUT real [1.0,20.0]MY_ROUTE_TIMEOUT real [1.0,40.0]NODE_TRAVERSAL_TIME real [0.01,15.0]MAX_RREQ_TIMEOUT real [1.0,100.0]NET_DIAMETER integer [3,100]ALLOWED_HELLO_LOSS integer [0,20]REQ_RETRIES integer [0,20]TTL_START integer [1,40]TTL_INCREMENT integer [1,20]TTL_THRESHOLD integer [1,60]


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Initialization:Uniforminitializationtoassurethattheinitialpopulationcontainsindividualsfromdifferent areas of the search space. It splits the search space into pop_size(numberofindividuals)diagonalsubspaces.

Crossover:WeusedBlendCrossoverOperator(BLX‐α),awell‐knownrecombinationoperatorforrealcodedEAs

2.Power‐AwareAODV.OptimizationMethodDetails

x

y


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RealisticVANETsimulationsshouldreflectrealworldinteractions.Inthissense,stochastic signal propagationmodels are used. In ourwork,we have used theNakagamipropagationmodel.Thesimulationresultsarenon‐deterministic(deviationsupto57%)

Monte‐Carlosimulation isusedtoperformmoreaccuratefitnessevaluations.EnergyandPDRarecomputedrepeatingntimesthesimulation

2.Power‐AwareAODV.FitnessEvaluation

nprocessingunits

Perform VANET simulations

Computes the fitness by using Monte-Carlo method

MASTER

SLAVE SLAVE SLAVE

F( )

Asns‐2 VANET simulation require large execution times (minutes)we appliedtheparallelmultithreadingmaster‐slaveparadigmtorunthensimulationsovernprocessingunits

Reducingdrasticallytherequiredtimetoevaluatethesolutionfitness

Inourwork,n=24


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VANETscenariosdefinition: Geographicalareas:

G1:240,000m2

G2:360,000m2

Numberofnodes:G1:20G2:30,45,and60

Generateddatatrafficrate: 128,256,512,and1024kbps

OptimizationscenarioG1,20,512kbps

DE(C++,MALLBA,andstandardpthreadlibrary): Populationsize=8individuals Numberofgenerations=50 Crossover:probability=0.9,α=0.2

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• ExperimentsDefinition• DEOptimizationResults• ValidationResults

G2

G1

N

MÁLAGA

3.ExperimentalAnalysis.ExperimentsDefinition

AberperformingDEconfigura&onanalysisexperimentsusingG1,20,128kbpsscenariotofindthebestDEparameteriza&on






3.ExperimentalAnalysis.DEOptimizationResults

Bestconfigurationfound: HELLO_INTERVAL=11.994,ACTIVE_ROUTE_TIMEOUT=12.439, MY_ROUTE_TIMEOUT=15.965,NODE_TRAVERSAL_TIME=8.106, MAX_RREQ_TIMEOUT=42.466,NET_DIAMETER=66, ALLOWED_HELLO_LOSS=6,REQ_RETRIES=9,TTL_START=12, TTL_INCREMENT=19,andTTL_THRESHOLD=54

Mainproperties: itgenerateslowercontroltraffic eachnodespendslesstimeinthetransmittingandthereceivingstates itmanageslongerpathssincethenetworkdiameterandthetimetolivearelarger itismoretoleranttodisconnectionsandpacketlossbecauseitallowsgreaterhellopacketlossanditresendRREQpacketsmoretimes

The average power consumption obtained with the optimized AODVconfiguration reduced 33.55% the RFC energy requirements, while thePDRdegradationwasonly5.17%


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3.ExperimentalAnalysis.ValidationResults

Bestenergy‐awareAODVconfigurationvsRFC3561over9scenariosinG2area Results:

Averagepowerconsumptionsaving=32.3%/averagePDRdegradation=8.8% Largestenergyreductioningreatestroadtrafficdensity(60nodes,max=68.9%)

SuffersfromseveraldropinthePDR Lowestenergysavingsinscenarioswiththelargestdatarates(1024Kbps) Kruskal‐Wallis statistical test results indicate that the energy improvementscanbeconsideredstatisticallysignificant

34%26%

19%

50%

00%

10%

20%

30%

40%

50%

G1_30 G2_30 G2_45 G2_60

energyim

provem

ent

overRFC

5% 7% 5%

18%

0%

10%

20%

30%

40%

50%

G1_30 G2_30 G2_45 G2_60

PDRde

grad

a4on

overRFC







4.ConclusionsandFutureWork

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Automaticmethodologyforcomputingpower‐awareAODVconfigurationsforVANETs,bycouplingDEandMonte‐Carlomethod(ns‐2simulations) Maincontribution:

DEandaparallelmaster‐slaveMonte‐Carlo simulation to reduce thevariabilityoftheconfigurationevaluationduetorandomnessinNakagamimodel

Mainresults:Significant reductions in power consumption when using the DE AODV energy‐awareconfiguration(32.3%average,68.9%best);averagePDRdegradation:8.8%

PromisingmethodologyforautomaticandefficientcustomizationofVANETroutingprotocols

Futurework: improvethesearchtoreducetheQoSdegradationindensescenarios useotherfitnessfunctions(newmetrics,explicitmulti‐objectiveapproaches) parallelizethepopulationevaluationtoallowlargerpopulationsinDE improvetheresultsbyusingseveralscenariostoevaluateeachconfiguration






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evolutionary power‐aware routing in vanets using...

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