Post on 02-Aug-2020
AhmedMostefaoui
Maîtredeconférences(HDR)eninforma6queUniversitédeFranche-Comté
Ins6tutFEMTO-ST,Dep.DISC,UMRCNRS6174
Ahmed.mostefaoui@univ-fcomte.fr
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
Vued’ensemblesurmestravauxderecherche(suite…)
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
Vued’ensemblesurmestravauxderecherche(suite…)
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:
7
SELECT AVR (temp), MAX (hum), MIN (light), COUNT() FROM sensors
QueryProcessinginWSNs
• Warehouseapproaches:rawdataisfirstsenttothesinkbeforequeryprocessing;i.e.,twoindependentprocesses.– Queryprecision.– Overu6liza6onofthenetworkresourcesandpoorscalability.
• In-networkcentralizedapproaches:atreerootedatthesinkisconstructedanddataisaggregatedinintermediatenodes.– Reducedu6liza6onofnetworkresourcesincomparisontowarehouseapproach,
– Vulnerabilityandpoorscalability.
8
QueryProcessinginWSNs
• Distributedapproaches:deriveanes6mateofaparameterorfunc6onofinterestfromraw(sensed)data.Thees6mateissuccessively(i.e.,itera6vely)carriedoutthroughlocalcomputa6onsfromtheexchangeddatabetweenimmediateneighbors.
– Nocentralbasesta6onisrequired,– Mul6-hopcommunica6onsareavoided(noneedtomaintainroo6ng
data),– Robustnessandgoodscalability.
– Importantcommunica6onsconsump6ontoreachtheconvergence,– Queryresponse6mepar6cularlyhighduetotheiritera6venatureon
onehandandtothenumberofpacketcollisiontheygenerateontheotherhand.
9
QueryProcessinginWSNs
• 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.
QueryProcessinginWSNs
Ques6on:howcouldserialapproachesmeetWSNsrequirements(i.e.,completeness,scalabilityandrobustness)whilstmaintainingtheirperformances?
11
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
12
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
13
PeelingAlgorithme
Defini6ons: 14
PeelingAlgorithme
BoundaryTraversalAlgorithm:wheneverstartedfromaboundarynode,allvisitednodesbelongtothesameboundary.
15
A.Mostefaoui,M.MelkemiandA.Boukerche“LocalizedRou-ngApproachtoBypassHolesinWirelessSensorNetworks”IEEETransac6onsonComputers,63(12):3053—3065,Sep.2014
Starting Points
Boundary
CSN1
N2
N3
N4
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
16
PeelingAlgorithme
Overview:
17
Wellsuitablefordenseandholefreetopologies
PeelingAlgorithme
Problem:unvisitedregiondisconnec6vity
18
PeelingAlgorithme
Solu6on:bridgenode
19
PeelingAlgorithme
Problem:ar6ficialholesSolu6on:deac6vatedlinks
20
PeelingAlgorithme
Hole’sProblem:Solu6on:breakingthehole
21
PeelingAlgorithme
Solu6on:HGN
22
PeelingAlgorithme
Solu6on:breakingholes
23
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
24
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
25
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
26
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
27
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
28
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
29
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
30
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
31
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
32
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
33
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
34
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
35
Star6ngNode
HGN
PeelingAlgorithme
Solu6on:breakingholes
36
Star6ngNode
HGN
EndingNode
PeelingAlgorithme
37
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
5
Hole Control Packet (HCP)
Problem:imbricatedholes(FalseHGN)
Solu6on:HoleControlPacketlaunchedbytheHGN
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
38
PeelingAlgorithme
• Star6ngNodeDetec6onProcess• Iden6fyingNBNsset
39
A.Mostefaoui,M.MelkemiandA.Boukerche“EfficientAlgorithmforSerialDataFusioninWirelessSensorNetworks.”Inthe16thACMInterna6onalConferenceonModeling,AnalysisandSimula6onofWirelessandMobileSystems,MSWiM'13,Pages181--188,Barcelone,Nov.2013
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
40
Proofofcorrectness
• Objec6ve:provethatPAguaran6esallnodescontribu6oninthequery(i.e.,visitsallnodes)whatevertheconfigura6onofthenetworkis.
• Twosteps:– Step1:provethatPAterminates(freeoflooping).– Step2:provethatthegeneratedsequenceofvisitednodes
containsallnodesofthenetwork.
41
Proofofcorrectness
Step1:
42
Proofofcorrectness
Step1:
43
Proofofcorrectness
Step1:
44
Proofofcorrectness
Step2:
45
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
46
EnhancedPA
Linearityphenomenon
47
EnhancedPA
Cyclicnodes
48
Outline
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
49
PerformanceEvaluaAon
• ComparedApproaches:– Centralizedapproach– Itera6veapproach– DepthFirstApproach– PA– EPA
• Metrics:– Communica6onEfficiency(#hops)– ConsumedEnergy,– QueryTime-To-End(i.e.,queryresponsiveness)
50
PerformanceEvaluaAon
• Se{ngs:
51
PerformanceEvaluaAon
• Singlequeryperformance:
52
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
IterativeCentralized
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
IterativeCentralized
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
PerformanceEvaluaAon
• Mul6plequeries:
53
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
IterativeCentralized
DFSPeeling
PerformanceEvaluaAon
• EPAperformance:
54
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
• Ourapproach:PeelingAlgorithm(PA)– BoundaryTraversalAlgorithm– PAOverview– Star6ngNodeDetec6on
• ProofofCorrectness• EnhancedPA• PerformanceEvalua6on• ConclusionandFutureWorks
55
ConclusionandFutureWork
• EfficientandScalableserialapproachfordatafusioninWSN.
• Theore6callyproventoensurequerycompleteness(i.e.,visi6ngallnodes)inanynetworkconfigura6on.
• Suitablefordenseandlargescaledeployments.
• S6llvulnerabletolinkandnodesfailures
56
MercipourvotreaUenAon…
PeelingAlgorithme
Overview:
58
vv
Star6ngNode
ExternalBoundary
PeelingAlgorithme
Overview:
59
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
60
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
61
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
62
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
63
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
64
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
65
vv
Star6ngNode
Currentunvisitedregionboundary
PeelingAlgorithme
Overview:
66
vv
EndingNode
Star6ngNode
Wellsuitablefordenseandholefreetopologies