Clustering in Vehicular Ad Hoc Networks Taxonomy, Challenges and Solutions

8/10/2019 Clustering in Vehicular Ad Hoc Networks Taxonomy, Challenges and Solutions

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Vehicular Communications 1 (2014) 134152

Contents lists available at ScienceDirect

Vehicular

Communications

www.elsevier.com/locate/vehcom

Clustering

in

vehicular

ad

hoc

networks:

Taxonomy,

challenges and solutions

RasmeetS Bali a,Neeraj Kumar a,JoelJ.P.C. Rodrigues b,

a DepartmentofComputerScienceandEngineering,ThaparUniversity,Patiala,Punjab,Indiab InstitutodeTelecomunicaes,UniversityofBeiraInterior,Portugal

a

r

t

i

c

l

e

i

n

f

o a

b

s

t

r

a

c

t

Articlehistory:

Received

15

February

2014Receivedinrevisedform5May2014

Accepted5May2014

Availableonline2June2014

Keywords:

Vehicularadhocnetworks

Clustering

Datadissemination

Routing

Over

the

last

few

years,

Vehicular

Ad

Hoc

Networks

(VANETs)

have

emerged

as

a

new

class

of

efficient

information

dissemination

technology

among

communities

of

users

mainly

because

of

their

wide

range

of

applications

such

as

Intelligent

Transport

Systems

(ITS),

Safety

applications,

and

entertainment

during

the

mobility

of

the

vehicles.

Vehicles

in

VANETs

act

as

an

intelligent

machine,

which

provides

various

resources

to

the

end

users

with/without

the

aid

of

the

existing

infrastructure.

But

due

to

the

high

mobility

and

sparse

distribution

of

the

vehicles

on

the

road,

it is

a

challenging

task

to

route

the

messages

to

their

final

destination

in

VANETs.

To address

this

issue,

clustering

has

been

widely

used

in

various

existing

proposals

in

literature.

Clustering

is

a

mechanism

of

grouping

of

vehicles

based

upon

some

predefined

metrics

such

as

density,

velocity,

and

geographical

locations

of

the

vehicles.

Motivated

from

these

factors,

in this

paper,

we have

analyzed

various

challenges

and

existing

solutions

used

for

clustering

in

VANETs.

Our

contributions

in

this

paper

are

summarized

as

follows:

Firstly,

a complete

taxonomy

on

clustering

in

VANETs

has

been

provided

based

upon

various

parameters.

Based

upon

this

categorization,

a detailed

discussion

is

provided

for

each

category

of

clustering

which

includes

challenges,

existing

solutions

and

future

directions.

Finally,

a comprehensive

analysis

of

all

the

existing

proposals

in

literature

is

provided

with

respect

to

number

of

parameters

such

as

topology

selected,

additional

infrastructure

requirements,

road

scenario,

node

mobility,

data

handled,

and

relative

direction,

density

of

the

nodes,

relative

speed,

communication

mode,

and

communication

overhead.

The

analysis

provided

for

various

existing

proposals

allows different

users

working

in

this

domain

to

select

one

of

the

proposals

with

respect

to

its

merits

over

the

others.

2014ElsevierInc.All rights reserved.

1. Introduction

Vehicular Ad Hoc Networks (VANETs) consist of Vehicles/Mo-

bile nodes communicating with each other over wired/wireless

links with/without existing infrastructure [1]. Vehicles have the

capabilitytocommunicatedirectlywithothervehiclesinPeer-to-

Peer

(P2P)

manner

or

indirectly

using

the

existing

infrastructurealongsidetheroad.Vehiclesandroadsideinfrastructureneedtobe

equippedwithdedicatedhardwareforprovidingsafetyandsecu-

ritytothepassengerssittingonboard.Standardizationofwireless

communicationtechnologyis requiredforprovidingentertainment

tothepassengers.ThereforeresearchonVANETshasbeenreceiv-

ingincreasinginterestinthelastcoupleofyears,bothinthe algo-

rithmicaspectsaswellasstandardizationeffortslikeIEEE802.11p

* Correspondingauthor.E-mailaddresses:[email protected] (R.S Bali),[email protected]

(N. Kumar),[email protected](J.J.P.C. Rodrigues).

&IEEE1609standards.In aclusteringstructure,themobilenodes

aredividedintoanumberofvirtualgroupsbasedoncertainrules.

Thesevirtualgroupsarecalledclusters.Underaclusterstructure,

mobilenodesmaybeassignedadifferentstatusorfunction,such

ascluster-head,cluster-gateway,or cluster-member.A cluster-head

normally serves as a local coordinator for its cluster, perform-

ing

intra-cluster

transmission

arrangement,

data

forwarding

etc.A cluster-gateway is a non-cluster-head node with inter-cluster

links, so it can access neighboring clusters and forward the in-

formationbetweenclusters.A cluster-memberisusuallycalledas

anordinarynode,which isanon-cluster-headnodewithoutany

inter-clusterlinks.

The notion of cluster organization has been used for Mobile

Ad Hoc Networks (MANETs) in number of issues such as rout-

ing, security, Quality of Service (QoS) etc. [1,2]. However due to

thecharacteristicsofVANETssuchashighspeed,variabledensity

ofthenodes,clusteringschemeswhichareproposedforconven-

tionalMANETsmaynotbesuitableforVANETs.Duetotimetaken

http://dx.doi.org/10.1016/j.vehcom.2014.05.004

2214-2096/ 2014ElsevierInc.All rights reserved.
http://dx.doi.org/10.1016/j.vehcom.2014.05.004http://www.sciencedirect.com/http://www.elsevier.com/locate/vehcommailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.vehcom.2014.05.004http://crossmark.crossref.org/dialog/?doi=10.1016/j.vehcom.2014.05.004&domain=pdfhttp://dx.doi.org/10.1016/j.vehcom.2014.05.004mailto:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/vehcomhttp://www.sciencedirect.com/http://dx.doi.org/10.1016/j.vehcom.2014.05.004


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R.S Bali et al. / Vehicular Communications 1 (2014) 134152 135

for cluster formation and maintenance, clustering requires addi-

tionalcontroloverhead.Thus agood clusteringalgorithm should

notonlyfocuson formingminimumnumberofclustersbutalso

dynamically maintain the cluster structure without increasing a

high communication overhead over the network. Thus clustering

allows the formation of a virtual communication backbone that

supports efficient data delivery in VANETs and it also improves

theconsumptionofscarceresourcesuchasbandwidth.A lowcost

clustering

method

should

be

able

to

partition

a

VANET

in

a

shorttimewithlittleoverheadofcontrolmessagebroadcasting.Hence,

VANETsmust follow a tight set of constraintsas compared with

MANETsandthereforerequirespecializedclusteringscheme.

Also,thedevelopedclusteringalgorithmshouldbedistributed,

withnocentralcoordinator.Thealgorithmshouldalsohandlethe

localityproperly,i.e.,singletopologychangeshouldhavelessim-

pactontheoveralltopologyandshouldbeabletodetectandreact

totopologychanges.Becauseofthehighdegreeofmobility,a clus-

teringalgorithmshouldconvergefastandshouldhaveareduced

overheadtominimizethetimelostintheclusteringprocess.

1.1. Motivationandchallenges

As

VANETs

have

been

used

in

various

applications

whose

ulti-mategoalistoprovidesafetyandcomforttothepassengerssitting

in the vehicles, hence there is a requirement of optimized solu-

tionsforclusteringinVANETs.Butdueto largenumberofnodes

andlackofrouters,a flatnetworkschememaycauseseriousscal-

abilityandhiddenterminalproblems.A possiblesolutiontoabove

problemsistheuseofanefficientclusteringalgorithm.As foref-

ficientcommunicationamongthevehiclesontheroad,Dedicated

ShortRangeCommunications(DSRC)isused,so itwouldbeagood

idea todividethevehicles inclusterssothatvehicleswithinthe

sameclustermaycommunicateusingDSRCstandards.Thesefacts

motivateustocategorizevariousclusteringtechniquesinVANETs

baseduponpredefinedcriteria.Buton theotherhand, thereare

numberofchallengesthatneedwelldesignedsolutionsforclus-

teringofvehicles.Someofthechallengesarehighmobilityofthe

vehicles,

sparse

connectivity

in

some

regions,

and

security.

Due to large number of parameters which have been consid-

ered in different clustering, it wasdifficult toconsider some pa-

rametersasstandard forevaluationofreviewedprotocols.To ac-

commodate this diversity, all the parameters were analyzed and

thensynthesizedintoeightstandardcategoriesinthispaper.These

eightparametershavebeenbroadlycategorized intovehicleden-

sityandvehiclespeed;whichcharacterizetheefficiencyofcluster-

ingprotocol;clusterstability,clusterdynamics,clusterconvergence

andclusterconnecttime;transmissionefficiencyandtransmission

overhead.Thisstandardizationwillhelpustoprovideacompara-

tiveanalysisofallthereviewedclusteringprotocols.

Someoftheabovedescribedparametersare illustratedasfol-

lows.VehicleDensitywhichisoneofthemostimportantparam-

eters

defines

the

average

number

of

vehicles

in

terms

of

vehicles

perkilometer(km)orvehiclesperlane.Forurbanscenarioshigh

valueofvehicledensityisconsideredcomparedtohighways.Ve-

hiclespeedistherangeofspeedsconsideredforsimulationbya

particular protocol in terms of m/s or km/h. A speed range that

varies realistically indicatesbetteradaptability. Transmissioneffi-

ciencyisdescribedasaveragenumberofmessagesorpacketsthat

aretransmittedorreceivedbyaclustermemberduringatimedu-

ration.Hightransmissionefficiencyshowsthataclusteringscheme

ismoreeffective indatadissemination.Transmissionoverhead is

theaveragecommunicationorcontroloverheadrequiredbyaclus-

teringschemeforclusterformationandmaintenance intermsof

numberofpackets.A clusteringschemethathas lowertransmis-

sionoverheadisdesired.Clusterstabilityistheaveragelife-timeof

a

cluster.

A high

value

of

cluster

stability

indicates

a

better

cluster-

ingprotocol.Theparameterclusterdynamicsdescribestheaverage

numberofstatuschangespervehicledefinedintermsofaverage

numberofclusterchangesorclusterheadchangesintermsofto-

talnumber of vehicles.A low value of clusterdynamics is more

suitable. Cluster connect time refers topercentage timeduration

thatavehiclestaysconnectedtoasinglecluster.A highvalueof

clusterconnecttimeindicateshighersuitabilityofaprotocol.Clus-

terConvergencereferstothedurationrequiredforallthenodesto

join

a

cluster

at

the

initiation

of

a

clustering

scheme.

The

suitabil-ityofaclusteringschemeforVANETsismorewhenitexhibitslow

clusteringconvergence.

1.2.

Main

contributions

Basedupontheabovediscussion,themaincontributionsofthis

paperaresummarizedasfollows:

AcompletetaxonomyforclusteringinVANETshasbeenpro-

videdwhichcategorizesclusteringbaseduponvariouskeypa-

rameters.

A detailed description of the protocols in each category has

been

provided

in

the

text.

Moreover,

an analysis

of

the

proto-cols

of

each

category

is

provided

by

careful

selection

of

various

parameters.

Finally, a detailed comparison and discussion of various ap-

proaches and protocols have been provided with respect to

variousparameters.Also,openissuesandfuturedirectionsin

this newly emergent area are highlighted in the text, which

guidesvarioususerstoselectaparticularsolutionbasedupon

itsmeritsovertheothers.

1.3.

Taxonomy

of

the

clustering

in

VANETs

Forefficientcommunicationamongthenodes inthenetwork,

stable clustering is required. In this direction, many researchers

have

used

various

techniques

to

form

a

stable

cluster

amongthenodes.Someof these techniquesconsistof theuseofsignal

strengthreceived,positionofthenodefromtheclusterhead,ve-

locityofthenodes,directionanddestinationofnode.Keepingin

viewoftheaboveissues,thedetailedtaxonomyofvariouscluster-

ingalgorithmisdescribedinFig. 1.

1.4. Organization

Rest of the paper is organized as follows. Section 2 provides

thedescriptionaboutthePredictiveclustering.Section3 describes

abouttheBackboneClustering.Section4 describestheMACbased

clustering. Section 5 discusses about the Traditional clustering

techniques.Section 6 exploresonHybridclustering.Section 7 de-

scribes

the

Secure

Clustering.

Section 8 provides

the

comparative

analysiswithrespecttovariousparameters.Section9 exploreson

the open research issues andchallenges.Finally, Section 10 con-

cludesthearticleandgivesthefuturedirectionsonthetopic.

2. Predictiveclustering

Inpredictiveclustering,theclusterstructure isdeterminedby

the current geographic position of vehicles and its future be-

haviour.Thisvehicletrafficinformationhelpstoassociatepriorities

whichthenassistinclusterformation.Thefuturepositionandthe

intendeddestinationsofvehicleshavebeenusedintheliterature

toformclustersinVANETs.Someoftheseprotocolsareclassified

as

position

based

and

destination

based

as

follows:


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136 R.S Bali et al. / Vehicular Communications 1 (2014) 134152

Fig. 1. Taxonomy of existing clustering approaches for VANETs.

2.1. Positionbasedclustering

Position based clustering is a technique of forming the clus-

tersonthebasisofgeographicpositionofthevehicleandcluster

head.Salhietal. [2] proposedanewpositionbasedclusteringal-

gorithm(NEW-ALM)whichisanimprovementtotheexistingALM

algorithm. The clusterstructure is determined by the geographic

positionofthevehicleandthecluster-head(CH)iselectedbased

onprioritiesassociatedwitheachvehicle.A hash functionbased

on theestimated travel time isused togenerate thispriority for

thevehicle.Thestabilityofthesystemisimprovedbyelectingthe

vehicleshavingalongertripasthecluster-heads.Thoughthisso-

lution gives a stable cluster structure but its performance is not

testedinsparseandjammedtrafficconditionswhichareveryfre-

quentindenseurbanregions.

Wang

et

al. [3] proposed

another

position

based

clustering

al-

gorithm.It isacrosslayeralgorithmbasedonhierarchicalandge-

ographicaldatacollectionanddisseminationmechanism.Theclus-

terformationinthisprotocolisbasedonthedivisionofroadseg-

ments.Howeverthisprotocol incursmoreoverheads forVehicle-

to-Vehicles(V2V)andVehicles-to-Infrastructure(V2I)communica-

tion.Thus itsperformance isaffectedbasedontheavailabilityof

aninfrastructure.

Fan et al. [4] proposed a clustering scheme where a utility

basedcluster formation technique isusedbyextending thedefi-

nitionofSpatialDependencywhichwas initiallyproposed in [5].

In the utility function, position and velocity, closest to a pre-

determinedthresholdvalueareusedastheinputparameters.The

threshold is computed based on the previously available traffic

statistics.

A status

message

is

periodically

sent

by

all

the

neigh-bouring vehicles. After receiving this information, each vehicle

choosesitsCHbasedontheresultsproducedbytheutilityfunc-

tion. The node with the highest value is chosen as the CH. This

schemeattemptstoenhancetheclassicalclusteringalgorithmsby

takingintoconsiderationsthecharacteristicsparticulartoVANETs.

However it still applies many fixed weights and the parameters

likefixedcluster formation intervalwhich impliesasynchronous

formationofclusters.Thisschemefailstoadapttotrafficdynamics

andisalsonotapplicableforeffectiveclusterre-organization.

Maslekaretal. [6] proposedanewcluster-headelectionpolicy

fordirectionbasedclusteringalgorithmcalledasModifiedCluster-

ingbasedonDirection inVehicularEnvironment(MC-DRIVE) [7].

The primary functioning of MC-DRIVE is based on the parame-

ter

THDISTANCE.

This

value

yields

the

optimal

value

of

the

cluster

and isdependenton thespeedand the radio rangeof thevehi-

cles

approaching

the

intersection.

THDISTANCE provides

the

means

for an effective cluster formation and CH election. The proposed

clusteringalgorithm isable tomaintain thestabilityof theclus-

terintermsofthenumberofnodeswithinacluster.Thishelpsto

achievebetteraccuracy indensityestimation. It isalsoobserved

that theaccuracycanbe further improvedbyreducing theradio

range up to a predefined threshold value. However, any further

reduction in the radio range leads to an increase in thenumber

of cluster-heads that results an increase in overhead of the sys-

tem.Wolny [8] optimizedtheexistingDMACalgorithmpresented

in [9] so that road traffic mobility is represented in an efficient

manner. The main idea for modified DMAC was to increase the

clusterstabilitybyavoidingre-clusteringwhengroupsofvehicles

move in different directions. The algorithm is based on periodi-

cal

transmission

of

status

message

and

it

also

forms

k-cluster

so

thatnodescanbek-hopsaway fromCH.This isachievedby in-

troducingTime-To-Liveparameter inmessagessentbythenodes.

Modified-DMACalsointroducesamethodforestimatingthecon-

nection time (called freshness in DMAC) of two moving nodes.

By periodiccomputationoffreshnessvalue,it ispossibletoavoid

re-clusteringwhentwonodesarewithintheconnectionrangefor

ashortperiodoftimewhichhelpstoincreasetheclusterstability.

AlthoughModified-DMAC increasesthealgorithmoverheadbutit

reducesthenumberofclusterchangestherebyincreasingthesta-

bilityofclusterformation.Itsperformancehasnotbeentestedin

sparseandjammed trafficconditionswhich arevery frequent in

denseurbantrafficscenarios.

2.1.1.

Discussion

on

position

based

clusteringPosition based clustering solutions are the key for clustering

inVANETs. In the last fewyears,manyclusteringprotocolshave

beenbuiltbyconsideringthevariouscharacteristics.Outofthese

proposals,theprotocolsbasedonthevehiclespositionsaremost

adequatetoVANETsduetotheirresiliencetohandlingthenodes

positionvariation [10].Table 1 providestherelativecomparisonof

theseprotocolswithrespecttokeyparametersthatinfluencepo-

sitionbasedclustering.

Since theaboveclusteringprotocolsprimarily relyon thepo-

sitionofthevehicle,therangeofvaluesforvehiculardensityand

vehiclespeedexhibitsavariation foreveryprotocolasshown in

Table 1.Howeverthevalueofclusterconvergencerateisloweven

ifvehicledensityandclusterdynamicsincreasewhichindicatesa

better

cluster

stability

for

these

schemes.

The

variation

in

cluster


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Table 1

Comparisonofposition basedclustering.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

PPC[3] LOW HIGH LOW HIGH LOW LOW MEDIUM HIGH

MODIFIED C-DRIVE[6] LOW MEDIUM LOW MEDIUM LOW LOW HIGH HIGH

CGP[2] HIGH HIGH HIGH LOW HIGH LOW HIGH HIGH

ALM[4] LOW MEDIUM HIGH LOW LOW LOW HIGH HIGH

Table 2

Comparisonofdestination basedclustering schemes.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

LICA [11] MEDIUM MEDIUM MEDIUM LOW MEDIUM MEDIUM MEDIUM HIGH

EUCLIDEANDISTANCE

CLUSTERING [12]

HIGH HGH MEDIUM LOW HIGH MEDIUM HIGH HIGH

AMACAD[13] LOW HGH MEDIUM LOW MEDIUM MEDIUM MEDIUM HIGH

CBLR [15] LOW MEDIUM MEDIUM LOW MEDIUM MEDIUM HIGH HIGH

size also affects performance in terms of mean cluster diameter

andthresholdforpositionbasedclustering.Thevalueoftransmis-

sionefficiencywhichultimatelyeffectspacketdeliveryratioisalso

on

the

lower

side.

From

Table 1,

it can

be

concluded

that

transmis-sionoverheadandclusterconnecttimeneedsfurtheranalysisfor

improving the overall efficiency of clustering. The high values of

clusterconnecttimeandtransmissionoverheadindicatetheneed

forfurtheranalysisandimprovementsothatpositionbasedclus-

teringschemescanbeefficientlyutilisedforVANETs.

2.2. Destinationbasedclustering

Destination based clustering technique takes into account the

currentlocation,speed,relativeandfinaldestinationofvehiclefor

cluster formation. The destination is known in prior using navi-

gationsystem.Variousproposalsinthiscategoryaredescribedas

follows:

Farhanetal. [11] proposedanalgorithmforimprovingtheac-

curacy

of

GPS

devices

called

Location

Improvement

with

Cluster

Analysis(LICA).Vehiclesareabletocollectreal-timedataandre-

laytheinformationtoothervehicles,guidingthedriverstoreach

their destination safely. To measure distance, time-of-arrival and

Received Signal Strength (RSS) techniques are used. LICA uses a

modifiedtri-alterationtechniqueinwhichmultiplemeasurements

can be taken for which the average value is used as the final

distance measurement resulting in a set of possible refinedxy

coordinatesonwhichaclusteranalysis isapplied,allowingmore

weight given to accurate data which results an improvement in

nodes location estimation. By using accurate distance measure-

ments,thelocationerrorisreducedinLICAandthusgivesbetter

performance.

Tianetal.[12] presentedaclusteringmethodbasedonavehi-

cles

position

and

moving

direction.

The

clustering

method

is

based

onEuclideandistance,whichusesthepositioninformationaswell

as themovingdirection todivide thevehicles intoclusters.Each

vehiclebroadcastsbeaconmessagethatincludeitsIDlatitude,lon-

gitude, direction and time to the whole network. The receiving

vehicle will first check the beacons hop count value and if the

numberofhopsislargerthanthemaximumvalue,it willdiscard

thisbeacon.Thensendervehicleupdatesitstopologytablebycal-

culatingthedistancebetweenthevehicles.Theclusterheadsare

generatedbyselectingthevehiclewithminimumdistanceparam-

eteras thecluster-head.Theremainingvehiclesare thendivided

intoclusters.

AdaptableMobility-AwareClusteringAlgorithmbasedonDes-

tination(AMACAD) [13] isbasedonfinaldestination invehicular

networks

to

enhance

the

clustering

stability.

It operates

in

a

dis-

tributedwaywiththefinaldestination,relativedestination,speed

andcurrentlocationofavehicleasparameterstocalculateamet-

ric called Fv,2 by exchanging message with its neighbors. This

manages

to

improve

the

lifetime

of

the

cluster

and

thus

decreasesthe number of cluster head changes. It implements an efficient

messagemechanismtorespond inrealtimeandavoidglobalre-

clustering.Thealgorithmisbasedoncertainassumptionslikethat

thedestinationofeachvehicle isknown. It isalsoassumed that

thecommunicationiscontentbasedandtheroutingisgeographic

based.Theminimumvalueof Fv,2 istheselectioncriterionused

byavehicle tojoinacluster.RegionGroupMobilitymodelpro-

posed in [14] wasalsomodified tomake itsuitable for VANETs.

In AMACAD,theauthorsevaluatedhowthevariationofthetrans-

mission range and speed affects the AMACAD performance. The

algorithm works well when average speed of vehicles is almost

constantwhichismosteffectiveinurbanareas.

Santos et al. [15] proposed Cluster Based Location Routing

(CBLR)

algorithm

to

choose

CHs

in

VANETs.

This

algorithm

is

based

on the regular transmission of beacons, which are used to dis-

tributethestateofthenodes.Accordingtothestatesofthenodes

nearby, a node chooses the appropriate state. To cope with the

changes in the topology, each node maintain a neighbor table,

in whichitliststhenodeswithwhichitcanexchangeinformation.

The update of this table can also be done according to received

beaconmessages.

2.2.1. Discussionondestinationbasedclustering

Table 2 compares the destination based clustering schemes.

In order to keep the clustering process stable, the frequency of

clusterchangesisminimizedbecauseavehicleonlyleavesaclus-

ter

when

it

encounters

a

CH

whose

destination

is

more

similarcompared to destination of current CH. Thus exploiting the ve-

hicularbehaviourby taking intoaccount thefinaldestinationsof

vehiclesenhancestheclusterstabilityandimprovesthetransmis-

sionefficiencyinmessagedelivery.It alsoresultsinhighercluster

connection time as the probability of a vehicle leaving a cluster

is generally low due to similarity in their destinations.However,

in case the number of vehicles in acluster becomes large, mes-

sagebroadcastresults inhigh transmissionoverhead.The impact

ofvehicledensityandvehiclespeedonclustering isalsonottoo

significantastheirvaluesgenerallyaremoredependentonchar-

acteristicssuchasexistingtrafficconditionsandroadscenariofor

destinationbasedclusteringprotocols.Thus theseprotocolsneed

tobe integratedwithalgorithms thatminimizemessageretrans-

missions

to

improve

their

efficiency.


5/19


Table 3

Comparisonoflane basedclustering schemes.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

Lane based clustering[16] LOW HIGH LOW MEDIUM MEDIUM LOW MEDIUM HIGH

BDA[17] MEDIUM HIGH MEDIUM L OW MEDIUM LOW MEDIUM HIGH

2.3.

Lane

based

clustering

Lane based clustering forms the cluster structure based upon

theestimationofvehicleslanewithrespecttocertainparameters.

Someoftheproposalsinthiscategoryareexplainedasfollows:

Fan et al. [16] proposed Broadcasting based Distributed Algo-

rithm (BDA) to stabilize the existing clusters that require only

singlehopneighborknowledgeand incurminimaloverhead.This

approachattemptstoimprovetheperformanceofclassicalcluster-

ingalgorithmsbymakingthemawareofthevehiclesmovement.

However,allnodesattempttore-evaluatetheirconditionsbycom-

puting utility values at the same time which may cause traffic

overhead and therefore consume more bandwidth. The authors

havealso theoreticallyanalyzed themessageand timecomplexi-

tiesofBDA.BDAgivesmaximumpriority toLeadershipDuration

for

cluster

formation,

which

is

difficult

to

compute

and

may

result

inlargeoverheadbyanodebeforeitjoinsacluster.

Almalagetal. [17]presentedalane-basedclusteringalgorithm

based on the traffic flow of vehicles. The proposed algorithm is

basedontheassumptionthateachvehicleknowsitsexactlaneon

the road through some lane detection system and in depth dig-

ital street map that includes lane information. It also uses GPS

combined with wheel odometer for lane detection of a vehicle.

The authors use the same general idea as the utility algorithm

in [16],butapplyadifferentsetof rules.Eachvehiclecomputes

andbroadcasts itsClusterHeadLevel (CHL)alongwith itsspeed

and other parameters. The vehicle with the highest CHL will be

selectedastheCH.CHLisdeterminedonthebasisofnetworkcon-

nectivitylevelofvehiclesandaveragevelocityoftrafficflow.The

proposed

algorithm

creates

CH

that

has

longer

life

time

as

com-paredtolowestnodedegreeforMANETs.

2.3.1. Discussiononlanebasedprotocols

Lane based clustering algorithms use the availability of lane

information to select stable clusters. Table 3 indicates that the

above two schemes have low number of CH changes that im-

proves the cluster stability. The transmission overhead of these

schemesisalsoreasonableonaccountofsmallnumberofretrans-

missions of broadcast messages since re-clustering is performed

only at lane intersections. These schemes have improved trans-

missionefficiencyduetobetterbroadcastingreachabilityandgood

clusterhead lifetimeasthevehicles inthesame lanemovewith

almost constant relative speed that results in highly stable clus-

ter

dynamics.

These

schemes

also

exhibit

small

delay

overhead

thatdemonstratestheirusefulnessformaintainingtheclustereven

forhighmobilityvehicularnetworks.Theclusterconnecttimefor

these clustering schemes is also reasonable. Thus the lane based

clusteringschemesexhibitgoodclusteringcharacteristicswiththe

constraintsthatplacementofthevehiclesshouldbeonthesame

lane.Theobservedvaluesofvehiclecharacteristicssuchasdensity

andspeedareonthelowerscalesincetheseprotocolsareadapt-

ableforurbanenvironmentduetheconstraintofvehicletravelling

inthesamelane.

3. Backbonebasedclustering

Backbone based clustering technique is based on forming a

backbone

for

cluster

communication.

The

backbone

then

performs

the communication and assists in CH election among the mem-bers

of

the

cluster.

Various

backbone

based

clustering

techniques

areclassifiedasfollows:

3.1.

k-hop

clustering

Inmultihopork-hopclustering,clusterstructureiscontrolled

bythehopdistance.Eachclusterhasoneofthenodesintheclus-

ter as the CH. Thedistances betweena CH and themembersof

theclusterarewithinapredeterminedmaximumnumberofhops

whichcanbeoneormorehops.Someoftheresearchproposalsin

thiscategoryareexplainedasfollows:

Zhangetal.[18] proposedamulti-hopclusteringschemebased

onthemobilitymetricforrepresentingN-hopmobility.A vehicle

is allowed to broadcast beacon message periodically and a vehi-

cle

calculates

Relative

Mobility

based

upon

two

consecutive

beaconmessagesreceivedfromthesamenodeinNhopdistance.Eachve-

hiclenode thencalculates theaggregatemobilityvalue,which is

the sum of relative mobility values into weight value for all the

neighboringnodesinN-hops.

Thevehiclenodesthenbroadcasttheiraggregatemobilityvalue

in theN-hopneighborhoodand thevehiclewithsmallestaggre-

gate mobility value is selected as the CH and the other vehicle

nodes work as cluster member nodes. The vehicle nodejoins a

cluster if it receives the beacons broadcast from the CH node.

When a vehicle node receives multiple beacon messages, it will

selecttheCHwhichisthe closestoneintermsofnumberofhops.

If severalCHshavethesamehopsthevehiclenodejoinstheclus-terwhichhasthelowestrelativemobility.

Zhang

et

al. [19] proposed

a

novel

k-hop

clustering

approach

that takes intoaccount thehighestconnectivity,vehiclemobility

andhostIDtoselectCH.Theproposedclusteringapproachmodi-fiesmaxmink-hopheuristicapproachdefinedin [20] forcluster

formation by considering highest connectivity in terms of signal

strengthandvehiclemobility.Thisscheme isabletodynamically

adjusttheperiodofannouncinglocationinformationaccordingto

vehiclevelocityinordertosuppresstransmissionoverheads.More-over, the distance-based converge-cast is deployed to collect all

memberships within the cluster, including the members located

ontheclusterborder.Anotherfeatureofthisapproachisitsability

toenhanceclusterstabilityduetovehicleactivationanddeactiva-

tionbyconsideringtheradiolinkexpirationtimeandthenumber

of vehicles connected to a cluster-head since they are essential

to keep vehicle in a cluster. Thus cluster-based topology discov-

ery

scheme

proposed

in

this

approach

utilizes

the

advantage

of

k-hopclusterarchitecturetoimprovethenetworktopologyscala-

bility.It improvesthenetworktopologystabilitywithacapability

totoleratefalseroutesandbalancetrafficloadsbyconsideringthe

inter-clusterlinkexpirationtime.By takingintoaccountthefactor

ofvehiclemobility,it reducestheoverheadandthelatencycaused

byroutepathrecovery.Wei et al. [21] proposed a robust Criticality-based Clustering

Algorithm (CCA) forVANETs thatemployednetworkcriticality to

direct theprocessofbuildingclusters.Networkcriticalityderives

itsrootsfromthedefinitionofrandomwalkbetweengraphs.It is

aglobalmeasureonagraphwhichquantifiestherobustnessofa

networkgraphtotheenvironmentalchanges,suchastrafficshifts,

topologymodifications,andchangesintheoriginanddestination

for

traffic.

To evaluate

the

performance

of

CCA,

it was

compared


6/19


Table 4

Comparisonofk-hop clusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

N-HOPCLUSTERINGUSING

RELATIVE MOBILITY [18]

MEDIUM MEDIUM LOW MEDIUM LOW MEDIUM HIGH HIGH

K-HOP CLUSTERING [19] MEDIUM MEDIUM LOW MEDIUM MEDIUM MEDIUM HIGH HIGH

CCA [21] LOW HIGH LOW MEDIUM LOW HIGH MEDIUM HIGH

HCA [22] LOW MEDIUM MEDIUM MEDIUM MEDIUM HIGH HIGH HIGH

withDBA-MACalgorithm thatemploysthehighestdegreeasthe

clusteringmetric.TheCCAalgorithmimprovesthelifetimeofclus-ters,and it reveals amorestablestructure formulti-hop mobile

wirelessnetworkssuchasVANETs.Dror et al. [22] proposed a distributed randomized two hop

clustering algorithm and named as Hierarchical Clustering Algo-rithm (HCA) that was influenced by the work presented in [23].

HCAformsTDMAlikesynchronizedclusters.In ordertoreducethe

number of collisions by simultaneous transmissions in the same

cluster,transmissionsareonlyallowedonassignedslotsbytheCH.

ThealgorithmdiffersfromotherclusteringalgorithmsforVANETsas it is capable of creating clusters with a larger span from the

CH.

It also,

does

not

require

the

knowledge

of

the

vehicles

loca-tions which contributes to its robustness. The algorithm handles

thechannelaccessanddoesnotassumeany lower layerconnec-

tivity.EventhoughHCAformsfewredundantclusters,theformed

clustersare muchmorestableand robust to topological changes

causedbyvehicularmovement.Howeverthemobilitypattern in-fluencesthealgorithmsbehaviourandhasagreatimpactonthe

clusterstability.Nevertheless,HCAalsosuffersfromsomedifficul-

tiesintermsofinterclusterinterferenceswhichcauseredundant

clusterchangesandmessagelossduetomessagecollisions.

3.1.1. DiscussiononK-hopclusteringprotocols

Multi-hop clustering algorithms shown in Table 4 utilize the

advantagesofk-hopclusterarchitecturetoimproveclusteringef-

ficiency.

It is

evident

from

Table 4 that

K-hop

clustering

schemeshavebetterclusterstabilityaswellas lowclusterdynamics.This

can be attributed to the reduced variation in CH and cluster-

member lifetime.Thusk-hopclusteringschemescanprovide im-proved and reliable performance for VANETs, especially for large

multi-hopwirelessnetworkswhennumberofvehiclesincreaseinthenetwork.However,theimpactofvehiclespeedandbehaviour

ofvehicledensityalsoneedsfurtheranalysisasitsaffecthasnotbeeninvestigatedindetailintheseprotocols.Althoughthesepro-

tocols have low cluster convergence time but they suffer from

interclusterinterferencewhichneedstobeanalyze forimproving

transmissionefficiencyandtransmissionoverhead.Theincreasein

transmissionefficiencyandhierarchicalclusteringstructureresultsin larger span as compared to single-hop cluster that results in

good

cluster

convergence

and

large

cluster

connection

time.

Theprotocolalsoneeds tobe further investigatedbyconsideringdif-

ferentvehicularcharacteristics.

4. MACbasedclustering

Several Medium Access Control (MAC) based clustering tech-

niqueshavebeenproposedforclusterformationinVANETs.These

techniques use IEEE 802.11 MAC protocol to generate clusters.

SomeofpopularMACbasedprotocolsarediscussedasfollows:

4.1.

IEEE

802.11

MAC

based

clustering

Suetal.[24] proposedaclusterbasedMultichannelcommuni-cationschemethatintegratesClusteringwithMACprotocols(CB-

MMAC).

The

proposed

scheme

mainly

consists

of

three core

pro-

tocolscalledClusterConfigurationProtocolthatgroupsallvehicles

inthesamedirection intoclusters.The Inter-clusterCommunica-

tionProtocolwhichdictates the transmissionsofreal-timesafety

messagesandnon-real-timetrafficsamongclustersovertwosepa-

rate IEEE 802.11 MAC-based channels respectively and the Intra-

cluster Coordination and Communication Protocol that employs

MultichannelMACalgorithmsforeachCHvehicleforcollecting/de-

livering safety messages from/to cluster-member vehicles using

theupstreamTime-DivisionMultiple-Access(TDMA)/downstream-

broadcastmethodandallocatingavailabledatachannelstocluster

membervehiclesfornon-real-timetraffics.

The

proposed

scheme

requires

the

use

of

two

transceiversoneused fordelaysensitivecommunicationwithin thecluster,while

theotherisusedforinter-clusterdatatransfer.

Boninietal.[25] proposedacross-layeredclusteringschemefor

fastpropagationofbroadcastmessageswhichiscalledasDynamic

Backbone Assisted MAC (DBA-MAC) scheme that may be consid-

eredanextensionoftheMACschemedescribedin[26].A dynamic

virtualbackboneinfrastructureisestablishedthroughadistributed

proactivetechnique.Thebackboneformationprocessconsidersthe

current distanceamongcandidatebackbone vehiclesand the es-

timated lifetime of the wireless connection among neighboring

backbonemember.Theauthorshavecompared theproposedso-

lutionwiththreesimilarproposals,a simple802.11MACflooding

scheme where each vehicle receiving an alert message and then

broadcasted

it

by

using

the

standard

IEEE

802.11

back-off

scheme,

a FastBroadcastprotocolproposedin[24]andthestaticbackbone

suchasroadsideinfrastructuresystemwhosenodesareplacedat

themaximumdistancepreservingtheconnectivity.DBA-MAChas

beenshown tobe compliantwith IEEE802.11DCFsystems,and

theperformanceofitshowsitsadvantagesinperformance,relia-

bility,andoverheadreduction.

4.1.1.

Discussion

on

IEEE

802.11MAC

based

protocols

MACbasedprotocolshave increasedpercentagecollisionsand

averagemessagedeliverydelaythatresultsinlowertransmission

efficiency and high transmission overhead due to increased con-

tentionwhennumberofvehiclesorspeedofthevehicleincreases.

Message

delivery

delay

is

mainly

caused

by

mobility

and

sparsedistributionofvehicles. It directly impacts theapplicationdesign

anddeploymentforVANETs.Liuetal. [27] identifiedmessagede-

liverydistanceanddensityofvehiclesastwomainfactorsforsuch

behavior basedonabidirectionalvehicletrafficmodel.Theconsid-

erationofbi-directionaltrafficalsoaffectsclusterconnectiontime

andresultsinlowerclusterconvergence.However,in theseproto-

colsthepercentagecollisionsandincurredoverheadfordelivering

safetymessagesisdecreasedbyreducingthechannelcontentions

forachievingtimelyandreliabledeliveryofsafetymessages.Due

tolowaveragerelativespeedamongclusterheads,theoverallim-

pactofvariationofvehiclespeedon theseclusteringschemes is

also low. Table 5 indicates thevariation in clusterdynamicsand

cluster stability that can be attributed to fluctuations in cluster

lifetime

and

cluster

size

in

these

protocols.


7/19


Table 5

ComparisonofMAC-basedclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

CB-MMAC[24] MEDIUM HIGH HIGH MEDIUM LOW LOW MEDIUM HIGH

DBA-MAC[25] MEDIUM LOW LOW LOW LOW LOW HIGH HIGH

Table 6

Comparison

of

TDMA

based

clustering protocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

CBMAC[30] LOW MEDIUM MEDIUM LOW MEDIUM MEDIUM MEDIUM HIGH

VeMAC [29] LOW LOW MEDIUM MEDIUM MEDIUM HIGH MEDIUM MEDIUM

TC-MAC[31] LOW LOW MEDIUM MEDIUM HIGH MEDIUM MEDIUM HIGH

4.2. TDMAbasedclustering

The process of assigning time slots can be scheduled using

TDMAtechniqueinwhichslotsareassignedfordatatransmission.

Someoftheproposalsinthiscategoryaredescribedasfollows:

Biswasetal. [28] proposedVehicularSelf-OrganizedMAC(Ve-

SOMAC) protocol based on a self-configuring TDMA slot reserva-

tion

protocol

which

is

capable

of

inter-vehicle

message

delivery

withshortanddeterministicdelaybounds.To achievetheshortest

delay,vehiclesdeterminetheirTDMAtimeslotbasedontheirlo-

cationandmovementontheroad.Also,theTDMAslotassignment

isdesignedtobeinthesamesequentialorderwithrespecttothe

vehicles physical location. The process of assigning time slots is

performedwithoutusinginfrastructureorvirtualschedulerssuch

asaleadervehicle.However,thisassumptionofforwardingmes-

sageswithoutprocessingtimeorpropagationdelayisunrealistic.

In reality,ifthemessageneedstobedeliveredfromthetailtothe

headoftheplatoon,it willneedatimeframeforeachhop.

Omar et al. [29] proposed a multichannel MAC protocol for

VANETs, called VeMAC, to reduce interference between vehicles

and reduce transmission collisions caused by vehicle mobility.

VeMAC

is

based

on

a

TDMA

scheme

for

inter-vehicle

communica-

tion.VehiclesinbothdirectionsandRSUsareassignedtimeslots

inthesameTDMAtimeframe.AlsoVeMACisdesignedbasedon

onecontrolchannelandmultipleservicechannelsinthenetwork

(as with DSRC/WAVE standards). VeMAC assumes that there are

twotransceiversoneachvehicleandthatallvehiclesaretimesyn-

chronizedusingGPS.Thefirsttransceiverisassignedtothecontrol

channel, while the second transceiver is assigned to the service

channels.

Gunteretal. [27] proposedclusterbasedmediumaccesscon-

trolprotocol (CBMAC),where theCH takeson amanagerial role

and facilitates intra-cluster communication by providing a TDMA

schedule to its cluster members. The CBMAC protocol uses an

adoptionofCBLRprotocolproposed in [13] forclusterformation.

Unlike

CBLR

which

is

based

on

regular

transmission

of

status

mes-sages, the frequency for sending thesemessagesdependson the

stateofnodeinCBMAC.In thisschemetheCHtakestherespon-

sibility toassignbandwidth to thememberof the clusterwhich

reducesthepacketcollisionsduetoIEEE802.11andalsoimproves

QoS.AlthoughCBMACminimizesthehiddenstationproblemand

provides better scalability, it depends on the CH every time a

new TDMA frame starts, which will lead to increased communi-

cationoverhead.CBMACalsodemonstratedthattheprobabilityof

a node will being CH for a short period of time is higher than

theprobabilitywhentheperiodislong.TrafficDensityalsoampli-

fiesthisaffectasincrease intrafficdensity increasesthenumber

of neighbouring CHs that are in transmission range. However in

CBMACneighbouringCHsareshowntooperatetogetherforacer-

tain

amount

of

time

rather

than

immediately

changing

their

state.

However,thissolutionleadstosomescalingissuesduetoCHsex-

changingtheirlocalschemawithconflictingCHs.Almalag etal. [31] proposeda newTDMACluster-basedMAC

(TC-MAC) that can be used for intra-cluster communications in

VANETs.Thisprotocolintegratesthecentralizedapproachofclus-

termanagementandanewschemeforTDMAslotreservation.The

mainobjectiveofthisworkwastoallowvehiclestosendandre-

ceive

non-safety

messages

without

any

impact

on

the

reliability

ofsendingandreceivingsafetymessages,even if thetrafficden-

sity ishigh.Theauthorsalsochangedtheconceptofhavingtwo

intervals by having vehicles listening to the control channel and

theservicechannelsduringthesamecycle.TC-MACalsoaimsto

decreasecollisionsandpacketdropsinthechannel,as wellaspro-

videfairnessinsharingthewirelessmediumandminimizingtheeffect of hidden terminals. TC-MAC is able to deliver non-safety

messages within reasonable time constraints, as well as meeting

therequirementsofminimumlatencyincaseofsafetymessages.

4.2.1. DiscussiononTDMAbasedclustering

Theaccess tothemediumwithinacluster isbasedonTDMA

which isprimarilyusedforoptimizingthecommunication.These

clustering

protocols

reduce

intra

cluster

collisions

as

well

as

packetlosscomparedtotraditionalclusteringprotocolsandthusprovide

fairnessinsharingthewirelessmediumforVANETs.Table 6shows

thatTDMAalgorithms have relatively smaller delayof multi-hopsafety messages as compared to other clustering schemes. Thus

theyprovidebettertransmissionefficiencyforclustermaintenancewhich improves the overall throughput of both inter-cluster and

intra-clustercommunication.Theseprotocolsalsoexhibithightransmissionoverheaddueto

extracostofchannelassignmentforTDMAslots.Howeverbyusingsomeoptimizationtechniquestheperformanceoftheseprotocols

canbefurtherenhanced.Calafate al. [32] proposedaschemethat

minimizes the content delivery time by seeking optimal packet

size for content delivery. Thus TDMA based clustering schemes

have

various

transmission

characteristics,

but

their

behavior

needs

further

analysis

on

traditional

vehicle

characteristics

such

as

vehi-

clespeedanddensity.Clusterstability isalso low for thesepro-tocols.Althoughclusterconnecttimeiscomparativelyreasonable,

buthighclusteringconvergenceduetoTDMAtimeslotisaserious

bottleneckinimplementingtheseprotocolsinVANTEs.

4.3. SDMAbasedclustering

In SDMA based protocols, the road is subdivided into fixedlength segments, and a segment is divided into a fixed number

of blocks. Each block is assigned a timeslot representing the al-lowedtimeforavehicletotransmitdata.SDMAisknowntohave

betterperformanceinadensenetworkwherepracticallyallslots

areused.But, theperformancedecreasesproportionallywith the

density.

Hence,

in sparse

networks,

SDMA

gives

poor

performance.


8/19


Table 7

ComparisonofSDMA-basedclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

CDGP[36] MEDIUM LOW LOW LOW LOW MEDIUM HIGH HIGH

Traffic Gather[34] HIGH LOW LOW LOW MEDIUM MEDIUM HIGH LOW

Fig. 2. Classification of traditional clustering.

Salhi et al. [33] proposed a protocol for hybrid vehicular ar-

chitecture,calledClusteredGatheringProtocol(CGP).Theprotocol

is designed to provide real-time data (e.g. average speed of ve-

hicle) related tospeedofvehicleetc. tobasestation.Thechoice

oftheclosestnodewhen it isattheendofthesegmentcan in-

creasethedelayinclusterformationbyrepeatedlyrunningofthe

electionprocedure.Thisalsoresultsinincreaseddelayinmessage

propagation inCGP.Theuseof IEEE802.11DCFcanalsoprevent

theestablishmentofpossiblecommunicationsbetweenvehiclesin

neighbouringsegmentsevenafterreceivingaCleartoSend(CTS)

packetsentbytheneighbouringCH.AnotherdrawbackofCGP is

thatitdoesntdefineanyretransmissionmechanismtodealwith

thereceptionoferroneousdata.

Chang

et

al.[34,35] proposed

a

different

dynamic

cluster

based

vehicle to vehicle protocol using SDMA. The protocol was called

TrafficGather.Thisprotocol inheritsallthedrawbacksoftheuse

ofastaticmediumaccesstechniqueinwiredorsensorsnetworks.

Thus, in the case of sparse density, many allowed slots will not

beutilized.AlthoughthereliabilityofSDMAincreasesinthecase

ofdensenetwork,useoffloodingtechniquemaycauseabroadcast

stormproblemevenwithoutusingamechanismofretransmission.

Brik et al. [36] proposed a new data collection protocol for

vehicular environments called Clustered Data Gathering Protocol

(CDGP). The use of a clustering technique in hybrid architecture,

Dynamic SDMA in the data collection phase and retransmission

mechanism to deal with erroneous data is the major character-

istics of CDGP. It avoids collision problems by implementing a

centralized,

dynamic

medium

access

technique,

and

enhancing

thereliabilitybytheintegrationofretransmissionmechanism.

4.3.1. DiscussiononSDMA-basedclusteringprotocols

Table 7showsthatSDMAbasedclusteringprotocolsshowaver-

ageclusteringconvergenceandtransmissionefficiency.Thisisdue

to the data collection time and number of time slots increasing

linearlyatapproximatelyconstantrateforthediscussedprotocols.

The SDMA mechanism also affects clustering overhead in terms

of packet delivery ratio. The vehicle density also influences the

message transmission time and results in lower cluster stability.

SDMA based schemes also have larger cluster connect time due

to re-clustering frequency being high. The throughput and num-

ber of transmissions also have an impact on cluster dynamics.

These

protocols

also

need

to

be

investigated

to

improve

vehicu-

larparameterssuchasclusterstabilityandclusterdynamics.The

clusterconnect timeandvehicledensityaffects theperformance

ofSDMA-based clusteringschemes.The vehicle densitydegrades

themessagetransmissiontimefortheseprotocols.HoweverSDMAbased clustering protocols can be used for providing V2I wire-

lesscommunicationsuchasIntelligentTransportationSystem(ITS)

in the near future. Initial investment costs could discourage the

deploymentofaubiquitousroadsideinfrastructuretosupporton-the-road networks and their absence implies discontinuous cov-erageandshort-livedconnectivity [37].Theschemeproposedby

Salhietal. [33] has notbeendiscussed inTable 7 as itdoesnot

specifyanysimulationresults.

5.

Traditional

clustering

This section discusses the Traditional Clustering techniques

usedinVANETs.Thesetechniquesaresubdividedintoactiveand

passiveclusteringbasedupon theroleofnodes inVANET.Fig. 2showsthesubcategoryofeachofthesetechniquesinVANETs.

5.1.Activeclustering

Incaseofactiveclusteringprotocols,therearecontinuousup-

dates of the clustering information and routing table for route

discoveryafterafixedintervaloftime.Theygenerallyinitiateclus-

teringprocessthroughfloodingwhichgeneratesasustainedrout-ingoverhead.ThevariousActiveClusteringprotocolsaredescribed

asfollows:

5.1.1. Beaconbasedclustering

InBeaconbasedclustering,clustersareformedbasedonsome

vehicularornetworkparameterdetectedbybeaconsofhellomes-sagesbythereceivingvehicle.Littleetal. [38] proposedabeacon

based clustering model, which is an extension of the algorithm

proposedin [2,5].In thisapproach,theclustersareformedbased

on mobility metric and the signal power detected at the receiv-ingvehiclesonthesamedirectedpathway.RSSvalueisusedasa

criteriatoassignweightstothenodesandbasedonthisweights

theCHiselected.Usingthismethod,theproposedprotocolhelps

in formingstableclusters.However, it doesnotconsider theoc-currence of losses in the wireless channel. In practical scenario

effectsofmultipathfadingareboundtoaffecttheclusterforma-

tion

method

and

thus

the

stability.


9/19


Table 8

Comparisonofbeacon-basedclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

DPP[38] LOW MEDIUM LOW LOW LOW MEDIUM MEDIUM LOW

ER-AC[39] HIGH LOW LOW LOW MEDIUM MEDIUM HIGH LOW

LORA-CBF[41] LOW LOW LOW LOW LOW MEDIUM MEDIUM MEDIUM

Teshima

et

al. [39] proposed

an

active

clustering

scheme

that

combinesthetraditionalEpidemicroutingwithautonomousclus-

teringSchemeproposedin[40].Theyconsideredacompleteclus-

terasasinglevirtualnode,andonlytheCHstoresdatapackets.

Wheneveracluster,encountersanewneighboringcluster,theCH

forwards data packet to the CH of the neighboring cluster. The

datapacketsareforwarded tothedestinationnodebasedonthe

hierarchical treewith the CH at the root. The CH constructs the

CH-basedtreethatcontainsthelistofclustermemberstomanage

itscluster.Theproposedscheme ismoreefficientwithdatastor-

agesinceitstoredatapacketsonlyintheclusterheadandallthe

nodesdonothavetostoredatapackets,whichresult inreduced

consumedpacketbuffer.

Santos et al. [41] presented a reactive location based routing

algorithm

that

uses

cluster-based

flooding

for

VANETs

called

LORA-CBF.Thisclusteringapproachisbasedonregularbeacontransmis-

sionswhichadvertisethestateofthenode.EachnodecanbeaCH,

gatewayorclustermember.TheCHmaintains informationabout

itsmemberandgatewayspackets.Basedonthestateoftheneigh-

bouring nodes, a node can select its own state. A CH will only

considerachangeofitsstateifitreceivesamessagefromanother

CH.A CHreceivingahellomessagefromanotherCHwillremain

inthesamestateifithasmoreMobileNodesonitsclusterthan

thesender.Thissimplecriterionfavors largerclustersanddoesnot

takeintoaccountthemobilityoftheclustermembers,howcohe-

sivethesmallerclusteris,or iftheclustersaremovinginopposite

directions.Also,withlargeneighbourhoods,theclusterswillhave

thetendencytogrowuncontrollably,thuspotentiallyoverloading

their

CHs.

5.1.1.1. Discussiononbeacon-basedclusteringprotocols Beaconbased

clusteringprotocolsprovideincreasedtransmissionoverheadespe-

ciallydueto increase innumberofvehiclesandhop-counts.The

effectoftransmissionefficiency intermsofvolumeofconsumed

packetbufferandend-to-enddelayishighbutdecreasesgradually

withanincreaseinsizeofthenetworkwith vehicledensity.This

indicatesanefficientmessagedeliveryatlowtrafficbutthepacket

deliveryratiostartsdegradingashopcountornumberofvehicles

increases.

The periodic transmission of beacons helps in cluster conver-

gencebutitalsoaffectsthethroughputofvehicularnetwork,es-

pecially at higher traffic density and ultimately the transmission

overheadasshown inTable 8.Theprotocolscanbemodifiedby

using

some

quota

based

protocols

like

TTL

Based

Routing

as

de-

scribedin [42] thatrestrictsthemaximumnumberofcopiesofa

message in thenetworkaswellasenhances thechanceofmes-

sage delivery. This will assist in improving the cluster dynamics

thathasrelativelylowervaluesforallthethreeschemesdiscussed

above.Theimpactofvehiclespeedalsoneedstobeimprovedfor

theseprotocols.

5.1.2. Mobilitybasedclustering

Maglarasetal.[43] proposedadistributedclusteringalgorithm

which forms stable clusters based on force directed algorithms.

Everynodeappliestoitsneighboursaforceaccordingtotheirdis-

tanceandtheirvelocities.Vehiclesthatmovetothesamedirection

ortowardseachotherapplypositiveforceswhilevehiclesmoving

away

apply

negative

forces.

According

to

the

current

state

of

the

node

and

the

relation

of

its

F to

neighbours F,

every

node

takes

decisionsaboutclusteringformation,clustermaintenanceandrole

assignment. This work also proposed mobility metric based on

forcesappliedbetweennodesaccordingtotheircurrentandtheir

futurepositionandtheirrelativemobility.

A new stability-based clustering algorithm (SBCA) [44], that

aims to reduce the communication overhead that is caused by

theclusterformationandmaintenance,as wellastoincreasethe

lifetime of the cluster. SBCA makes use of mobility, number of

neighbours,and leadershipor CHduration in order to providea

morestablearchitecture.Thenodesremainassociatedwithagiven

cluster and not with any CH as is the case with most existing

clusteringapproaches.WhenoneCHisnolongerinthecluster,an-

otherCHtakesover;theclusterstructuredoesnotchangebutonly

the

node

playing

the

role

of

CH.

This

allows

for

stable

cluster

archi-tecture,withlowoverheadandbetterperformance.SBCAprotocol

significantly improves the cluster residence time, for each node,

reducingtheoverheadandthusimprovingtheperformance/relia-

bilityalso.

Souzaetal.[45]presentedabeacon-basedclusteringalgorithm

forprolongingthecluster lifetime inVANETsbyusinganewag-

gregatelocalmobilitycriteriontodecideuponclusterre-organiza-

tion.A nodesALMisthevarianceoftherelativemobilityoverall

neighbours.Twonodesmovingclosertogetherresultinanegative

mobility.A lowervariancemeanslessmobilityofthenodeinre-

lation to itsneighbours.The intuitionbehind thisscheme isthat

anodewith lessvariance relative to itssurroundings isabetter

andmorestablechoiceforCH.Theproposedclusteringalgorithm

displays

a

better

performance

in

terms

of

stability.

However

since

thenodesarehighlydynamicinnaturethepositionofthenodes

changeveryfastandhencemayinduceacomputational overhead

incalculatingtheweightassociatedwiththenodes.

The algorithm called, Affinity Propagation for Vehicular net-

works (APROVE), [46] a distributed mobility-based clustering

scheme that forms cluster with low relative velocity between

Cluster Members also gives stable clusters by improving cluster

headduration,clustermemberduration,andreducingrateofCH

change. The proposed clustering technique uses the fundamen-

tal ideaofAffinityPropagationproposedby [47] whichhasbeen

showntoproduceclustersinmuchlesstime,andwithmuchless

error than previous techniques [48]. The proposed algorithm is

validatedbycomparingittothemobility-basedad-hocclustering

scheme,

MOBIC [41] and

simulation

results

confirm

the

supe-rior performance of APROVE, when compared to other accepted

mobility-basedclusteringtechniques.

Kayis et al. [49] addressed mobility by first classifying nodes

intospeedgroupssuch thatnodeswillonlyjoinaCHofsimilar

velocity. Code Division Multiple Access scheme is used to assign

orthogonalcodestothepreviouslyidentifiedvehicularnodes.Ev-

ery vehicle knows the speed group to which it belongs prior to

cluster formation phase. The data packet header transmitted by

thesendingnode ismodifiedbyaddingspeedgroup information

to it. If a major speed change occurs and the node moves at a

speedoutofitsclusteringgroupintervalduringathresholdvalue

speed T,it updatesitsspeedandclusteringgroupinformationand

thenodeseeksanothercluster tojoin. In thisway, the timeun-

til

a

cluster

member

leaves

the

communication

range

of

its

CH

is


10/19


Table 9

Comparisonofmobility-basedclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

APROVE[46] LOW HIGH MEDIUM MEDIUM MEDIUM LOW HIGH MEDIUM

SBCA [44] MEDIUM MEDIUM MEDIUM LOW LOW LOW LOW HIGH

SP-CL[43] LOW HIGH MEDIUM HIGH LOW LOW MEDIUM HIGH

Table 10

Comparisonofdensity-basedclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

DBC[51] HIGH HIGH LOW MEDIUM LOW MEDIUM MEDIUM MEDIUM

D-CUT [50] LOW LOW LOW MEDIUM LOW MEDIUM LOW LOW

extended whichincreasesthelifespanofthecluster.Consequently,

nodetransitionratebetweenclustersisalsodecreased.

5.1.2.1. Discussion on mobility based clusteringprotocols Mobility

based clustering protocols minimize relative mobility as well as

distanceofeachCH to itsclustermembers,so they improve the

cluster convergence and cluster dynamics. Table 9 indicates that

the

discussed

protocols

have

better

cluster

stability

that

can

be

attributed to reduced values of clustering overhead and average

numberofclusterheadchanges,therebycreatinglesserandmore

stableclusters.Thisimprovedstabilityhelpsinimprovingtheper-

formanceaswellasreliabilityofVANETsandthusmakemobility

basedclusteringprotocolsforthoseenvironmentswhichhavedy-

namicbehavior andwheremobilitycanberepresentedefficiently.

Howevervehicledensityandvehiclespeedwhicharethepredomi-

nantfactorsaffectingthemobilityneedtobeinvestigatedinmore

detail for all these protocols. It can also be concluded from Ta-

ble 9 thatthepacketdeliveryratioforthediscussedprotocolshas

alowervaluewhichindicatesadecreaseintransmissionefficiency

andincreasedtransmissionoverhead.

5.1.3.

Density

based

clustering

Yairet al. [50] proposed an iterative algorithm named as Dis-

tributed Construct Underlying Topology (D-CUT) in which each

nodediscoversandmaintainsageographicallyoptimalclustering

forthecurrentnetworkconfiguration.D-CUTalgorithmpartitions

thenetworkintogeographicallyoptimizedclusters.Theprotocolis

appliedintwophases.In firstphase,beaconsinthesamecluster

areaggregatedbyaCH inasynchronizedmanner. In thesecond

phase,theCHdisseminatesacompressedaggregatedbeaconofits

ownclusterto itsadjacentclusters.Thevehiclesproduceasnap-

shot of the surrounding vehicle map, and update the clustering

solution according to the changes in the network configuration.

All neighboring vehicles share matching partitioned vehicle map

producing thesamenewpartitionedmap foreachvehicle in the

network.

The

algorithm

updates

the

partitioning

according

to

the

most recent topologicalchanges thus maintaining thegeographi-

callyoptimizedclusters.

Kuklinshi et al. [51] proposed a multi-level cluster algorithm

calledtheDensityBasedClustering(DBC)basedonseveralfactors

likeconnectivity level, linkquality,relativenodepositionpredic-

tion of a nodes position in future and node reputation. This al-

gorithmhasthreephases. In thefirstphase,a nodeestimates its

connectivityleveldefinedasnumberofconnection,whichisused

to discover density of local neighborhood of a node. Every node

countsthenumberofreceivedacknowledgmentstofindthenum-

ber of active links. This information determines whether a node

belongstothedenseorsparsepartsofnetworksbycomparingthe

connectivitylevelagainstathresholdvalue.Theaimofthesecond

phase

is

to

select

stable

links

from

all

the

current

links.

This

se-

lectionismadeonsomepredictionaboutfuture,butitalsotakes

intoaccountthepastknowledgeofspeedanddirectionofvehicle.

This is the basis for estimating the links quality. In this evalua-

tionavehiclealsousessignal-to-noiseratioofthelink.In thelast

phasecommunicationhistory isusedtodeterminenodesreputa-

tionbeforeitbecomesaclustermember.Theeffectsofmultipath

fadingarealsotakenintoaccountinthisdensitybasedclustering

algorithm.

5.1.3.1. Discussionondensitybasedclusteringprotocols Densitybased

clustering protocols allow strong connections between cluster

membersandlowvariationinnumberofclusterheadchangesthat

resultsinimprovedclusterstability.Table 10indicatesthatcluster

stabilityishighfordensitybasedprotocolsirrespectiveofnumber

ofvehiclesinacluster.Thedensityinformationhelpsinimproving

theawarenessineachvehicleaboutthecompositionofitscluster

andprovidesstrongconnectionsbetweenclustermembersforcre-

atingamorereliableclusteringtopologythatprovidescomparable

clusterconvergence.Howeverfurtherstudyneedstobedone for

improvingthetransmissionefficiencyandtransmissionoverheads.

Dependence only on density limits the cluster connect time and

cluster

dynamics.

The

vehicle

speed

also

needs

to

be

further

in-

vestigatedasithasadirectimpactondensityofthevehicles.

5.1.4. Dynamicclustering

VANETs have relatively more dynamic nature as compared to

MANETs resulting in fast changes in the network topology. The

designand implementationofanefficientandscalablealgorithm

for information dissemination in VANETs is a major issue that

should be tackled. Indeed, in this dynamic environment, an in-

creasing number of redundant broadcast messages will increase

resourceutilization,whichwouldindirectlyaffectthenetworkper-

formance [52].Dynamicclustering technique formsclusterstruc-

turebasedonnodedynamics likemobilitypatterns,velocityand

density.

Kakkasageri

et

al. [53] developed

a

multi

agent

based

dynamic

clustering scheme for VANETs. The scheme comprises of heavy-

weightstaticand light-weightmobileagentsthat formamoving

dynamicclusteronalanebetweentwointersectionsbyconsider-

ing parameters such as vehicle speed, direction, connectivity de-

greetoothervehiclesandmobilitypattern.Initially,clustermem-

bersareidentifiedbasedonvehiclesrelativespeedanddirection

for dynamic clustering. CH is selected among the cluster mem-

bers based on stability metric derived from connectivity degree,

averagespeedand timeto leavetheroad intersection. It consists

of a set of static and mobile agents. The relative speed differ-

enceamongneighbouringvehiclesisthemainparameterusedfor

clusterformation.Theneighbourvehiclestraveling inthesamedi-

rectiononalaneareonlyconsidered.Theclustermemberwiththe

highest stability

metric

is

considered

as

the

CH.

This

scheme

also


11/19


Table 11

Comparisonofdynamicclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

MULTIAGENTDRIVENDYNAMIC

CLUSTERING [53]

LOW MEDIUM LOW LOW MEDIUM LOW MEDIUM LOW

VWCA [54] MEDIUM LOW LOW MEDIUM MEDIUM LOW MEDIUM LOW

Table 12

Evaluationofpassiveclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

PASSCAR [56] MEDIUM LOW HIGH MEDIUM MEDIUM LOW LOW LOW

hascertain limitationssuchasallvehiclesneedtohaverelatively

strong computational resources, capability of authenticating and

validationofvehiclesduringdynamicclusteringprocess,whichis

notpracticalforcurrentvehicularnetworks.

Daeinabi et al. [54] proposed a novel clustering algorithm,

vehicular

clustering

based

on

the

Vehicular

Weighted

ClusteringAlgorithm (VWCA) that takes into consideration the number of

neighbours based on dynamic transmission range, the direction

of vehicles, the entropy model proposed in [55], and the dis-

trustvalueparameters.Theseparameterscanincreasethestability

andconnectivityandcanreducetheoverheadinnetwork.VWCA

works with an Adaptive Allocation of Transmission Range tech-

nique,wherehellomessagesanddensityoftrafficaroundvehicles

areusedtoadaptivelyadjustthetransmissionrangeamongthem.

VWCAusesdistrustvalueintheweightedsumoperation.Thedis-

trustvaluehasbeenobtainedfromproposedMonitoringMalicious

Vehicle algorithm. Using distrust value, vehicles that have lower

distrustvalue than their neighboursareelectedas cluster-heads.

Therefore, cluster-heads are more trusty vehicles than other ve-

hicles

in

the

network.

The

VWCA

technique

mainly

focuses

on

improving

the

CH

duration,

membership

duration

and

security.

Us-

ingVWCA,communicationoverheadsrequiredforjoiningtoanew

clusterinnetworkdecreasesbecausethemembershipdurationfor

each vehicle has increased. In addition, using the entropy term

intheweightedsumoperation,VWCAcanreducethenumberof

overheadscreatedbyhighspeedvehicles.Furthermore,VWCAcan

increasenetworkconnectivitywhenelectingcluster-heads.

5.1.4.1. Discussion on dynamic clustering basedprotocols Table 11

shows the comparison of Dynamic clustering protocols. It shows

that cluster dynamics only has an average value that can be at-

tributedtodecreaseinclusterlifetimewithvariablerates.Theve-

hicledensityalsohasanegativeaffectonclusterstability.Initially,

thestabilityisrelativelyhighbutitdecreaseswithnetworkload.

The

transmission

efficiency

has

a

comparable

value

with

other

clusteringtechniquesthatcanbeattributedtomoderaterangeof

valuesofcontroloverheadandpercentageconnectivity.Sincethese

schemesalsohavepermissibleoverheadandconnectivity,so these

providemoreflexibilityandadaptabilityandcanbeconsideredas

agoodadd-ontoexistingclusteringschemes.However,theimpact

ofvehiclespeedneedstobeinvestigatedforrealisticvehiclesce-

narioand impactoftheseclusteringprotocolsonclusterconnect

timealsoneedsfurtheranalysis.

5.2. Passiveclustering

Passiveclusteringisaclusteringmechanismthatpassivelycon-

structsaclusterstructure [21,56].At anytime,a nodeinacluster

possesses

an

external

or

internal

state.

In passive

clustering

each

vehiclecan lower thecontroloverhead inpacketfloodingby the

useofon-goingdatapacketsinsteadofextraexplicitcontrolpack-

etstoconstructandmaintaintheclusters.Whenanodereceives

datapackets,it maychangeitsclusterstatebasedonthestatein-

formationpiggybackedinon-goingdatapackets.Thisreducesthe

number

of

explicit

control

packets.

Thus

Passive

clustering

mecha-nismgeneratessignificantlylessoverheadforclustermaintenance

thanthe traditionalcluster-based techniquebecause itsnodesdo

notmaintainclusterinformationallthetime.

Wang et al. [56] proposed a Passive ClusteringAided Routing

protocol forVANETs (PassCAR) that refines thepassiveclustering

mechanismproposedin [57]whosemaingoalwastoconstructa

reliableandstableclusterstructureforenhancingtheroutingper-

formance in VANETs. The proposed mechanism also includes the

routediscovery,routeestablishment,anddatatransmissionphases.

The main idea behind PassCAR was to select suitable nodes to

becomecluster-headsorgateways, which then forward route re-

questpacketsduringtheroutediscoveryphase.PassCARassesses

thesuitabilityofnodesusingamulti-metricelectionstrategy.

This

strategy

considers

link

reliability,

link

stability,

and

link

sustainability

as

the

main

factors

and

quantifies

them

using

the

metricsofnodedegree,expectedtransmissioncount,andlinklife-

time,respectively.EachCHorgatewaycandidateself-evaluatesits

qualification forCHorgatewaybasedonapriorityderived from

aweightedcombinationoftheproposedmetrics.PassCARdesigns

an efficient passive clustering based mechanism that operates at

thelogicallinkcontrolsub-layer,andtheproposedmechanismcan

easily be associated with any routing protocol to support stable,

reliable,andpermanentdatadelivery.

5.2.1. Discussiononpassiveclustering

ThenumberofclustersconstructedusingPassiveClusteringas

showninTable 12 remainssteadywithvaryingvehicularconcen-

tration which indicates medium cluster stability. This can be at-

tributed

to

the

consideration

of

node

degree

as

a

key

parameter

in

thisprotocol.Theachievedtransmissionefficiencyisalsocompara-

blewithotherclusteringprotocols.However,duetoconsideration

oflinkquality,passiveclusteringhashighoverheadforclusterfor-

mation and maintenance especially in urban environment where

obstacleshaveaneffecton linkquality.Thisdegradesthecluster

connecttime.Passiveclusteringalsohaslowerclusterconvergence

due to the use of information being piggy back in ongoing data

packets.

6. Hybridclustering

Hybrid clustering techniques combine two or more existing

techniquessuchasuseofartificialintelligence,fuzzylogicetc.Fol-

lowing

are

the

schemes

in

this

category

of

clustering.


12/19


Table 13

Comparisonofintelligence-basedclusteringprotocol.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

FUZZY BASED CH ALGO [58] MEDIUM HIGH LOW HIGH LOW LOW HIGH LOW

ALCA[59] HIGH HIGH LOW HIGH LOW LOW MEDIUM LOW

MULTIAGENTDRIVENDYNAMIC

CLUSTERING [53]

MEDIUM HIGH MEDIUM MEDIUM LOW LOW HIGH LOW

Table 14

Comparisonofcooperativede-centralizedclustering.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

QuickSilver[61] LOW LOW HIGH LOW MEDIUM HIGH HIGH MEDIUM

6.1. Intelligencebasedclustering

Hafeez et al. [58] proposed a distributed and dynamic Clus-

ter head selection criteria to organize the network into clusters.

CH iselectedbasedonstabilitycriteriawhichreflecttherelative

movementbetweenadjacentvehicles.Thevehiclesaccelerationis

alsousedinthisworktopredictitsspeedandpositioninfuture.

However,

the

decision

to

accelerate,

to retardation

or

to

stay

on

the same speed depends on many factors such as the distance

betweenthevehicleanditsfrontneighbour,therelativespeedbe-

tween them, theroadconditionsand thedriversbehavior.Since

the drivers behaviors and how they estimate the inter distance

and other factors are subjective, so triangular fuzzier is used to

dealwiththisuncertaintyusingthefuzzy logicinferencesystem.

The proposed scheme can achieve a highly stable cluster topol-

ogywhichmakesitmoresuitableforimplementationinVANETs.

However,thedistributedprocessingoverheadresultsinadecrease

inmessagetransmissionefficiency.

Kumaretal.[59] proposedanAgentLearning-basedClustering

Algorithm(ALCA).Agentsareableto learnfromtheenvironment

inwhich theyoperateandperform thetaskofCHselection.The

proposed

approach

consists

of

selection

of

CH

keeping

in

view

of

thedirectionofmobilityanddensityof thenodes.Thedirection

of themobilityof thenodes iscalculatedby theagent inan in-

teractive manner. Agent learns from the direction of motion of

thevehicleandtrafficflowacrossdifferentjunctionsoftheroad.

Agentsaredeployedatdifferentroadjunctionsformonitoringthe

activitiesofthevehicles.Agentsperformtheiraction,andaccord-

ingly, their actions are rewarded or penalized in unit steps. The

densityofthevehiclesandaveragespeedareusedfordividingthe

timeintodifferentzones.Thesezonesarethenusedforcollecting

theinformationaboutthevehiclesusedasinputtotheagentsfor

clustering.Learningrateisalsodefinedfortheagentstotakethe

adaptive decisions.Foreachactionperformedby theagents, the

corresponding action is rewarded or penalized, and value of the

learning

parameter

is

incremented

or

decremented.

This

process

continuesuntilthemaximumvalueisreached.Theperformanceof

theproposedschemeisevaluatedbyvaryingthenumberofagents

withvariousparameters.Theresultsobtainedshowthatthepro-

posedschemecanbeusedinthefutureapplicationsinVANETs.

6.1.1.

Discussion

on

intelligence

based

clustering

protocols

Table 13showsthatalltheintelligencebasedclusteringproto-

colshavegoodclusterstabilitywhichisduetolargeCHduration

andclustermemberduration.Thevalueoftheseparametersalso

improvesasthevehicledensityincreases.Theseprotocolsgenerate

reasonably stable clusters but they also cause large transmission

overheadwhichreduces thepacketdeliveryratioresulting inre-

ducedtransmissionefficiency.Sincehybridtechniquesorheuristics

can

be

used

for

cluster

formation,

the

additional

overhead

results

inhighclusterdynamics.Thus,theseprotocolscanbeagoodalter-

nativeforuseinfuturevehicularnetworksorforthosenetworks

thatimplementaspecificapplicationlikesecurityandmultimedia

applications.

6.2.

Cooperative

de-centralized

clustering

Cooperative

vehicular

systems

are

currently

being

investigated

todesigninnovativeITS solutionsforroadtrafficmanagementand

safety.Throughvariouswirelesstechnologies,cooperativesystems

cansupportnoveldecentralizedstrategiesforubiquitousandcost

effectivetrafficmonitoringsystem[60].

QuickSilver [61] is a light weight distributed clustering pro-

tocol that integrates a traditional source routing protocol for in-

tra cluster node centric communication and the construction of

a multichannel link for contention free inter cluster data centric

communication. It isasystemarchitecture thatprovidesefficient

useofavailableresources toguarantee thatnoharmfulcompeti-

tion takes place for the channel bandwidth. QuickSilver employs

lightweight-clusteringwhereclustersformandbehaveinanunco-

ordinatedmannerwithoutrequiringaclusterIDandthereareno

CHs.

QuickSilver

utilizes

two

radio

interfaces

that

allows

vehicle

to

maintaintheirintraclusterconnectivityandatthesametimelook

forinterclustercontactopportunities.Clusterformationandmain-

tenanceisdonebybuildingaclusterformationandmanagement

listofneighborsateachnode.It focusesoncreationofstablelinks.

6.2.1. Discussiononcooperativede-centralizedclustering

Table 14showsthattheseclusteringprotocolshavelowcluster

stabilityandaverageclusterconnecttime.This isduetothefact

thataveragenumberofinter-clusterlinksthatareactivewhenve-

hicles are in contact initially increases as the overlapping region

foravehicleincreasesandthenithasadecreaseasvehiclesmove

away fromeachother.Transmissionefficiencyalsohascompara-

ble value for these protocols. The effect of channel assignment

on

different

node

densities

is

represented

in

the

form

of

num-beroflinksthatshowsanincreasewiththenumberofchannels.

This indicates the effectiveness of the protocols for inter-cluster

communicationasshowninTable 14.However,hightransmission

overhead and lower vehicle density results in reduced effective-

ness for intra-cluster communication in decentralized clustering

schemesconsideringrealisticvehicularspeedconditions.

6.3. Driverbehavior basedhybridclustering

Vehicles arenowadays provided withavarietyofsensors ca-

pable of gathering information from their surroundings. In near

future, thesevehicleswillalsobecapableofsharingall thehar-

vestedinformation,withthesurroundingenvironmentandamong

nearby

vehicles

over

smart

wireless

links.

They

will

also

be

able

to


13/19


Table 15

Comparisonofdriverbehaviorbasedhybridclusteringprotocols.

Vehicle

density

Cluster

stability

Vehicle

speed

Cluster

dynamics

Transmission

efficiency

Clustering

convergence

Transmission

overhead

Cluster

connecttime

CellularAutomata

Clustering[64]

LOW HIGH LOW HIGH HIGH LOW MEDIUM LOW

COIN[63] LOW MEDIUM LOW MEDIUM MEDIUM LOW LOW LOW

generate

alerts

at

regular

intervals

for

emergency

services

such

as

incaseofaccidents[62].

Bl

Documents

Clustering in Vehicular Ad Hoc Networks Taxonomy, Challenges and Solutions