Nature-Inspired Networking : Theory and Applications

389 Pages·2018·9.217 MB·English

by  Cong-Vinh, Phan

Nature-Inspired
Networking

PhanCongVinh
Nature-Inspired
Networking: Theory and
Applications
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Title:Nature-inspirednetworking:theoryandapplications/editedbyPhan
CongVinh.
Description:BocaRaton:Taylor&Francis,CRCPress,2018.|Includes
bibliographicalreferencesandindex.
Identifiers:LCCN2017044928|ISBN9781498761505(hb:alk.paper)
Subjects:LCSH:Ad-hocnetworks(Computernetworks)|Neuralnetworks
(Computerscience)|Biomimicry.
Classification:LCCTK5105.77.N382018|DDC004.6–dc23
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Contents
ListofFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi
ListofContributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii
1 AlgebraicAspectsofAutonomicSystems . . . . . . . . . . . . . . 1
PhanCongVinh
2 Bio-CommunitiesCommunicationsParadigmsforVehicular
SocialNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
FrancescoChiti,EnricoDei,andRomanoFantacci
3 ApplicationofNatureInspiredAlgorithmsforWireless
Multi-hopAdHocNetworkOptimizationProblemsinDisaster
ResponseScenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Jesu´sSa´nchez-Garc´ıa,Jose´ ManuelGarc´ıa-Campos,
DanielGutie´rrezReina,SergioLuisToral,andFedericoBarrero
4 IntroducingNovaGenesisasaNovelDistributed
System-BasedConvergentInformationArchitecture . . . . . . . . 89
AntonioMarcosAlberti,MarcoAurelioFavoretoCasaroli,
RodrigodaRosaRighi,andDhananjaySingh
v
vi (cid:4) Contents
5 CollectiveIntelligenceinNetworking . . . . . . . . . . . . . . . . 145
RatneshwerandVandanaKushwaha
6 QoSRouteSearchforMobileAdHocNetworkUsing
GeneticAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
J.Abdullah
7 NetworkFlowsunderThermalRestrictions . . . . . . . . . . . . . 231
SamikshaSarwariandShrishaRao
8 Bio-InspiredSolutionsandNetworkonChip(NoC)Fault
TolerantAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 261
MuhammadAtharJavedSethi,FawnizuAzmadiHussin,and
NorHishamHamid
9 Bio-InspiredNetworkonChip(BNOC) . . . . . . . . . . . . . . . 309
MuhammadAtharJavedSethi,FawnizuAzmadiHussin,and
NorHishamHamid
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
List of Figures
2.1 Potential use cases from a data exchanging and networking
perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.2 Bacteriallifecyclemainphases . . . . . . . . . . . . . . . . . 40
2.3 Vehicle life cycle main phases representation via finite state
machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.4 End-to-enddeliveringlatencyδ asafunctionofThforbothan
analyticalmodelandasimulatedscenario. . . . . . . . . . . . 43
3.1 Multi-hopadhocroutingalgorithmsclassification . . . . . . . 59
3.2 Genericfitnessorobjectivefunction. . . . . . . . . . . . . . . 62
3.3 HillClimbingalgorithm. . . . . . . . . . . . . . . . . . . . . 63
3.4 SimulatedAnnealingalgorithm . . . . . . . . . . . . . . . . . 64
3.5 TabuSearchalgorithm. . . . . . . . . . . . . . . . . . . . . . 65
3.6 Evolutionaryalgorithm. . . . . . . . . . . . . . . . . . . . . . 66
3.7 EvolutionofGeneticalgorithms. . . . . . . . . . . . . . . . . 67
3.8 Geneticinformationofanindividualorpotentialsolution . . . 67
3.9 Singlecrossoveroperation. . . . . . . . . . . . . . . . . . . . 68
3.10 Mutationoperation. . . . . . . . . . . . . . . . . . . . . . . . 69
3.11 ParticleSwarmOptimizationAlgorithm. . . . . . . . . . . . . 71
3.12 AntColonyOptimizationAlgorithm. . . . . . . . . . . . . . . 72
4.1 TCP/IP layered protocol communication between two end
point devices. Functionally, intermediate nodes only require
thebottomthreelayersofthereferencemodel . . . . . . . . . 91
4.2 Informationarchitectureandtheirrelativescopes. . . . . . . . 95
vii
viii (cid:4) ListofFigures
4.3 People attribute “weak semantics” and meaningful names to
physical (e.g. a car or a house) and virtual existences (a com-
puter program or a file). If they are unique in some scope,
they can be used as identifiers and locators. Therefore, bind-
ingsamongnames(orname-bindings)cancaptureallsortsof
relationships among virtual and physical existences. They can
representsemanticrelationshipslike“contains,”“iscontained,”
or“closeto.”Inthisexamplescenario,thecaris“closeto”the
house,and“contains”thetabletandsmartphone.Also,theper-
son“iscontained”inthecar. . . . . . . . . . . . . . . . . . . 97
4.4 NG architecture overview. The arrows in the figure illustrate
coreservicesinterrelationshipsandtheirmessageexchanging.
A PGS represents hosting resources, which are negotiated to
other components, enabling the emergence of distributed for-
wardingandroutingservices.PGSsstartoperationbysending
a “hello” message to other peers. After the “hello,” they can
transmitmessagesdestinedtootherPGSs.VFSsestablishvir-
tual links over PGSs. Continuing, VRSs can contract VFSs to
build their routing tables. If they don’t know how to route a
message, they subscribe the names on the header of the NG
messageuntiltheyfindoutapath.Thesethreenetworkingser-
vices (PGS, VFS, and VRS) follow a bottom-up paradigm to
create a communication network, where protocols are imple-
mentedasservices(namedhereasPIaaS).Thecommunication
network can be a simulated overlay network, an experimen-
taloverlaynetwork,orarealexclusiveNGnetwork,withNG
hardware. NBs are published/subscribed via PSS. The GIRS
selects the appropriate HTS instance to store a certain NB.
These three pub/sub services (PSS, GIRS, and HTS) provide
pub/subfunctionalitytootherservices.Finally,theSDS,RpS,
and RgS prepare the environment for semantics rich service
orchestration,aimedathighlevelapplicationdevelopment. . . 102
4.5 Map of NG terminology and relationships. Things are clas-
sified into physical or virtual. All individual existences can
haveattributednames,whichcanbeusedasidentifiersand/or
locators. An NB links two or more names. The virtual world
encompasses content and services, which are used to imple-
ment NG. The services form a distributed system, which has
instancesintoseveraloperatingsystems. . . . . . . . . . . . . 106
4.6 ExampleofNGarchitectureimplementationonaLinuxOS. . 107
4.7 The sequence diagram of NG core services bootstrapping in
thecurrentimplementation. . . . . . . . . . . . . . . . . . . . 108
5.1 Aframeworkforcollectiveintelligence. . . . . . . . . . . . . 150
ListofFigures (cid:4) ix
5.2 Classificationofcollectiveintelligencesystems. . . . . . . . . 151
6.1 Differenttypesofmultipleroutes . . . . . . . . . . . . . . . . 186
6.2 Routesreliability. . . . . . . . . . . . . . . . . . . . . . . . . 187
6.3 Mobileadhocnetwork. . . . . . . . . . . . . . . . . . . . . . 192
6.4 Procedureforgeneticalgorithm. . . . . . . . . . . . . . . . . 195
6.5 ChromosomerepresentationofroutefromStoT. . . . . . . . 195
6.6 Crossoveroperation . . . . . . . . . . . . . . . . . . . . . . . 198
6.7 Schemesformultipleroutesmutation . . . . . . . . . . . . . 200
6.8 (a)Tournamentselectionalgorithm.(b)SUSalgorithm. . . . . 203
6.9 Plotofaverageminimumcostforvariousselectionmethods. . 206
6.10 Plot of C as a function of population size—tournament,
AMC
andelitism(0.05). . . . . . . . . . . . . . . . . . . . . . . . . 207
6.11 Lowestcostagainstgenerationfortournamentselection . . . . 208
6.12 Transmissionefficiencyasafunctionofcrossoverprobability. 209
6.13 Transmissionefficiencyasafunctionofmutationprobability. . 209
6.14 Theinitialsetupofsimulationenvironmentwith40nodes . . . 212
6.15 APDRagainstthesourcetrafficrates . . . . . . . . . . . . . . 215
6.16 Averageend-to-enddelayagainstsourcetrafficrate. . . . . . . 215
6.17 Averagethroughputasafunctionofsourcestrafficrate. . . . . 216
6.18 Meanpacketdeliveryratioasafunctionofmaximumvelocity. 217
6.19 Averagepacketdelayasafunctionofmaximumvelocity. . . . 218
6.20 Averagetotalthroughputagainstmobilitylevel. . . . . . . . . 219
6.21 Averagepacketdeliveryratioagainstnodedensity . . . . . . . 220
6.22 Averageend-to-enddelayagainstnodedensity . . . . . . . . . 221
6.23 Averagenormalizedroutingloadagainstnodedensity . . . . . 222
6.24 Averagetotalthroughputagainstnodedensity. . . . . . . . . . 222
6.25 Averagepacketdeliveryratioasafunctionofcongestionlevel. 223
6.26 Averageend-to-enddelayagainstcongestionlevel . . . . . . . 224
6.27 Totalaveragethroughputagainstthecongestionlevel. . . . . . 225
6.28 Average normalized routing load as a function of congestion
level. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225
8.1 NetworkonChip(NoC). . . . . . . . . . . . . . . . . . . . . 264
8.2 NoCwithheterogeneousresources . . . . . . . . . . . . . . . 265
8.3 NoCtorusandtreetopology. . . . . . . . . . . . . . . . . . . 265
8.4 NoCringandirregulartopology . . . . . . . . . . . . . . . . 266
8.5 Biologicalneuron . . . . . . . . . . . . . . . . . . . . . . . . 272
8.6 Biologicalsynapse. . . . . . . . . . . . . . . . . . . . . . . . 273
8.7 Synaptogenesisconcept . . . . . . . . . . . . . . . . . . . . . 274
8.8 Sproutconcept. . . . . . . . . . . . . . . . . . . . . . . . . . 275
8.9 GTandBEservicesmechanism. . . . . . . . . . . . . . . . . 277
8.10 Bio-inspiredNoCarchitecture. . . . . . . . . . . . . . . . . . 279
8.11 Portarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . 279
8.12 ISpacketformat . . . . . . . . . . . . . . . . . . . . . . . . . 281