Ant Colony Optimization in Ad Hoc Networks: A Comprehensive Survey

In the interlinked globe of present-day, rapid transformation of technology presents numerous challenges. The development of smart cities, equipped with intelligent appliances, is essential for ef icient management of communication between machines and humans. Networking plays a pivotal role in building smart cities with advanced devices, and optimizing performance remains a critical concern. In this review article, Weconcentrate on a specific type of wireless network - ad hoc networks and analyze current research onanature-inspired algorithm, Ant Colony Optimization (ACO), to enhance their ef iciency. This paper provides an extensive review of the use of Ant Colony Optimization or ACO applied to MANETs, andVANETs, moreover FANETs from 2021 to 2024. Additionally, it examines the characteristics andapplications of various ad hoc networks, provides a detailed discussion on the Ant Colony Optimizationalgorithm, and highlights key challenges along with future research directions. It is hoped that this paper will be a useful resource for scholars in this area.

1. InternationalJournalofWireless&MobileNetworks(IJWMN),Vol.17,No.4,August2025
DOI:10.5121/ijwmn.2025.17405 55
ANTCOLONYOPTIMIZATIONINADHOC
NETWORKS:ACOMPREHENSIVESURVEY
PromeSahaResha
1
,HridoyChandraDas
2
andDulalChakraborty
1
1
DepartmentofInformationandCommunicationTechnology,ComillaUniversity,
Cumilla,Bangladesh
2
DepartmentofArtificialIntelligenceforIndustrialApplications,Ostbayerische
TechnischeHochschuleAmberg-WeidenUniversity,Amberg,WeideninderOberpfalz,
Bavaria,Germany
ABSTRACT
Intheinterlinkedglobeofpresent-day,rapidtransformationoftechnologypresentsnumerouschallenges.
Thedevelopmentofsmartcities,equippedwithintelligentappliances,isessentialforefficientmanagement
ofcommunicationbetweenmachinesandhumans.Networkingplaysapivotalroleinbuildingsmartcities
withadvanceddevices,andoptimizingperformanceremainsacriticalconcern.Inthisreviewarticle,We
concentrateonaspecifictypeofwirelessnetwork-adhocnetworksandanalyzecurrentresearchona
nature-inspiredalgorithm,AntColonyOptimization(ACO),toenhancetheirefficiency.Thispaper
providesanextensivereviewoftheuseofAntColonyOptimizationorACOappliedtoMANETs,and
VANETs,moreoverFANETsfrom2021to2024.Additionally,itexaminesthecharacteristicsand
applicationsofvariousadhocnetworks,providesadetaileddiscussionontheAntColonyOptimization
algorithm,andhighlightskeychallengesalongwithfutureresearchdirections.Itishopedthatthispaper
willbeausefulresourceforscholarsinthisarea.
KEYWORDS
Smartcity,MANET,VANET,FANET,ACO,Optimization,Ant,Routing
1.INTRODUCTION
By2050,theglobalpopulationisexpectedtoriseby70%,leadingtomigrationintosmalltowns
andabandonedcities.Tosupportthisgrowth,smartcitieswillintegrateelectronicdeviceslike
connectedsensors,intelligenttransport,cloud/fogtechnologies,service-basedmiddlewarefor
sustainabilityandefficiency[1].Safer,connected,andeco-friendlycitiesthroughefficient
management[2].MobileAdHocNetworks(MANETs),despitetheirinitialdesignformilitary
communications,nowplayakeyroleinseamlessconnectivityandresourceoptimizationin
urbanenvironments[3][4].Additionally,VehicularAdHocNetworks(VANETs)enhance
autonomousdrivingandtrafficmanagement,improvingroadsafetyandcongestion[5].Flying
AdHocNetworks(FANETs)utilizeUAVsforaerialcoordination,emergencyresponse,and
surveillance.Together,thesedecentralizednetworksaddresschallengesinpollution,
transportation,andsafety,helpingsmartcitiesevolveintomoreconnected,efficient,andeco-
friendlyurbanecosystems[4][6].
1.1.PurposeandChallengeStatement
IssueswithMANETs,particularlyVANETs,includerestrictedcapacity,mobility,congestion,
andsecurityrisks.InFANETs,optimizedroutingenhancesdatatransferdespitehighUAV

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mobility[7][8].Nature-inspiredalgorithms,especiallyAntColonyOptimization(ACO),are
frequentlyusedforrouteselectionindynamicnetworkslikeMANETsduetotheirflexibilityand
QoSguarantee[9].6GwirelesstechnologywillrevolutionizeMANETs,VANETs,andFANETs
withterabitspeeds,ultra-lowlatency,andhighreliability,enablingadvancedsmartcity
applicationslikemachinecommunication,AR/VR,andthetactileInternet[1][4][6].
1.2.Objective
ConsideringananalysisoftheissuesTheotherpartofthisinvestigationisstructuredasfollows:
summaryisgiveninSection2aboutAdHocNetworks.FundamentalprinciplesofACOare
presentedinSection3.InSection4,ACO-BasedRoutingAlgorithmsforMANET,VANET,and
FANETarecompared.Section5outlinesopenchallengesandpotentialavenuesforfuture
research,whileSection6concludes.
2.OVERVIEWOFADHOCNETWORK
Aprototypicalanarchicnetworkmadeupoverheadwithnopriorinfrastructureiscalledanad
hoctopology[10].I’spossiblewhichcategorizeadhoctypesnetworksintofourvarietiesbased
ontheapplicationdomain:MANETs,VANETs,FANETs,andUANETs.
2.1.MobileAd-HocNetworks(MANETs)
MANET,ormobileadhocnetwork,seemsasortofwirelesssystemwherepointsengagetoone
otherentirelylackingrequirepermanentfacilitiesoranauthoritativecentre.MANETstofunction
everymemberactsasbothagatewayalongwithaserver,enablingittosendandreceive
messagesfromothernodes.Itisveryadaptable,flexible,andoffersmanybenefits,whichis
helpfulwhenmoreconventionalnetworkscannotbeformed[11][12].
MANETproperties
(1)Becauseoftheirmobilenodes,theirtopologyiserraticandrapidlyshifting.(2)Becauseof
openmediaandfreespace,thewirelesschannelhasbothvariablecapacityandlimitedbandwidth.
(3)Everynodeinthenetworkfunctionsindependentlyandforwardsdatabyactingasbothahost
andarouter.(4)MANETthatusuallyupofdifferentkindsofappliances,resources,signal
strengths,aswellcommunicationtechnologies(likeBluetoothorWi-Fi).Therefore,bothshort-
rangeandlong-rangedevicescanbeapartofaMANET.(5)MANETsmustemploycongestion
controltechniquestoboostnetworkthroughputsinceusingsharedmediamightcausedata
transmissionbottlenecks[13].
MANETApplications
Typicalapplicationsinclude(1)militarybattlefields(2)naturaldisasters(3)educational
institutions(4)healthcarefacilities;and(5)commercialandciviliansettings.BecauseMANETs
mayestablishanetworkwithouttheneedforfixedinfrastructureinlocationsthatareriskyor
difficulttoreach,theyareessentialformilitaryoperations.Naturalcatastrophesareestimatedto
kill45,000peopleannuallyonaveragethroughouttheworld.Qualityeducationisthefourthgoal
amongtheseventeenobjectivestoearnsustainablegrowththattheUNset.Thestrategiesfor
achievingthisobjective,however,maybedifficulttoimplementinruralorisolatedlocationsand
inunderdevelopednations[13].Applicationsforchatande-commerce,databases,trafficsharing,
andmoreareincludedinthis.

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2.2.VehicularAdHocNetworks(VANETS)
VANET,mobileentities(vehicles)andstationaryentities(roadsideunits)worktogetherto
exchangevitalinformationabouttheconditionofroadsandothertransportation.Between
vehicles(V2V),betweenvehicleandinfrastructure(V2I),vehicleandSensor(V2S),Intra-
Infrastructure(I2I),vehicletothestructureofacellphonenetwork(V2CN),andfromvehicle
towardpersonalDevice(V2PD)arethesixwirelesswaystocommunicatethataretypically
foundinthiskindofnetwork.
VANETproperties
(1)VANETsworkinaconstantlyshiftingatmosphere,withrapidsettingchangesduetovarying
vehiclespeedsandfrequentnodeconnections/disconnections.(2)GPSenableseasyroad
informationaccess,estimatingvehiclepositionsviaspeedandtrajectory.(3)Asvehiclesmove
around,thetopologyisalwayschanging.(4)Unliketraditionalnetworks,VANETsfaceno
powerconstraintsasvehicleshavepowerfulbatteriessupportingonboardunits.(5)Additionally,
modernvehiclescomeequippedwithhighcomputationalcapabilities,includingGPRS,memory,
sensors,storage,internetconnectivity,andadvancedantennas,enhancingnetworkperformance
andcommunicationefficiency[14].
VANETApplications
VANETsarefrequentlyusedinthefollowingareas:(1)Ad-HocEnabledCarCommunities(2)
Ad-HocFacilitatedITSVehicleNavigation(3)StolenVehicleTracking(4)On-the-GoTraffic
ManagementandAutomaticIdentificationofTrafficRuleOffenders(5)WarningofAccidentsat
Intersections.
2.3.FlyingAdHocNetworks(FANETS)
FANETsrepresentlikeaspecializedformofmobileadhocnetworksalsoVANET,hereUAVs
functionasnetworknodes.FANETsarecharacterizedbyfrequenttopologychanges,three-
dimensionalenvironmentalconditions,diversemobilitypatterns,andvaryingterrainstructures
[15].
FANETproperties
(1)FANETstypicallyfeaturealowerconcentrationofUAVnodesincomparisonwithother
typesofnetworkssuchasMANETsandVANETs(2)FANETsexhibitsignificantlyhighernode
mobilitythanVANETsandMANETs(3)FANETtopologychangesfrequentlyduetoUAVs'
fastmovement(4)Theradiopropagationmodeliskeyindesigningandsimulating
communicationsystems(5)FANETpowerconsumptiondependsonUAVsize,communication
range,hardware,andlinkobstacles[16].
FANETApplications
MANETslacktheperformanceneededfordisasterandwarzonetaskslikefloods,earthquakes,
andrescuemissions.Thus,FANETsaremostlyutilizedfor(1)targetdetection,(2)emergency
scenarios,(3)civilianandpublicapplications,(4)datapacketdelivery,and(5)routeguidance.

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2.4.DifferencesbetweenMANETs,VANETsandFANETs
Table1providesabriefofthecomparisonsbetweenFANET,VANET,andMANETregarding
certaincriteria.
Table1ComparisonofNetworks[17][18]
3.OVERVIEWOFANTCOLONYOPTIMIZATION(ACO)
3.1.BiologicalInspiration
Researchincomputerscience,mathematics,engineering,power,andbusinesshasfrequently
drawninspirationfrombiology[19].Meta-heuristicalgorithmsofferoptimalornearlyoptimal
answersinareasonableamountofcomputationaltimefornetworksofanysize.Forpathfinding
andoptimizationproblems,ParticleSwarmOptimizationorPSO,AntColonyOptimizationor
ACO,andGeneticAlgorithmsorGAiscurrentlyextensivelyemployed.Specifically,ACO
calledabiologicallyinspiredmethodthatisbaseduponantwhileforaging.Itisusefulfor
resolvingchallengingcombinatorialissuesandisamemberoftheoptimizationmeta-heuristic
family[20].
3.2.AntsintheRealWorld
Withover8,800speciesknowntodate,antsaregregariousinsectsthathavebeenaroundfor
morethan100millionyears.Therearevariouscastesinacolony,andeachhasaspecific
function:(1)Workers:Sterilefemalesinchargeofmaintainingthenest,foraging,andtendingto
thequeenandheryoung.(2)Queens:Thefertilefemaleswholayeggsandestablishcolonies.(3)
Drones:Maleantsthatareonlypresentduringthematingseasontoreproduce.(4)Soldiers:In
certainspecies,theselarger,morepowerfulworkersprotectthecolonyfromintruders[21].
Parameters MANETs VANETs FANETs
NodeDensity
speed
Lowas6km/h Modesttofastspeedthat
20to130km/haverage
VeryLessFromslow
towardfast(6-460
km/h)
Mobility Identicaltwo
dimensions,aswell
randompaths,Little
Identicaltwo
dimensions,aswell
randompaths,Large
Free,three
dimensuional,
Frequentor
predeterminedpaths,
extremelylarge
Model of
transmission
Groundlevel Groundlevel Air
BandoffrequenciesFrom30MGHzto5
GHz
5.9GHz UAV2-5GHz,2-2.4
GHz
Changeindesign Alteringandhardto
predict
Straightmotionthatis
muchadvanced
Slow,fast,and
stationary
Technologythatlacks
wires
IEEEstandard
802.11a/b/g/nand
802.16
IEEEstandard802.11pIEEEstandard
802.11a/b/ac/g/s/n/p
PowerConsumptionEffectiveUseof
Energy
Notrequired MiniUAVsare
required.

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3.3.AntColonyOptimization
AntColonyOptimizationknownasideaofferedfromMarcoDorigomadedealingwithavariety
ofcombinatorialdifficultieswithefficiency.Let'sexploretheworkingofantsinACOtechnique
throughaflowchart.
Figure1.WorkflowofAntcolonyoptimization(ACO)
Antscanfindoptimalornear-optimalpathsbybalancingexplorationandexploitation;
evaporationandheuristicparameters(suchasαandβ)encourageexploration,whilehigh
pheromoneconcentrationsencourageexploitation.InACO,pheromoneupdaterulesrefine
solutionsthroughreinforcement(depositingpheromoneonsuccessfulpaths)andevaporation
(diminishingpheromoneovertime).Theiterativepheromoneupdatesdriveconvergence,with
theconvergencerateindicatingthealgorithm'seffectivenessinfindingsolutions[20][21][22].
ACOiscombinedwithvarioustechniquesforimprovedrouting,includingfuzzylogicforsecure
pathselection(FTAR),MLforadaptiverouting(Q-learning,NSGA-II),andmeta-heuristicslike
ACO-FDRPSOforenergyefficiency.SecurityprotocolslikeSAR-ECCandS-AMCQintegrate
ACOwithECCandattackdefense,whileABPKMdetectsSybilattacks.Networkmodelslike
MAR-DYMOandMAZACORNETusetheNakagamiFadingModel,QoRAestimatesQoSwith
SNMP,andHOPNETcombinesACOwithZRP.Additionally,pheromone-basedmobility
modelsguideUAVmovementsandoptimisecoverageviapheromonemaps[18][21-23].
Forbetterrouting,ACOisintegratedwithanumberofmethods,suchasmeta-heuristicslike
ACO-FDRPSOforenergyefficiency,fuzzylogicforsecurepathselection(FTAR),andmachine
learning(ML)foradaptiverouting(Q-learning,NSGA-II).WhileABPKMidentifiesSybil
attacks,securityprotocolssuchasSAR-ECCandS-AMCQcombineACOwithECCandattack
defence.TheNakagamiFadingModelisusedbynetworkmodelssuchasMAR-DYMOand
MAZACORNET,QoRAusesSNMPtoestimateQoS,andHOPNETcombinesACOandZRP.
Furthermore,pheromone-basedmovementmodelsusepheromonemapstooptimisecoverageand
directUAVmotions.

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3.4.ConnectingACOtoAntBehavior
OneofACO'smaintenetsis(1)PositiveFeedback:Overtime,effectivesolutionsarereinforced.
DistributedComputation:Severalagentsoperateconcurrentlywithoutcentralizedcontrol.
StochasticDecisionMaking:Prematureconvergenceisavoidedandexplorationisguaranteedby
randomness.ACOalgorithmsarefrequentlyemployedtosolverouting,scheduling,and
optimizationissuesindynamicandcomplexcontextsbymimickingthesenaturaltendencies
[21][22].
3.5.EarlyAnt-BasedAlgorithmsthatwereDeveloped
Numerousroutingmethodshavebeenputforththusfarforadhocnetworks,withanemphasison
factorslikescalability,loadbalancing,andeffectiverouting.Thesealgorithmsfallintooneof
threecategories:hybrid,proactive,orreactive.Ant-ColonyBasedRoutingAlgorithm(ARA)and
AntNetaretwovarietyofproactiveACO-basedprotocolsthatkeepcontinuousrouting
information,guaranteeinglowerlatencybutattheexpenseofincreasedoverhead.Ant-Based
Control(ABC)andPACONETforMANETsareexamplesofreactiveACO-basedprotocolsthat
createroutesonlywhennecessary,loweringoverheadbutraisinginitialdelay.HOPNETand
AntHocNetareexamplesofhybridACO-basedprotocolsthatoptimizeroutingefficiencyand
adaptabilitybycombiningthebenefitsofproactiveandreactivetechniques[22][23].
However,thisstudyhighlightsrecentapplicationsofACOinvariousadhocnetworkstoguide
researchersonemergingtrendsandfuturecontributions.Asystematicpaperselectionprocess,
illustratedthroughadiagram,ensuresfocusedandrelevantliteraturecoverage.
Figure2.PRISMAStudySelectionProcessFlowDiagram

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Table2:ExistingworkonintegratingACO-basedroutingalgorithmsinMANET,timeframe(2021-2024)
ReferencesAlgorithms/mechanisms Performancemetrics Simulationtools
[12] ACOwithDelayAwareEnergy
Efficient(ACO-DAEE)
PDR,E2EDelay,
Throughput
NS-2.34
[24] ANNwithACO Energyconsumption,
Networklifetime,PDR,
Routingoverhead,E2E
delay
NS-3
[25] Deeplearninginspaceandtime
withenhancedACO
PDR,Throughput,E2E
delay,TruePositiveRate
(TP)
SimPylibraryin
Python
[26] EnhancedManhattanMobility
Model(EMMM)andACO
PDR,Throughput,Dropped
packets,Averagee2edelay,
andpacketoverhead
NS-2.35
Bonnmotion-3.0.1
[27] Quantum-inspiredACO(QACO)DiscoveryTime,Routing&
GatewayOverhead,
ProtocolTracing
MATLAB9.1
(R2016B)
[28] MyANT PDR,E2Edelay,
Throughput
Glomosim
[29] Cross-layerroutingthatrespects
energysystemutilisingACO
(AOERP)
E2Edelay,Deadnodes,
Reliability,Energyfactor
NS-2.34
[30] Gametheoreticapproachwith
AntHocNet
E2Edelay,PacketLoss
Ratio,Throughput
NS-2.34
[31] FuzzylogicandACO PacketsDiffused,PDR,
Avg.DeliveryRatio
NS-3
[32] ACObasedMAODV PDR,E2Edelay,
Throughput,Overhead
NS-2.34
Withacross-layerstrategy,authorUppalapatiSrilakshmietal.[12]setouttodesignanenergy-
efficientmilitaryroutingframework.Overcomingtheshortcomingsofcurrentmethods,suchas
PDO-AODV,whichpreventoptimalpathselection,isoneoftheprincipalgoals.Theirresults
demonstratethatACO-DAEEperformsbetterinPDR,delay,andthroughputthanPDO-AODV
andN-CRStogether.BecausetheForwarderSelectionFunctioningemploysaprobability-
dependentproceduretodeterminethebestnexthopbytakingintoaccountimportantvariables
likePheromoneTrail,LinkQuality,NodeEnergyLevel,alsoTrustRating,theauthorschosethis
approach.
HandeandSadiwala[24]proposeanenergy-efficientMANETroutingmethodusingANNand
ACO.ANNenablesintelligent,adaptiveroutingbasedonfactorslikeenergy,mobility,and
congestion,outperformingAODVandDSR.ACOenhanceslinkselection,boostingPDR,
reducingenergyuseanddelay.FutureworkincludesoptimizingANNscalability,reducing
computationalcosts,andintegratingreal-timemobilityandhybridmodels.
TheaimofJIAMIAOZHAOetal.[25]istoformulateacross-layerdesignfordirectional
multicastthatusesDI-prediction.Thesuggestedsolutionoutperformsthecompetitionandavoids
packetlossduetonodemobilitybyproactivelymanagingdirectionalinterference(DI)withST-
ResNet,anANNversionthatpredictsDIpatternsspatiotemporally.TheimprovedACO+
algorithmthenusesthesepredictionstomaintainoptimalmulti-treeroutingthatavoids
interference.ComparedtoreactivemethodssuchasAODVandAMRoute,thiscross-layer
approachimprovesDIpredictionaccuracy(81.33%truepositive),enhancespacketdeliveryratio

8. InternationalJournalofWireless&MobileNetworks(IJWMN),Vol.17,No.4,August2025
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(82%recovery),booststhroughput(10.7Mbps,25%improvement),anddecreasesEnd-to-End
Delay.
SatveerKouretal.[26]predominantobjectiveistousemobility-awareACOtoimprove
MANETperformance.BecauseofitsintegrationwiththeEnhancedManhattanMobilityModel
(EMMM)anddistinctivelinkselectionmethod,thesuggestedACBBRtechnique,avariantof
ACO,performsbetterthananotherACOvariantknownasAMBRLB.Comparedtomulti-path
techniques,EMMMguaranteessmoothernodemotions,whileACBBRcostofpackets.In
mobilityexperiments,theseelementsresultinnotablegainsinthroughput,missedpackets,
averagepdrande2edelay.Oneareaforfuturedevelopment,accordingtotheauthors,ischoosing
adynamicthresholdforidentifyingsignificantrelationships.Althoughtheyadmitthatthiscould
bedynamic,theynowclassifylinksusingafixedthresholdof80%ofthedepositedpheromone.
Additionally,theyproposethattheACBBRapproachbeemployedforfutureimprovementsin
networkloadbalancingbyapplyingittomultipathACOalgorithms.
JamalKhudairMadhloometal.[27]forthegoalofQuantum-InspiredAntColonyOptimization
(QACO)methodtoenhanceinternetaccessinMANETsthroughimprovedgatewaydetection.
QACOreducesnetworkoverhead,acceleratesgatewaydiscovery,andmaintainsstablepathsby
leveragingquantumparallelismtoavoidprematureconvergenceandefficientlyexploresolution
spacesaddressingthelimitationsofclassicalACO.ItoutperformsprotocolslikeAODV,OLSR,
ZRP,andAntHocNetinspeedandoverheadreduction.However,challengesremain,including
theneedforbetterinternalnetworkdiscovery,adaptivelinkthresholds,andimprovingsearch
efficiencylimitedbyfixedlookup-basedquantumrotationangles.
MyANT,arevolutionaryACOdependentroutingalgorithm,istheprincipalobjectiveofthe
authorsPimalKhanparaandSharadaValiveti[28]foradhocnetworks.Becauseofitshybrid,
bio-inspiredACOmethodology,whichadjuststothedynamicfeaturesofadhocnetworks,the
MyANTroutingschemeperformsbetter.Itallowsforongoingimprovementandpath
optimisationbyfusingproactiveroutemaintenancewithreactivepathdiscovery.Byrebuilding
pathwaysorgeneratingbackups,MyANTalsoeffectivelymanageslinkfailures.Consequently,
MyANTachieveshigherthroughputthanconventionalprotocolssuchasAODV,DSR,and
AntHocNet.AlthoughitspacketdeliveryiscomparabletoAntHocNet's,itsdelayisgreaterthan
thatofAODVandDSR.Reducingtheroutingoverheadbroughtonbyproactiveantsisthe
primarytaskfornextdevelopmentintheMyANTroutingsystem.
ResearchersShaikShafiandD.VenkataRatnam[29],introduceanovelACOdependentenergy
concerncrosslayerAOERPwiththemainobjectiveofenhancingrouteconsistencyandnetwork
longevityinMANETs.BychoosingAdaptiveRelayNodes(ARNs)accordingtoEnergyFactor
andNearbyNodeProportionandutilisingACOwithStabilityFactor,LET,Congestion,andHop
Counttodeterminetheoptimalpath,theprotocol'scross-layerdesignoptimisesrouting.
Regardingreliabilityandenergyefficiency,eedlatency,andnodelongevityatdifferentdensities
andspeeds,AOERPperformsbetterthanEPAAODVandK-meansAODVACO.Analysingthe
suggestedplanfromavarietyofanglestoenhancesecurityanddefendagainstthreatsin
MANETsisthemainfocusforfutureresearch.
Bydynamicallyadjustingitspheromoneevaporationvalueonline,theauthorsMarwanA.
Hefnawyetal.[30]mainlyaimatenhancingACOroutingstrategiesforMANETs.The
recommendedgame-theoreticmethodforadjustingAntHocNet'spheromoneevaporationrate
enhancesperformancethroughadaptive,inexpensivetuning.Bydynamicallymodifyingthe
evaporationparameter,itlowersoverhead.Inbiggernetworks,thisproducesbetterresultsthan
AntHocNetregardingeedlatency,packetloss,andthroughput.Insomeareas,itperformsbetter
thanAODVaswell.Theprimaryobstaclesaretherequirementformorethoroughexperimental

9. InternationalJournalofWireless&MobileNetworks(IJWMN),Vol.17,No.4,August2025
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findingsandtheincorporationofadditionalQualityofService(QoS)measureslinkedtothe
gamers.
Fuzzyantcolonynetworkingsafety,orOFACA-5G,wasproposedbyR.Nithyaetal.[31]
OFACA-5G:5GMANETswithimprovedroutingviafuzzylogic.For5G-MANETsutilising
mmWavetechnology,theOFACA-5GalgorithmperformsverywellwithanenhancedACO
hybridroutingsolutionandsecurity-awarefuzzylogictoidentifyroguenodes.Itprovideslower
overhead,lowerlatency,improvedPacketDeliveryRatio(PDR),andstrongfaulttolerance.
OFACA-5GperformsbetterthanAODVandismoreresilientthanANT,particularlywhenit
comestoSybilandblackholeassaults.Asignificantobstacletofurtherresearchistheneedfor
increasedcautioninselectingsuitableparametersforthefuzzyframework.
Theauthors,MallikarjunaAnantapurandVenkanagoudaChanabasavanagoudaPatil[32],
introducedanACO-basedMAODVforsafeMANETtransmission.ByemployingACOto
counteractblackholeattacks,theMAODV-ACOalgorithmimprovessecuredatatransmissionin
MANETsandachievesa99.66%packetdeliveryratiofor100nodes.Itminimisesoverhead,cuts
downondelay,andincreasesthroughput.TheauthorsdrawattentiontoissueswithMANETs,
suchasroutingcomplexity,securityflaws,andrestrictionsinprotocolsincludingAODV,
TSQRS,andCARP.
Table3:ExistingworkonintegratingACO-basedroutingalgorithmsinVANET,timeframe(2021-2024)
ReferencesAlgorithms/mechanisms Performancemetrics Simulationtools
[5] GyTAR,E-IFTIS,Dijkstra’s
algorithmandACO
PDR,E2Edelay,andBytes
overhead
NS-3andSUMO
[8] EnhancedLocation-AidedAnt
colonyRouting(ELAACR)
Throughput,PacketLossRate,
PDR,Overhead,E2Edelay
NS2.35,MOVE,
SUMO0.22.0
[22] ACO WaitingTime,Route
Diversity,FuelConvergence,
TripStandarddeviation
MATLAB
[33] ACO-AODV Packetdroppingrate,
Overhead,Expecteddelay,
Throughput
MATLAB
[34] AODVaswellACO Ratioforpacketloss,Delay
Throughput,TotalOverhead,
Energyloss
MATLAB
[35] VANET-ACO ConnectionProb.,PDR,
Delay,LatencyVar.,
Throughput,QoS,Segment
Connection,TxRate,Vehicle
Density
Omnet++
[36] ACOtechniqueandthe
AODV
Delay,PacketLoss
Throughput
NS-2
[37] ACOwithDistance-Based
SourceRouting
PDR,PLR,E2Edelay,
Throughput,Routing
Overhead
NS-2.34
[38] ACO,Fuzzylogic,TCPand
UDP
PDR,E2Edelay,Network
Overhead,PSNR
NS-2.34,EvalVid,
VanetMobiSim
[39] GeneticAlgorithm(GA)into
ACO
PDR,Averagethroughput,
E2Edelay,Packetloss
NS-3.26and
SUMO

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Theevaluationofmultiplejunctionorientedtrafficawarenessnavigation,orMJTAR,forurban
VANETsistheprimaryaimofthisstudyconductedbySeung-WonLeeetal.[5].AntColony
Optimisation(ACO)andatwo-hopjunctiontechniqueareusedbyMJTARtooptimiseVANET
routinginordertoavoidclosedroads,decreaselatency,andincreasepacketdelivery.Itreduces
routingoverheadthroughtheEnhancedInfrastructure-FreeTrafficInformationSystemand
performsbetterthanGyTARandGSR.ThelimitationsofMJTARincludetherequirementfora
hybridRSUapproach,thedifficultyofobtainingtrafficinformationinlow-densityareas,and
morecomputationatintersections.Futureresearchwillconcentrateonenhancingforwarding
nodeselection,investigatingcollisionavoidanceformulti-sourcetransmissions,andusing
machinelearningtotrafficprediction.
Theinvestigation,whichwascomposedbyRaghuRamamoorthy[8],suggestsusingELAACR
forexchangingdataonVANETsinaseamlessandsecuremanner.Inhighwaysettings,
theprotocolperformsverywellbyintegratinganAntColonyRouting(ACR)algorithmfor
effective,securepathfindingwithLocation-AidedKeyManagement(LAKM)forsafevehicle
authentication.Bygivingprioritytohighpheromoneroutes,itlowersthePacketLossRate,EED,
andincreasesPDR.Inaddition,ELAACRprovidesfasterconvergenceandlessroutingoverhead
thanEHACORPandF-ANT.Becauseofdifferentspeedsandimpedimentsthatraiseprocessing
overhead,ELAACRisbestsuitedforhighwaysandisnotappropriateforurbansettings.Future
researchshouldincorporateacongestionstrategyandmodifyitformetropolitanenvironments.
Theauthorsoftheresearch,AmarPartapSinghPhrawahaetal.[22],aimtoimprovetransport
performancebyestablishinganintelligentroutestrategy.Thesuggestedapproachusesa
decentralised,adaptivealgorithminspiredbyAntColonyOptimisation(ACO)tooptimise
transportationnetworks.Becauseitcanmodifypheromonelevelsinresponsetoreal-time
congestion,itperformswellindynamicnetworkscenariosandchangingconditions,resultingin
resource-optimized,time-efficient,andcost-effectivetransportationwithalowerenvironmental
impact.Thedifficultiesofmaintainingbignetworks,findingsuboptimalsolutions,andfine-
tuningparametersarehighlightedinthepaper.Forgreateradaptability,futureresearchwill
concentrateoncombiningthealgorithmwithmachinelearningandsmartcities.
Inordertocreateaquantum-secureIoVprotocolwithoptimalroutingfor5Gintelligentcities,
TannuSharmaetal.[33]taketheinitiative.Byemployingringlearningwithmistakestoprovide
post-quantumsecurityandACOforhostilevehicleidentificationandoptimisedrouting,this
approachperformsexceptionallywell.Withloweroverhead,higherthroughput,andfewerpacket
drops,itperformsbetterinIoVcontextsthanSE-AOMDVandellipticcurvecryptography.IoV
issuesthatcausedelaysandexcessivecostincurrentframeworksarehighlightedinthepaper,
includingprivacy,security,andineffectiveroutingwithoutmaliciousvehicledetection.
Theintentofthisstudy,whichwaspreparedbyPayalKaushaletal.[34],wastocomparethe
real-timeapplicationsofAODVandACO.ThesourcesexamineandcontrastACOandAODV,
tworeactiveroutingtechniques.Whenitcomestothroughput,packetlossratio,anddelay,ACO
outperformsAODV.ThereasonforthisisthatACO'sroute-findingtechniqueisdynamicand
scalablesinceitisaself-organizationsystemthattakesintoaccountoffitnessmeasurementslike
latencyanddistance,andvehiclevelocity.Thecurrentroutingprotocolsneedtobeoptimised.
ThisleadstotherecommendationthathybridprotocolsbecreatedinordertoimproveVANET
performanceevenmore.
AliM.Alietal.[35]talkabouthowtooptimizeroutingforUGVcommunicationduring
firefighting.TheACO-basedprotocolusesthroughputtochoosethebestroutes,outperforming
GSRandCARinQualityofServicemetricsincludinglatency,packetdeliveryratio,and
connectionprobability.Thestudydrawsattentiontotheerroneouspathwaysandincapacityof

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thecurrentVANETroutingprotocolstoadjusttonetworkchanges.Subsequentinvestigations
willexaminetheinfluenceofVANETweightvariablesandcontrastACOwithalternative
optimisationmethods.
ManojSindhwanietal.[36]composedthisresearch,whichisabouthowtoimproveVANET
routingbycuttingdownontraffic.ThesuggestedACO-AODVprotocolperformsbetterthan
AODVbyincreasingthroughput,decreasingpacketlossanddelay,andusingoptimalpath
selectiontominimisecongestionandbandwidthuse.Bychoosingrouteswiththeleastdistance,
theACOalgorithmaccomplishesthis.TheconstraintsofAODVindynamicVANETs,
specificallycongestionandinefficientbandwidth,arediscussedinthestudy.Inorderto
maximisedependabilityandefficiency,futureresearchwilltesttheprotocolwithmorenodesin
locationswithhightraffic.
RaghuRamamoorthyandMenakadeviThangavelu[37]proposedEHACORP:EnhancedHybrid
ACOforVANETs.EHACORPimprovespacketdeliveryandthroughputwhileloweringlatency,
packetloss,androutingoverheadbyutilisingasource-basedACOandtheDistanceCalculation
Methodtodeterminethebestroutes.ItperformsbetterinurbanroadsettingsthanF-ANT,
AODV,ARA,andAntNet.Thefocusonurbantopologiesandthelackofextensivetestingon
highwaysarethestudy'slimitations.EHACORPwillbeexpandedtohighwayscenariosinfuture
researchtoassessperformancemorebroadly.
Thispaper,publishedbyMohammadVafaeietal.[38]designaQoSadaptiveACO-Fuzzymulti-
pathroutingforVANETs.Thesuggestedtechniqueperformsexceptionallywellinurban
VANETsbyutilisingfuzzylogicforintelligentnext-hopvehicleselectionandACOformulti-
pathQoSrouteselection,whichenhancespacketdelivery,throughput,andlowersoverheadand
latency.ItismoreefficientthantheUDP,MSLND,VACO,andAQRVprotocols.Withlittle
testingonhighways,thestudy'sfocusonurbantopologiesisoneofitslimitations.Inthefuture,
theprotocolwillbeexpandedtohighwaystomeetmoreQoSrequirements,suchassecurityand
privacy.
GaganDeepSinghetal.[39]proposesandevaluatesGAACOroutingforVANETs.By
integratingGAandACOforoptimisedrouting,increasingpacketdeliveryandthroughput,and
decreasingdelayandpacketloss,GAACO(GeneticAlgorithminAntColonyOptimisation)
performsexceptionallywellinurbanVANETtrafficsituations(basic,complicated,and
Dehradun).ItperformsbetterthantheAODV,ACO,andPSOprotocols.Thestudy'sfocuson
urbantrafficisoneofitslimitations;futureresearchwilltrytoexpandGAACOtootherVANET
scenariosandinvestigateothermetrics,suchasintra-satellitecommunicationandFANET.

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Table4:ExistingworkonintegratingACO-basedroutingalgorithmsinFANET,timeframe(2021-2024)
Referen
ces
Algorithms/mechanisms Performancemetrics Simulation
tools
[6] MFA,ACO,EFA,IABC PDR,E2Edelay,Routing,
OverheadandThroughput
NS-3.26
[15] EALC, CACONET,
GWOCNETs
Clusterscount,buildtime,
lifetime,energyuse
EALC
matches
clusters,
outperforms
ACO&GWO
[16] Ant-Hocnetwithfuzzylogic PDR,E2Edelay,
Throughput
MATLAB
[40] HybridACO-PhysarumForaging
Model
Avg.E2Edelay,PDR,
energy,routingtime
OMNeT++
[41] ACO,PSO,GA,DE Collisionavoidance,
communicationrange,and
accuracy
Real-world
applications
[42] ACO,OLSR PDR,Throughput,E2E
delay
NS-2
[43] ACO,PSO,RAODVandDijkstra
algorithm
PDR,betweenhostsdelay,
Overheadofrouting,
Networks,Throughput
NS-2
[44] EnhancedACO Throughput,Network
Lifetime
NS-2
[45] AntHocNet Clusters:Count,Build
Time,Lifetime,EnergyUse
NS-2
[46] AntHocNet Throughput,Bandwidth
Use,PacketLoss,QoE
NS-2
[47] ACOwithtrust-awarealgorithm Latency,Packetdelivery
ratio,aswellasRouting
overhead
OMNET++
[48] MAAandBCO Throughput,Scalability,
PDR,andE2edelay
MATLAB
AmritaYadav'spaper[6]aimstoimproveFANETroutingalgorithmssotheymaybeusedinreal
time.TheModifiedFireflyAlgorithmoutperformsACO,IABC,andEFAregardingthroughput,
packetdelivery(98%)andE2edelay(0.9ms)acrossnodedensities(10-50nodes).ACOservesas
areferencepointforcomparison.Withnodedensitiesgreaterthan20,theMFA'sperformance
deteriorates,indicatingtheneedforalternativenature-inspiredalgorithms(NIAs)forcertainuses.
NewNIAs,energy-efficientrouting,andmovingtohardwareforreal-timeFANETapplications
willallbeinvestigatedinfutureresearch.
FarhanAadilandothersexpressedit[15].Itsgoalistoaddressimportantproblemsrelatedto
effectiveclusteringandmodifyingthepowerofUAVtransmissions.Becauseofitssimpler,less
complicatedmethodology,theEnergyAwareLink-basedClusteringmethodperformsbetterin
clusterconstructiontimeandenergyusagethanACO-basedCACONETandGWO-based
clustering.ACOisusedasastandardbywhichtocompare.AlthoughEALConlyroughlyrivals
CACONETinclusterlifetime,itovercomesACO'shighcomputationalcomplexityandsluggish
convergence.Inordertoincreaseroutingefficiency,futureresearchwillconcentrateon
integratinghighnodemobility.

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Theintentofthisstudy,authoredbySaifullahKhanetal.[16],istoimproveFANETrouting
utilisingAnt-HocNetandfuzzylogic.FAnt-Hocnetusesfuzzylogictoanalysewirelessnetwork
status,includingbandwidth,nodemobility,aswellqualityoflink,andimprovesthroughput,end-
to-enddelay,alsopacketdeliveryproportion.TheAnt-Hocnetmethod,thatisfoundedonACO,
isimproved.ThestudydoesnotaddresscongestioncontrolinFANETs,securityagainstrogue
nodes,orpacketdroplikelihood.Thesetopics,whichincludecongestionreductiontechniques,
priority-basedcommunication,packetdropmonitoring,andsecurity,willbethefocusoffuture
research.
ThegoaloftheresearchcarriedoutbySiweiYangetal.[40]istopresentanewICRPfor
scalable,adaptiveUAVrouting.Inhigh-speednodemobilityscenarios,theInter-ClusterRouting
Protocol(ICRP)performsbetterthanAODV,FL-AODV,andEnhancedAntAODVintermsof
packetdeliveryrateandEED.ItisbasedonACOandovercomesitsslowconvergencebyusing
aPhysarumpolycephalum-inspiredheuristicandimprovingpheromoneupdates.Wireless
communicationinformationleakageisnotcoveredinthestudy.Futureresearchwillconcentrate
onsecurerouting,authentication,encryption,andnetworkadaptationfor5G/6GandIoT.
YunusAlqudsiandMuratMakaraci[41]discussthelatestprogressinswarmaerialrobots.The
reviewstudyhighlightsACO'sconvergenceandoptimalperformanceinlimitedsearchspacesas
aheuristicsearchalgorithmforpathplanning,taskallocation,andresourcemanagementin
SwarmRobotics.Ithighlightsissueswithswarmaerialrobots,suchasscalability,faulttolerance,
andalgorithmiccomplexity.Futureresearchwillconcentrateoncreatingadaptive,energy-
efficientalgorithms,combiningAIandmetaheuristics,andtacklingmoraldilemmas.
ShiyarKarboozetal.[42]suggestedtheINT-OLSRroutingprotocolintheirstudy.Byselecting
morereliableroutesbasedonnodespeedanddistance-factorsthatOLSR'shop-countmeasure
doesnotaccountfor—theIntelligence-OLSR(INT-OLSR)protocolperformsbetterthanOLSR.
Althoughitincreasesthroughputandthedatadeliveryratio,itperformssimilarlytoentiredelay
ofOLSR.
Thisstudy,producedbyHayderA.Nahietal.[43],aimstointroduceMOHOQ-FANETfor
improvingQoS.Throughput,packetdeliveryrate,delay,aswelloverheadareallimprovedby
theMulti-ObjectiveHybridOptimisationforQoSAssistedFlyingAd-HocNetwork(MOHOQ-
FANET),whichperformsbetterthanCSPO-FANETandOSNP-FANET.ACOisutilisedfor
dependableroutingusingRAODVandshortestpathselection.Futureresearchwillconcentrate
onemployingclusteringtoimproveFANETenergyefficiency.
MuhammadHameedSiddiqiandothers[44]comeupwithadrone-basedFANETforkeepingan
eyeontrafficinsmartcities.TheEnhancedAntColonyOptimisationapproachoutperforms
conventionalACO,IACO,andICMPACOintermsofnetworklongevityandperformance,
particularlyinbiggerareas.Byalteringthepheromonetechnique,eACOimprovesdata
communicationandrouteselectionindynamicFANETs.Butwhentherearemorethan650
nodes,itperformsworse.
InamUllahKhanetal.[45]wrotethisstudywiththegoalofaddressingimportantconcernsand
suggestingeffectiveclustering.TheAntHocNetmethodexcelsinFANETsbyachievingoptimal
throughput,balancedpacketdeliveryratio,reducedloss,andlowdelay,outperformingprotocols
likeDSR,ZRP,M-DART,DSDV,andAOMDV.Futureworkwillfocusondeployingadditional
mobilitymodelsforUAVs.
TheaimofSadoonHussainetal.research[46]istocreateaninnovativeant-basedrouting
systemfornetworksthatflysecurely.Intermsofend-to-enddelay,overhead,anddata

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forwarding,theAntHocNetapproachperformsbetterthanprotocolssuchasi-ACO,LEACH,
ZRP,M-DART,DSR,DSDV,alsoAOMDV.Itachieves93%optimisedpacketdroprate,90%
increasedbandwidth,and95%enhancedthroughput.Pheromoneupdatesareusedfordata
encryptionandsecurity,anditisbasedonACO.Futureresearchwillinvestigatehybridmobility
modelsforFANETs,particleswarm-basedrouting,andtheintegrationofcomputational
intelligence,AI,andmachinelearningtoenhancecommunicationstandards.
SahabulAlametal.[47]proposedabio-inspiredstrategywithACOforsafe,energy-efficient
routingandtrustedleaderselectioninsideclusters,thesuggestedalgorithm,aTrustedFuzzy
RoutingScheme,performsverywellinFANETs.InFANETs,itworksbetterthancurrent
protocolslikeSecRIPandUNION,demonstratinggainsinlatency,packetdeliveryratio,also
lessroutingoverhead.
AuthorAltafHussainetal.[48]workwithbothMoth-and-Ant(MAA)alongsideBeeColony
Optimisation(BCO)routingprotocolsoutperformDSR,AODV,andDSDVinFANETsdueto
theirsuperiorthroughput,scalability,PDR,anddecreasedEnd-to-EndDelay.Futureresearch
willconcentrateoncreatingmoreeffectiveroutingalgorithmsforinteractionbetweenUAVsand
thegroundaswellasbetweenUAVs,tacklingissueslikelinkstabilityandenergyconsumption
inhigh-speedUAVs.
4.OPENCHALLENGESANDFUTURERESEARCHDIRECTIONS
4.1.CurrentChallengesinACO-BasedRouting
WhileACOshowspromiseinaddressingroutingconcernsinadhocnetworks,challengesremain.
InMANETs,ACOmustadapttothenetwork'sextremelydynamicnaturewhilemaintaining
energyefficiencyandsecurity.Furthermore,developmentsinparametertuning,QoSsupport
throughcross-layerdesigns,andscalabilityviamethodologiessuchasQuantum-InspiredACO
(QACO)arecrucialforperformanceoptimization.ModificationstoVANETsareaimedat
enhancingconvergencespeed,flexibilitytonetworkdynamics,decreasedoverhead,increased
security,andmoreoptimalroutingindiversevehiclecontexts.Fromcomplexalsocontinuous
changingnatureofUAV-basedinfrastructures,FANETsrequireimprovementsinACO
versatility,convergencespeed,efficiency,andoverallperformance.Addressingtheseproblems
withenhancedoptimizationtechniquesandnewtechnologyiscriticalformaximizingACO's
promiseinnext-generationadhocnetworks[49][50].
4.2.PossibleResearchAreas
Forenergyefficiencyandadaptability,futureMANETresearchshouldconcentrateonintelligent
cross-layerroutingusingAIandswarmtechniqueslikeANN+ACO[12,24,25,29].Security
shouldbeimprovedthroughfuzzylogicandtrustmodels[31,32],andscalabilityshouldbe
improvedthroughquantum-inspiredtechniquesanddynamicparametertuning[27,30].For
improvedQoSandperformanceinVANETs,itiscrucialtoaddresscongestion,security,and
dynamictopologies[33,36,39],enhancesupportforbothurbanandhighwayenvironments[8,
37,38],andintegratemachinelearningwithACOforadaptiveroutingandtrafficprediction[5,
22].Withafocusonenhancingclustering,real-timehardwaredeployment,andsecure
communicationunder5G/6Gframeworks[43,44,45,47,48],futureresearchonFANETsshould
prioritiseenergy-efficient,secure,andadaptiveroutingusingAI,fuzzylogic,andhybrid
metaheuristics[6,15,16,40,46].Thesedevelopmentswillbeessentialforcreatingadhoc
networksthatareresilient,scalable,andnext-generation.

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5.CONCLUSION
AlthoughintegratingACOintoadhocnetworksforsuggestingsmartcityapplicationspresents
severalchallenges,surveyresultshighlightitspotential.Thissurveypresentthat,research
increasinglyfocusesonhybridizationwithtechniqueslikefuzzylogic,PSO,geneticalgorithms,
securitymechanismsandotherstoenhanceroutingefficiency.Assmartcitiesdependon
advancedcommunicationnetworks,ACO-basedsolutionscanimprovetrafficmanagement,
energyoptimization,andreal-timedataexchange.However,accordingtothisinvestigation's
analysis,themajorityofworksconcentrateonPDR,latency,andthroughput,ignoringimportant
metricssuchbufferoccupancy,loadbalancing,andjitterthatareessentialforreal-time
performanceinsights.Inconclusion,thisstudyidentifiesthreekeyresearchdirections:(1)
AdvancementsinACO(2)Diverseadhocnetworkapplicationsand(3)Emergingresearchtrends
offeringvaluableinsightsforfuturesmartcitycommunicationsystems.
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18. InternationalJournalofWireless&MobileNetworks(IJWMN),Vol.17,No.4,August2025
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AUTHORS
PromeSahaReshareceivedherBachelor'sdegreeinInformationandCommunication
TechnologyfromComillaUniversity,Bangladesh,in2023,followedbyaMaster'sdegree
fromthesamedepartmentinApril2025.Herresearchpassionsinvolvewireless
communication,routingprotocols,andmachinelearninganddeeplearningapproaches.
Sheiscurrentlyseekingopportunitiesforhigherstudiestofurtherheracademicand
researchcareer.
HridoyChandraDasreceivedhisBachelor'sdegreeinInformationandCommunication
Technologyin2023.HeiscurrentlypursuingaMaster'sdegreeinArtificialIntelligencefor
IndustrialApplicationsatOstbayerischeTechnischeHochschuleAmberg-Weiden
University,Germany.Hisresearchinterestsassociatedwithsignalprocessing,networking,
andAI-drivenapplications.
Dr.DulalChakrabortyisanAssociateprofessorofthedepartmentofInformationand
CommunicationTechnologyatComillaUniversity,Cumilla,Bangladesh.Hisresearchhas
appearedinseveralpeer-reviewedjournals.Heispassionateaboutmobilead-hoc
networks,networkroutingprotocols,networktrafficanalysis,machinelearning,
andrelatedfields.