Ahmed Mostefaoui - LIMOS - Accueil · (MQTT-CV). • Samir Chouali, Azzedine Boukerche, Ahmed...

AhmedMostefaoui

Maîtredeconférences(HDR)eninforma6queUniversitédeFranche-Comté

Ins6tutFEMTO-ST,Dep.DISC,UMRCNRS6174

[email protected]

Vued’ensemblesurmestravauxderecherche(5dernièresannées)

Réseauxdecapteursscalaires

•  Routagelocalisé:ladécisionduroutageestpriselocalementenu6lisantuniquement

l’informa6on1-hop.•  AhmedMostefaoui,MahmoudMelkemi,AzzedineBoukerche:“LocalizedRou6ngApproachtoBypassHolesinWireless

SensorNetworks.”IEEETrans.Computers63(12):3053-3065(2014)•  AhmedMostefaoui,MahmoudMelkemi,AzzedineBoukerche:“Rou6ngthroughholesinwirelesssensornetworks.”MSWiM

2012:395-402

•  Fusiondedonnées:approchessérialiséesplusefficacesmaispluscomplexesàdévelopper.•  MohammedAmineMerzoug,AzzedineBoukerche,AhmedMostefaoui,SamirChouali:“SpreadingAggrega6on:Adistributed

collision-freeapproachfordataaggrega6oninlarge-scalewirelesssensornetworks.”J.ParallelDistrib.Comput.125:121-134(2019)

•  MohammedAmineMerzoug,AzzedineBoukerche,AhmedMostefaoui:“Efficientinforma6ongatheringfromlargewirelesssensornetworks.”ComputerCommunica6ons132:84-95(2018)

•  AzzedineBoukerche,AhmedMostefaoui,MahmoudMelkemi:“Efficientandrobustserialqueryprocessingapproachforlarge-scalewirelesssensornetworks.”AdHocNetworks47:82-98(2016)

•  AhmedMostefaoui,AzzedineBoukerche,MohammedAmineMerzoug,MahmoudMelkemi:“AscalableapproachforserialdatafusioninWirelessSensorNetworks.”ComputerNetworks79:103-119(2015)

Vued’ensemblesurmestravauxderecherche(suite…)

RéseauxdecapteursmulAmédias

•  Contrôledistribué:contrôler,demanièredistribuée,ledébitdesdonnéesvidéotoutentenantcomptedescontraintesduréseaux(consomma6onénergé6que,qualitédesliens,etc.).

•  NesrineKhernane,Jean-FrançoisCouchot,AhmedMostefaoui:“Op6malpower/ratetrade-offforinternetofmul6mediathingslife6memaximiza6onunderdynamiclinkscapacity.”FutureGenera6onComp.Syst.93:737-750(2019)

•  NesrineKhernane,Jean-FrançoisCouchot,AhmedMostefaoui:“Maximumnetworklife6mewithop6malpower/rateandrou6ngtrade-offforWirelessMul6mediaSensorNetworks.”ComputerCommunica6ons124:1-16(2018)

•  SécurisaAon:sécuriserlesimagestransmisesenpréservantlesressourcesduréseau•  AhmedMostefaoui,ZeinabFawaz,HassanN.Noura:“Arobustimage-encryp6onapproachagainsttransmissionerrorsin

Communica6ngThingsNetworks.”AdHocNetworks94(2019)•  AhmedMostefaoui,HassanN.Noura,ZeinabFawaz:“Anintegratedmul6mediadatareduc6onandcontentconfiden6ality

approachforlimitednetworkeddevices.”AdHocNetworks32:81-97(2015)

•  Couverturepériphérique:ordonnancerl’ac6vitédesnœudsdetellesorteàassurerunecouverturepériphérique.

•  AmorLalama,NesrineKhernane,AhmedMostefaoui:“ClosedPeripheralCoverageinWirelessMul6mediaSensorNetworks.”MobiWac2017:121-128


Réseauxdevéhiculesconnectésetmobilités(encollaboraAonavecPSAPeugeotCitröen) •  Bigdata:accéléra6onetpassageàl’échelledesapplica6onsV2I.

•  AmirHaroun,AhmedMostefaoui,FrançoisDessables:“Datafusioninautomo6veapplica6ons.”PersonalandUbiquitousCompu6ng21(3):443-455(2017)

•  AmirHaroun,AhmedMostefaoui,FrançoisDessables:“ABigDataArchitectureforAutomo6veApplica6ons:PSAGroupDeploymentExperience.”CCGrid2017:921-928

•  AnthonyNassar,AhmedMostefaouiandFrançoisDessables:“Improvingbid-dataautomo6veapplica6onsperformancethroughadap6veresourcealloca6on”.IEEESymposiumonComputersandCommunica6ons2019:49-55Barcelona

•  ProtocoledecommunicaAon:développementetvérifica6ondeprotocolesspécifiques(MQTT-CV).

•  SamirChouali,AzzedineBoukerche,AhmedMostefaoui:“TowardsaFormalAnalysisofMQsProtocolintheContextofCommunica6ngVehicles.”Inthe15thACMMOBIWAC2017,Miami,FL,USA,November21-25,2017.ACM2017,pages:129-136

•  SamirChouali,AzzedineBoukerche,AhmedMostefaoui:“EnsuringtheReliabilityofanAutonomousVehicle:AFormalApproachbasedonComponentInterac6onProtocols.”Inthe20thACMIMSWiM2017,Miami,FL,USA,November21-25,2017,pages:317-321,BestShortPaperAward.

•  Villeconnectéeetmobilité:cartographietempsréeldesplacesdesta6onnementdisponiblesauniveaud’uneville.

•  MohammedAmineMerzoug,AhmedMostefaoui,AbderrezakBenyahia:“SmartIoTNo6fica6onSystemforEfficientIn-CityParking.”ACMQ2SWinet2019:37-42


RéseauxWBAN

•  MainAendespersonnesâgéesàdomicile:systèmeintégréd’aideetd’alerteencasde

malaise.•  MoustafaFayad,AhmedMostefaoui,SamirChouali,SalimaBenbernou“FallDetec6onApplica6onfortheElderlyinthe

FamilyHeroesSystem.”ACMMobiWac2019:17-23

ScalableApproachesforSerialDataFusioninWirelessSensorNetworks

A.Mostefaoui1,A.Boukerche2,M.A.Merzoug1andM.Melkemi21UniversityofFranche-Comté,France2UniversityofOsawa,Canada.3UniversityofhauteAlsace,France

QueryProcessinginWSNsWirelesssensornetworks

Objec6ve:answerquerieslike:

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SELECT AVR (temp), MAX (hum), MIN (light), COUNT() FROM sensors

QueryProcessinginWSNs

•  Warehouseapproaches:rawdataisfirstsenttothesinkbeforequeryprocessing;i.e.,twoindependentprocesses.–  Queryprecision.–  Overu6liza6onofthenetworkresourcesandpoorscalability.

•  In-networkcentralizedapproaches:atreerootedatthesinkisconstructedanddataisaggregatedinintermediatenodes.–  Reducedu6liza6onofnetworkresourcesincomparisontowarehouseapproach,

–  Vulnerabilityandpoorscalability.

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•  Distributedapproaches:deriveanes6mateofaparameterorfunc6onofinterestfromraw(sensed)data.Thees6mateissuccessively(i.e.,itera6vely)carriedoutthroughlocalcomputa6onsfromtheexchangeddatabetweenimmediateneighbors.

–  Nocentralbasesta6onisrequired,–  Mul6-hopcommunica6onsareavoided(noneedtomaintainroo6ng

data),–  Robustnessandgoodscalability.

–  Importantcommunica6onsconsump6ontoreachtheconvergence,–  Queryresponse6mepar6cularlyhighduetotheiritera6venatureon

onehandandtothenumberofpacketcollisiontheygenerateontheotherhand.

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•  Serialapproaches:thees6mateissuccessively(i.e.,serially)updatedfromnodetonodeun6lallnodesinthenetworkarevisited.Thelastnodeholdstherightes6mate.

–  Veryefficientintermsofreducingcommunica6onscomparedtocentralizedanddistributedapproaches.

–  Requiretheconstruc6onofanHamiltonianpaththroughthenetwork(KnowntobeNP-Completeproblem),

–  Thecostoffindingsuchapath,ina“decentralized”mannertoensurescalability,isveryhigh,

–  Highvulnerability

10M.RabbatandR.Nowak.“Quan6zedincrementalalgorithmsfordistributedop6miza6on”.IEEEJournalonSelectedAreasinCommunica6ons,23(4):798-808,2005.


Ques6on:howcouldserialapproachesmeetWSNsrequirements(i.e.,completeness,scalabilityandrobustness)whilstmaintainingtheirperformances?

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Outline

•  Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on

•  ProofofCorrectness•  EnhancedPA•  PerformanceEvalua6on•  ConclusionandFutureWorks

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PeelingAlgorithme•  Requirements:

–  Serialnature,–  DecentralizedandLocalized:noextra-knowledgethanwhatisalreadyavailable(i.e.,

informa6onaboutimmediateneighborsandtheirloca6ons)isusedinordertomeetscalabilityrequirement,

–  QueryCompleteness:allnodescontributeinthequery;i.e.,itshouldvisitallnodesinthenetwork,

–  Randomtopology:couldhandleallnetworktopologies.•  Assump6ons:

–  Nodesknowtheirgeographicalloca6onsandthoseoftheirimmediateneighbors,–  Allnodesareconnected(networkconnec6vity),–  TheQueryIni6atorNode(QIN)couldbeanynodeinthenetwork(owenthesink),–  UDGModel

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PeelingAlgorithme

Defini6ons: 14

PeelingAlgorithme

BoundaryTraversalAlgorithm:wheneverstartedfromaboundarynode,allvisitednodesbelongtothesameboundary.

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A.Mostefaoui,M.MelkemiandA.Boukerche“LocalizedRou-ngApproachtoBypassHolesinWirelessSensorNetworks”IEEETransac6onsonComputers,63(12):3053—3065,Sep.2014

Starting Points

Boundary

CSN1

N2

N3

N4

Outline



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PeelingAlgorithme

Overview:

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Wellsuitablefordenseandholefreetopologies

PeelingAlgorithme

Problem:unvisitedregiondisconnec6vity

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PeelingAlgorithme

Solu6on:bridgenode

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PeelingAlgorithme

Problem:ar6ficialholesSolu6on:deac6vatedlinks

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PeelingAlgorithme

Hole’sProblem:Solu6on:breakingthehole

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PeelingAlgorithme

Solu6on:HGN

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PeelingAlgorithme

Solu6on:breakingholes

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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

PeelingAlgorithme


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Star6ngNode

HGN

EndingNode

PeelingAlgorithme

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Previous Node

1

2

34

10

11

12

1314 15

16

17

18

59

87

6

False HGN

True HGN

Broken links Hole 1

Hole 2

(a)

(b)

1

2

34

10

11

12

1314 15

16

17

18

9

87

6

Broken links Hole 1

Hole 2

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Hole Control Packet (HCP)

Problem:imbricatedholes(FalseHGN)

Solu6on:HoleControlPacketlaunchedbytheHGN

Outline



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PeelingAlgorithme

•  Star6ngNodeDetec6onProcess•  Iden6fyingNBNsset

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A.Mostefaoui,M.MelkemiandA.Boukerche“EfficientAlgorithmforSerialDataFusioninWirelessSensorNetworks.”Inthe16thACMInterna6onalConferenceonModeling,AnalysisandSimula6onofWirelessandMobileSystems,MSWiM'13,Pages181--188,Barcelone,Nov.2013

Outline



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Proofofcorrectness

•  Objec6ve:provethatPAguaran6esallnodescontribu6oninthequery(i.e.,visitsallnodes)whatevertheconfigura6onofthenetworkis.

•  Twosteps:–  Step1:provethatPAterminates(freeoflooping).–  Step2:provethatthegeneratedsequenceofvisitednodes

containsallnodesofthenetwork.

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Proofofcorrectness

Step1:

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Proofofcorrectness

Step1:

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Proofofcorrectness

Step1:

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Proofofcorrectness

Step2:

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Outline



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EnhancedPA

Linearityphenomenon

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EnhancedPA

Cyclicnodes

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Outline



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PerformanceEvaluaAon

•  ComparedApproaches:–  Centralizedapproach–  Itera6veapproach–  DepthFirstApproach–  PA–  EPA

•  Metrics:–  Communica6onEfficiency(#hops)–  ConsumedEnergy,–  QueryTime-To-End(i.e.,queryresponsiveness)

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•  Se{ngs:

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•  Singlequeryperformance:

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0

500

1000

1500

2000

2500

3000

100 150 200 250 300 350 400 450 500

Num

ber o

f Com

mun

icat

ions

Number of Nodes

IterativeCentralized

DFSPeeling

0

5

10

15

20

25

30

35

40

45

100 150 200 250 300 350 400 450 500

Ener

gy C

onsu

mpt

ion

(J)

Number of Nodes


DFSPeeling

0

5

10

15

20

25

30

100 150 200 250 300 350 400 450 500

One

Que

ry R

espo

nse

Tim

e (s

)

Number of Nodes


DFSPeeling

0

2

4

6

8

10

12

14

100 150 200 250 300 350 400 450 500

One

Que

ry R

espo

nse

Tim

e (s

)

Number of Nodes

DFS with MACDFS without MACPeeling with MAC

Peeling without MAC


•  Mul6plequeries:

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0

100

200

300

400

500

600

700

800

100 150 200 250 300 350 400 450 500

Net

wor

k Li

fetim

e (N

umbe

r of Q

uerie

s)

Number of Nodes


DFSPeeling


•  EPAperformance:

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0

200

400

600

800

1000

100 150 200 250 300 350 400 450 500

Num

ber o

f Com

mun

icat

ions

Number of Nodes

DFSPA

EPA

0

1

2

3

4

5

6

7

100 150 200 250 300 350 400 450 500

Ener

gy C

onsu

mpt

ion

(J)

Number of Nodes

DFSPA

EPA

0

2

4

6

8

10

12

100 150 200 250 300 350 400 450 500

One

Que

ry R

espo

nse

Tim

e (s

)

Number of Nodes

DFSPA

EPA

Outline



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ConclusionandFutureWork

•  EfficientandScalableserialapproachfordatafusioninWSN.

•  Theore6callyproventoensurequerycompleteness(i.e.,visi6ngallnodes)inanynetworkconfigura6on.

•  Suitablefordenseandlargescaledeployments.

•  S6llvulnerabletolinkandnodesfailures

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MercipourvotreaUenAon…

PeelingAlgorithme

Overview:

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vv

Star6ngNode

ExternalBoundary

PeelingAlgorithme

Overview:

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vv

Star6ngNode

Currentunvisitedregionboundary

PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

Star6ngNode


PeelingAlgorithme

Overview:

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vv

EndingNode

Star6ngNode

Wellsuitablefordenseandholefreetopologies

Ahmed Mostefaoui - LIMOS - Accueil · (MQTT-CV). • Samir Chouali, Azzedine Boukerche, Ahmed...

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