CarAgent: Collecting and disseminating floating car data in vehicular networks

Contents lists available atScienceDirect

Vehicular

Communications

www.elsevier.com/locate/vehcom

CarAgent:

Collecting

and

disseminating

floating

car

data

in

vehicular

networks

Hui Huang

a

,

b

,

∗

,

Mahbub Hassan

a

,

b

,

Glenn Geers

a

,

c

,

Lavy Libman

d a_{School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia}

b_{Data61, CSIRO, 13 Garden St, Eveleigh, NSW 2015, Australia} c_{ARRB Group Ltd, 2-14 Mountain St Ultimo, NSW 2007, Australia} d_{Google Australia, 48 Pirrama Rd, Pyrmont, NSW 2009, Australia}

a

r

t

i

c

l

e

i

n

f

o

a

b

s

t

r

a

c

t

Article history:

Received19January2018

Receivedinrevisedform16July2018 Accepted9August2018

Availableonline13August2018 Keywords:

Intelligenttransportationsystems Floatingcardata

V2Vcommunication

Heterogeneous vehicularnetworks

Floatingcardata(FCD)referstothemotionandsensordataproducedbymovingvehiclesontheroad. Given that trafficmanagement authorities and drivers can benefit from FCDenormously, there is an urgency to develop efficient FCD collection and dissemination techniques that scale with peak road traffic.In thispaper, wepresent CarAgent,amessageexchange protocolthatperiodically collects and uploadsFCDtodatacentreswiththeminimalutilisationofexistingmobilenetworkresources.Wealso show howCarAgent can be easily extended toefficiently disseminate FCDto all vehiclesin atarget area usingDedicatedShortRangeCommunication(DSRC),arecentlyreleased wirelesscommunication standard for vehicles. We analytically derive the key performance metrics of CarAgent and validate themwithsimulations.Weevaluate CarAgentusingmicroscopicsimulationofroadtrafficinrealstreet mapswhileincorporatingwirelessprotocoldetailsofLongTerm Evolution(LTE)andDSRC.Simulation results confirm that,comparedto the state-of-the-art,CarAgent consumes 50% less LTEresources for FCDcollection.ForFCDdissemination,CarAgentconsumes45%lessDSRCresourceswhileimprovingthe speedofdisseminationsignificantly.

©2018ElsevierInc.Allrightsreserved.

1. Introduction

Modernvehiclescontinuouslygenerateawiderangeofmotion, position,andsensordata, whichare oftenreferred asfloatingcar data (FCD) [1,2]. FCD can be valuable formonitoring road traffic inrealtimeandexploresolutionsforfutureexpansionofroad in-frastructuresandservices[3–5].Itisthereforeimportanttocollect FCDfrom thevehicles on theroad andupload themperiodically todatacentresequippedwithadvanceddataanalyticsandbackup facilities.SomeFCDaggregates, suchastheaveragevehiclespeed ofdifferentroadsegmentsinatargetarea,canaidreal-time nav-igationformotorists.FastandefficientdisseminationofsuchFCD aggregatestoall vehiclesina targetarea, therefore,isalso desir-able.

TherealisationofFCDcollectionanddisseminationwillrequire thedevelopmentofappropriatewirelesscommunicationstandards

*

Correspondingauthorat:SchoolofComputerScienceandEngineering, Univer-sityofNewSouthWales,NSW2052,Australia.

E-mail addresses:hui.huang@unsw.edu.au(H. Huang),

mahbub.hassan@unsw.edu.au(M. Hassan),

glenn.geers@arrb.com.au

(G. Geers), libman@google.com(L. Libman).

for vehicles. In this regard, two noteworthy developments have takenplace inrecentyears.In2010, a newwireless communica-tionstandard,calledDSRC[6],hasbeenreleasedtoenablevehicles tocommunicateandexchangedatadirectlywithother nearby ve-hicles.DesignofDSRC,however,wasmotivatedbytheroadsafety. To help prevent accidents, DSRC dictates that every vehicle peri-odically(1to10timesper second)broadcastsitstravellingstatus in smallbeacon messagescalledCooperativeAwareness Messages (CAMs)[7].DSRCalsohastheprovisionforvehiclestouploaddata to data centresvia special DSRCroad side units(RSUs). Unfortu-nately, dueto thehuge costofRSU deployments, DSRC RSUsare yettobewidelyimplemented.

Recently,Long TermEvolution(LTE),thestandardplatform un-derpinning the 4thand5thgeneration mobile networks, has be-comepopularwiththevehiclemanufacturersfordirectly connect-ing individual vehicle to the Internet [8]. With LTE connectivity, a vehicle nolonger requiresa DSRC RSU toupload data.Instead, they canuploadFCD toaremote datacentreusingthesameLTE mobilenetworkthatweusetoconnectoursmartphones.This de-velopmentismotivatingresearcherstoconsidersimultaneoususe ofbothLTEandDSRCforFCDcollectionanddissemination[2,9,10]. AlthoughLTEandDSRCtogetherprovideapromisingplatformfor collectinganddisseminatingFCD,scalabilityremainsamajorissue.

Fig. 1. Cluster based FCD collection.

OneofthemainchallengesofFCDcollectionanddisseminationis theextremelyhighloadcreatedonthelimitedradio resourcesof LTEandDSRCwhenalargenumberofvehiclesattempt communi-cationduringpeaktraffic.

Thestate-of-the-artsolutionto theLTEoverloadproblemisto usethecooperativeawareness enabledby DSRCtocreateclusters ofvehicles having common features (e.g. proximity, travel direc-tion,speed,connectivity)[11–13],andletasinglevehiclefromthe cluster,i.e., theclusterhead, uploadtheaggregatedstatisticsfrom allclustermembers.Cluster-baseddataaggregationandcollection doimprovethesituationfromindividual collections,butthey are notvery efficientwhen aggregate statisticsfor eachroadsegment aretobecollected,whichistypicallyrequiredbymostdatacentre analytics[14,15].

Themainsourceofinefficiencyofcluster-basedapproacharises fromthefactthatvehiclesonagivenroadsegment,eventheyare located in close proximity, could be divided into many separate clusters, asillustrated by the example in Fig. 1.In thisexample, the8vehiclesaregroupedinto3clusters,whereeachclusterhead willupload an aggregate forthe road segmentto the server, re-sultinginatotalofthreeuploadsforthesameroadsegment.The mainreasonfortheroadsegmentfragmentisbecauseofthelossy natureofDSRCchannel:asvehiclesneedtoexchangecontrol mes-sageswitheachotherstoformclusters,lossofthesemessages re-sultintheclusteroverlappingproblem[16],whichproducesmore number of clusters in small range, hence reducing the FCD col-lectionefficiency.The increasedloadon thenetwork can worsen thesituation,i.e.,thenumberofdifferentclustersformedincreases with increasing number of vehicles (evidence from microscopic roadtrafficsimulationisprovidedinSection5).

ThescalabilityproblemalsoariseswithDSRC utilisationif ve-hiclesattemptto disseminateFCD toall vehicles ina target area through vehicle-to-vehicle (V2V) communications. The state-of-the-art FCD dissemination methods [17–19] require each vehicle to periodicallybroadcast selected data from its localdatabase to other vehicles in the vicinity. Upon hearing the broadcast, other vehiclesupdate their ownlocal databases.However, periodic dis-seminationcan dramaticallyincrease communicationoverheadin densetrafficthreatening thecollapseofDSRC services.Adetailed simulationstudyrevealed[20] that, withperiodic broadcastingof

300-byteFCD messages once every second, DSRC would be fully

saturatedwhentraffic densityreachesto22vehs/km/lane, which isbelowpeak-hourtrafficinmanycities.Althoughconceptuallyit ispossibletoreduce DSRCloadbyincreasingthebroadcast inter-valduringpeakhours,studieshaveshownthatFCDdissemination latencyincreasesexponentiallywithalinearincreaseinbroadcast interval [21]. Since FCD disseminationaims to aidreal-time nav-igation, large broadcast intervals are not desirable, which leaves

the efficient FCD dissemination over DSRC as an open research

problem.

In this paper, we present CarAgent to address the aforemen-tioned problems in FCD collection and dissemination. The pro-posed approach employs a set of FCD collectors called agents,

which are protocol packets capable of moving from one host to

another in the network, to collect and disseminate FCD among

the target region. Our idea is similar to the concept of Mobile Agents (MA),whichhasbeenexploredfordatacollectionin wire-lesssensornetworks[22,23] androutingpathdiscoveryinmobile adhocnetworks(MANETs)[24–26].Webelievethatourapproach canprovideaflexible andeffectivesolutiontoFCDcollectionand dissemination,astheagentscan migratefromonevehicle to an-other at the speed of wireless communications and collect road segmentaggregates effortlessly without having to constantly form clustersandselectclusterheads.Besides,sincedifferentdatatypes andapplicationsrequiredifferentdatafusion algorithm(e.g.how to merge the collected individual data into a single aggregate), the localdataprocessingcould be easily re-programmedby sim-plymodifyingtheexecutioncodecarriedbyagents,enablingeasy re-taskingonthevehicularnetwork.

While collecting FCDs and migrating through the network,

agents can also serve as a natural platform to disseminate the samedatatoothervehiclesinthetargetarea.Thus,theCarAgent protocolhasthepotential toserveasbothFCDcollectorand dis-seminator, which further simplifies deployment efforts and com-plexity.

Ourcontributionscanbesummarisedasfollows:

•

We proposea novelFCDcollectionanddissemination frame-work, called CarAgent, which combines the services of both LTEandDSRC. Tothebest ofourknowledge, thisisthe first attempttoapplyagentconcepttoFCDcollectionand dissemi-nationincomplexurbanroadnetworks.

•

Weanalyticallyderivethekeyperformancedynamicsof CarA-gentandvalidatethemthroughextensivesimulations.

•

WecomparetheperformanceofCarAgentagainst

state-of-the-artusingmicroscopicsimulationsofroadtrafficinrealstreet maps of differentroad patterns while incorporating wireless protocoldetailsofbothLTEandDSRC.Simulationresults con-firm that, comparedto thestate-of-the-art, CarAgentreduces wireless network resourceconsumptionsby 50% and45% for LTEandDSRC,respectively,whileimprovingthespeedofFCD disseminationoverDSRCsignificantly.

The rest of this paper is organised as follows. Section 2 de-scribes related works. Section 3 presents the system model of CarAgentfollowed by its analytical modellingandnumeric study in Section 4. Performance evaluation based on microscopic road traffic simulationis presentedin Section 5.We drawconclusions inSection6.

2. Backgroundandrelatedworks 2.1. Mobileagentinadhocnetworks

Mobile agent based information collection and dissemination hasbeenwell studiedinthecontextofWirelessSensorNetworks (WSN) [22,23]. These approachesdo not requiresensor nodesto directlysendrawdatatothedatasink,ontheotherhand,thesink nodecreatesmobileagents,whichmigrateamongthenetworkto collect reliabledata. Theproblem, therefore,can be reducedtoa variantoftheNP-hardTravellingSalesmanProblem(TSP)suchthat the agentis requiredto findtheshortestpossible routethat vis-its each node exactly once and returns to the data sink in the graph induced by the network topology. However, the data col-lectionschemesproposedforWSNsdonot mapwell tovehicular

networks due to the dynamic network topology caused by high

nodemobility.

In the context of Mobile Ad Hoc Networks (MANETs), many

DuetothedynamicnatureofMANETs,routediscoveryand main-tenance is a quite challenging task. It has been found that Ant ColonyOptimisation(ACO)algorithms [27],whichdeploysmultiple simpleagentscalledartificialantstoestablishoptimumpaths be-tweensourceanddestinationbymeanofstigmergy,cangive bet-terresultsastheyarehavingthecharacterisationofswarm intel-ligence,whichishighlysuitableforsuchtypeofvolatilenetwork.

Concerningtheuseofmobileagentforinformation dissemina-tioninVANETs,onlyafewworkswereestablishedinrecentyears forthepurposeofservicediscovery anddatacollections. In [28], theauthorsproposedtheADOPELprotocolusingthemobileagent tocollectdatafordelaynon-sensitive applicationsbasedon Rein-forcementLearning (RL), themobile agent can adaptivelychoose thenext relay nodesalong the roadsegment that offersa better chance to collect more data without losing the way back to the datasink.However,thisworkonlyconsidersthecaseofthesingle agent in a simplified highway scenario, and the approach might

be inadequate to address the same problem in the urban area

dueto the complicatedroadnetwork topology. In [29], a mobile agent-basedschemeforservicediscovery inMANETsisproposed, wheremobileagentsaretravellingthroughthenetwork,collecting aswellasdeliveringthedynamicallychangingserviceinformation. Theirschemeisfurthermodifiedandextendedin [30] tobe suit-able forthe context ofVANETs, such that mobile agents migrate basedon themoving speed, directionandlocation ofvehicles to disseminateinformationtoothersofthebroaderareainashorter time. However, their works only focus on the one-to-many dis-seminationscenario,wheremobileagentsarecreatedbyanentity whowantsto broadcastapiece ofinformationto vehicleswithin agivenregion.

2.2. Trafficinformationsysteminvehicularnetworks

Several works have been recently conducted to investigate

the non-safety related FCD collection and dissemination issues by adopting different techniques and approaches. In the caseof FCD collection, the recent trend has focused on FCD collection

in heterogeneous networks, within which vehicles possess two

interfaces: 1) 802.11p/DSRC and 2) LTE at the same time [31]. A commonparadigmistousethecooperativeawareness enabled by DSRC to create clusters of vehicles having common features (e.g. proximity,traveldirection,speed,connectivity).Afterthe clus-terformation,eachClusterHead(CH)collectsdatafromtheir clus-termembers,andthentheaggregatedinformationisdeliveredto the FCD server on the cloud. For example, in [12], the authors proposed the LTE4V2X to collect the data from vehicles on the road. Their work utilises eNodeBs to organise vehicles into sev-eralclusters. The selectedCHsthen can collectdata fromcluster

membersusing802.11p/DSRCcommunicationandthenuploadthe

aggregated information to the server via eNodeBs. In their later extension paper [13], multi-hop communication technique is ap-pliedto collect datainside areaswhere thereis noLTE coverage (e.g. tunnel).

Ingeneral,thedatacollectionprotocolproposedinthe

above-mentioned works requires to form and maintain some certain

structuresamongvehicles.Inahighly dynamicvehicularnetwork, vehicles join and leave clusters along their travel route, causing changesintheclusterstructure.Frequentchangesincluster mem-bership,splittingandmergingofclustersincreasethecontrol over-headanddraintheradioresources[16].Inordertoovercomethis problem,morerecently,theauthorin[32] proposedanFCD collec-tionapproachbasedontheOn-the-FlyClustering(OFC)technique. In their work, vehicles are assumed to know the time instance when the collection starts. Upon collection interval starting, ev-eryvehiclecomputesitsownsendingtimer basedonthecurrent LTEchannel qualityandthenumberofonehopDSRCneighbours.

Fig. 2. Overall operation of the CarAgent system.

Vehicleswhosetimerexpirebecomeclusterheadsanduploadthe aggregateddataoftheir one-hopneighbourstotheserver. Imme-diately after the uploading, a cluster head broadcasts an Inhibit message over the DSRC network tosuppress other vehicles from uploadingduplicatedinformation.However,sincetheyassume ve-hicles need to know the starting time instance for each collec-tioncycle,theirapproachcannotcopewithon-demandcollections. In addition, the OFC algorithm does not take the road network topology intoconsideration,a roadsegmentmightbe coveredby several different clusters, results in several fragments thus less-optimised data aggregationefficiencyforcollecting road segment aggregates.

Asforthetrafficinformationdissemination,currentapproaches reportedintheliterature usuallyfollowthepush-basedmodel,or periodic disseminationmodel[33,34],whereeach vehicle period-ically selecta set of sensorreadings orimportant messages,and then broadcastthemtoitsvicinity. Othervehiclesupon reception then rebroadcast thereceived information based on certain poli-ciesandcriterion.

Since periodic dissemination can dramatically increase com-munication overheadwhenthetraffic densitybecomes high[14], networkperformancewouldbeseverelyaffected,leadingto band-width waste. In order to overcome this deficiency in the

push-based model, some studies suggested to adaptively adjust the

frequency of periodic broadcasting based on message utility and channel quality. Forexample,the authorin [18,19] proposed the AdaptiveTrafficBeacon(ATB)protocoltoexchangetraffic informa-tion amongvehicles on the road.The broadcast interval is adap-tivelyadjustedbasedonthechannelqualityindicatorcalculatedby consideringthenumberofpacketcollisions,SignaltoNoise(SNR) levelandthenumberofneighbours.ThegoalofATBistosendas many informationaspossible,butavoidoverloading thewireless channelatanytime.Theirsimulationresultsindeedshowthatthe increasing rate ofcommunication overheadasa function of traf-ficdensitycanbeeffectivelyreduced,however,thetrade-offbeing madeislongerdisseminationlatencyduetotheincreased broad-castinterval[21].

3. Systemmodel

TheoveralloperationoftheCarAgentisillustratedinFig.2.To collect FCD froma given target area, a cloud server periodically injectsabatchofmobileagentsintotheVANETcoveringthearea usingLTE.TheagentscollectFCDaggregatesfromtheVANETusing DSRC andreportthemback tothe serverviaLTE.Afterreporting the aggregatesto theserver, they can optionallyroaminside the VANET forsome timeto disseminatethecollected informationto the vehicles using DSRC. Agents eventually self-terminate them-selvesafterapredeterminedlifetime.Inthefollowing,wedescribe thesystemmodelsforroadsandvehicles,andtheagentsforFCD collectionanddissemination.

3.1. Roadsandvehicles

tothenextintersection.Ifthedistancebetweentwointersections islargerthantheeffectiveDSRCcommunicationrange(e.g.urban highway), the road span should be further divided into smaller onesso that vehicles located within the same road segment are

able to communicate with each other directly. We also assume

thateachvehicleisequippedwithaGPSreceiveranddigitalmaps forobtainingtheroadsegmentationscheme,locationandtime in-formation.The GPSpositioning accuracyisassumedto beprecise enoughforthevehiclesto locatethemselvesinspecificroad seg-ments. Incontrast, the server cannot locate vehicles within road segments,butitcandeterminewhetheravehicleisinsideatarget areaornot, thanks to the LTE tracking capability using base sta-tions.

Vehiclesequippedwithboth DSRCandLTE,move throughthe roadnetwork ina targetgeographicalarea.VehiclesuseDSRCfor

V2Vcommunications andLTE forcommunicating with the cloud

server. The proposed CarAgent, instead of harvesting individual FCD,collects FCDaggregatesdescribing roadsegmentperformance. Followingthedefinition in[14], an FCDaggregate isa tuplethat containsinformationaboutaspecificroadsegment,asthe follow-ing:

A

:= (

S

,

T

,

v

,

Q

)

(1)

where

S

istheroadsegment’s ID,

T

is thetime stampand v is theprimary value ofthe aggregate,which conveysvaluesofFCD producedbyvehiclesinthisroadsegment,suchasspeed,heading, roadconditionsandroad occupancy. The item

Q

isthe auxiliary valueused by manyaggregationschemesto denotethe certainty orqualityofinformationafterithasbeenaggregated,i.e.standard deviationofanaverageorcountofFCDssummarisedinthe aggre-gate.

It is assumed that each vehicle locally stores the FCD from CAM broadcasts it receives from other vehicles. Therefore, a ve-hiclecan computean FCDaggregate oftheroadsegment itstays whenneeded, giventhattheroadsegmentationschemeisknown inadvance.HowtomergeindividualFCDintoanaggregateisout of our scope, in fact, it most depends on the type of data and theparticular applications.Forexample,geographical coordinates mightbefusedbycalculatingtheirboundingboxandtheaverage

and minimum speed could be used for traffic congestion

moni-toring.Thisdesigndecisioncan reduce theamountofdatabeing transmittedto the remote server. More importantly, itcould im-provedataprivacyasindividual datausually “disappear”intothe aggregates[14].

3.2.CarAgent:FCDcollection

In thissection, we explain how CarAgentcollects FCD aggre-gatesfromthe network.In particular, we describe theagent for-mat, agent injection into VANET, and hopping of agents within VANET.

3.2.1. Agentformat

In this context, the agent is a data packet whose format is shownin Fig. 3. As mentioned before, the cloud server periodi-callyinjectabatchofagentsintothetargetregion,agentsinjected

from the same batch has the same batchID, and the agent ID

fielduniquelyidentifieseachagentwithinthebatch.Sincehowto mergeindividualFCDsintoanFCDaggregatedependsonthedata ofinterestaswell asthe particularapplication(e.g.lossless/lossy fusion, average/maximum calculation), each agent also carries a pieceofexecutioncodetodeterminesthelocaldataprocessingin eachhop.Clearly,theuseofagent provideshighflexibilityofthe FCDcollectionprocess. Bychangingtheexecutioncodeofagents,

Fig. 3. Agent format.

different data processing and aggregation can be carried out at eachhop.

Beside,theagentalsomaintainsabuffer thatcanstoreupto K collectedFCDaggregates. Thevalueof K islimitedtoensurethat themaximumlengthofan agentcanbetransportedwithina sin-gle DSRCMACframetoavoidpacketfragmentation, whichwould increasesystemcomplexityandreducethereliabilityofagent hop-ping.

3.2.2. Agentinjection

Becausea single agentcan onlycollectFCDaggregatesfroma smallnumberofroadsegmentsduetothelimitedbuffer,tocover alargetarget area,aserveristhereforerequiredtoinjectabatch ofN agentsrandomlytoN distinctvehicleswithinthetargetarea eachtimeitwantstolearnthecurrenttrafficstatusofthearea.

To achieve this, we assume a special-purpose backendserver as foreseenin the ETSI specification[35] for supporting geocast-ing services inLTE-basedvehicular networks. Thebackendserver maintains a list of geographical areas. Vehicles crossing over a new area register themselves, allowing the server to know their presentinanyarea atalltimesandtheirIP addresses.Notethat, theassumptionoftheexistenceofsuchabackendserverdoesnot underminetheusefulnessoftheproposedCarAgentsystem,asthe serverdoesnotstorepreciouslocationinformationofeachvehicle, butonlybeabletodeterminewhetheravehicleisinsideatarget areaornot.

3.2.3. Agenthoppingalgorithm

Oncean agent enters (injected) intothe VANET,it keeps hop-ping from one vehicle to another until K hops, collecting and bufferingFCDaggregatesfordistinctroadsegmentsalongitspath. Eachagent therefore willstore atmost K FCDaggregates, where theparameter K isdesignedtoensurethattheagentcanbe con-tainedwithinasingleDSRCMACframe.

Recall that each vehicle locally stores the individual FCDs by processing theperiodic CAMs broadcast by DSRC. Once an agent hopsto a vehiclelocated at road segment i, the agent then can getaccesstothelocalstorageofthehostvehicle,run the execu-tion codeto createan FCDaggregate of that roadsegment. Note that duetochannel fadingandpacket collisions,the hostvehicle mightnotbeabletoreceivetheCAMsfromallvehicleswithinthe sameroad segment.In thissense, theCarAgentcan onlyachieve the “besteffort”FCD aggregations.As suggestedby [14,15], most trafficefficiencyapplicationscantolerateacertainlossof informa-tion.

Allthatisrequiredisfortheagenttosuccessfullyhopfromone roadsegmenttoanothersegment.Therefore,weproposea4-way handshakealgorithmtohelptheagentsuccessfullyhoptothenext roadsegmentrandomly.Thealgorithmisdescribednext:

•

The vehicle currentlyhosting the agent broadcaststhe agent andsetsatimer,twait,waitingforrepliesfrompotential

vehi-clesfromotherroadsegmentstohosttheagent.

Fig. 4. Time domain illustration of next forwarder selections.

•

Oncethe timer treply expires, thevehicle temporarily storing

theagentreplieswithabroadcastHop-REQ message.Another vehicle, located on the same road segment, cancels its own treplyifitoverhearsaHop-REQ beforeitstimer(treply)expires.

In other words, only one vehicle fromeach roadsegment is expectedtoreplywithHop-REQ.

•

The currenthostvehicle temporarily storesall received Hop-REQsbefore its timer twait expires.Upon expiryoftwait,the

hostvehicle restarts the broadcast process ifit doesnot re-ceiveanyHop-REQ.Otherwise,itchoosesthenextvehiclefrom a road segment not visited before if there is one, otherwise

randomly chooses one among the received Hop-REQ as the

nextholderandunicastsaHop-CFM toit.

•

If the original agent holder does not receive the MAC layer ACKfromthenextholder,itrestartsthemigrationprocessto preventagentsfromunexpecteddrops.

Fig.4showsthetimedomainillustrationofthenextforwarder selectionsof theproposed agent migrationalgorithm. Consider a

scenario in which two vehicles, A and B, located at the same

roadsegmentreceiveanagentbroadcastfromaneighbouringroad segment.VehicleA doesnotoverhearanyHop-REQ fromother ve-hicleslocatedonthesameroadsegment,itthensendsaHop-REQ after TimerA expires. Vehicle B, during its timer countingdown, overhearstheHop-REQ from A.It thenstopsthe contention pro-cess.

Notethat the proposed 4-way handshake algorithmuses uni-castofHop-CFM with MAC layer acknowledgement,which might leadtotheheadoflineblockingeffectinhighlydynamic vehicu-larenvironment[37].However, simplyusingabroadcast message toconfirmthenextagentholdercouldresultinunexpectedagent dropsiftheHop-CFM islost.Thetrade-off beingmadehereisthe improvementofthereliabilityofagentmigrationwiththepriceof thepossibleadditionaldelaysonmessages waitingon thequeue. Consideredthattheproposed CarAgentonlyintroducesmall traf-ficovertheDSRCchannel (evidencefrommicroscopicroadtraffic simulationis provided inSection 5), the overheadcausedby the MAC layer ARQ process is very small. Moreover, 802.11p allows theuse ofdifferentaccesscategories(AC), the impactofblocked queueon safetyapplicationcould be relieved by assigning lower prioritytotheHop-REQ messages.

It isclearthat the timer twait dictatesthe hoppinglatency of

each agent. Therefore, a smaller twait would lead to faster agent

hopping, reducinglatencyforFCDcollection.However, asstudied in [38], too small timer leads to broadcast overload problem in contention-based networks. The optimal settingof twait depends

onthecongestioncontrolmechanismaswellasthecurrent chan-nel loads. Following the recommendations in [36,39], we choose twait

=

25 ms.

3.2.4. FCDuploading

After K hops, agents stop collecting newFCD andupload the collectedaggregatestotheserver.Anaiveapproachfortheagents wouldbetosimplyuploadeverybufferedaggregatetotheserver. However, this would resultin redundantuploading as the same roadsegmentcouldbesampledbymorethanoneagents.Inorder to prevent duplicate uploads, CarAgentimplementsthe following simplescheme.Eachvehiclemaintainsavariable,whichisinitially setto

−

1,indicatingthattheroadsegmentthatthevehicleis cur-rently staying at has not beensampled by any agent during the currentcollectioncycle.Thevariableisupdatedunderthe follow-ingrules:

•

Ifthe vehicle overhearsan agent broadcast fromanother ve-hiclelocatedatthesameroadsegment,itsetsthevariableto theagent’sbatchID,indicatingthattheroadsegmentnowhas beensampled.

•

Every timea vehicle movesto a newroadsegment,it resets thevariableto

−

1.

Anagentsamplesthecurrentroadsegmentonlyifthevariable ofthecurrentholderdoesnotequaltoit’sbatchID.AfterK hops, the agent simplyuploads every bufferedaggregatestothe server by using LTE uplink. The approach is feasible as agent hopping takesonly afewmilliseconds, andeach agent onlyhops K times (K is usually a small value), during which vehicles move only a very small distance.As we will show in Section 5, theproposed schemesignificantlyreducesredundantuploadsforCarAgent. 3.3. CarAgent:FCDdissemination

Afterthedatacollectionandupload(afterK hops),thebatchof agentscanbetreatedasadistributedfloatingdatabasecontaining traffic informationofroadsegments ofthetarget area.Therefore, by simplyprolongingthelifetimeofagentsbyanadditionaltime interval, Tds,itispossibletodisseminatethecollectedFCD

aggre-gatestoother vehicles inthearea usingthefree DSRCspectrum. Thiscanbeachievedbylettingtheagentscontinuetohoparound theVANETandallowingothervehiclestoaccumulatetheFCD ag-gregates as they opportunistically overhear the agent broadcasts. Overtime,eachvehiclethenwilllearnthetrafficconditionofthe targetarea.Longertheagentshoparound,themoreopportunities thevehiclesgettobuildtheknowledge.InSection4,wewill ana-lyticallymodelthespeedofdisseminationintermsoflearningthe fractionofthetargetareaasafunctionoftime.

Duringdissemination,agentsdonotcollectanynewFCD aggre-gates,buttheyaimtodeliverthecollectedinformationtoasmany vehicles in the target area as possible.To roamthe entiretarget area as rapidlyaspossible, theagents, therefore,need tohop as faraspossible.InspiredbytheETSI GeoNetworkingstandard[40], the newhoppingobjective,i.e.,extending thehopdistanceasfar aspossibleateachhopingattempt, canbeachievedbymodifying thetimersettingcriterionoftreply asfollows:

treply

=



twait

+

tmin−_RctwaitD D

≤

Rc

tmin D

>

Rc

(2)

where Rc is the effectiveDSRC communication rangeand D

de-notes thedistance betweentherecipient vehicle andthecurrent agent holder.Thetmin issetto1 msfollowingthedefaultsetting

oftheETSIGeoNetworkingstandard. 4. Performancemodelling

Fig. 5. (a) An urban road network, and (b) the corresponding undirected graph.

•

Expected Collection Coverage

(

N

,

K

)

: This metric refers to theexpectedfractionofallroadsegmentsforwhichtheserver isabletocollecttheFCDafterinjectingandreceivingabatch of N agents, whereeach agent completes K hopsbefore up-loading thecollecteddata.The higherthefraction, thebetter thecoverage,andviceversa.

•

Dissemination Speed

(

t

)

: For a given vehicle in the target area, we define vehicleawareness as thefractionof road seg-ments it learns about by overhearing agent broadcasts over DSRC. The disseminationspeed is then expressed asthe ex-pectedvehicleawarenessasafunctionoftimespentbyagents roamingintheVANETaftertheirinitialK hopsofFCD collec-tion.

In this section, we derive these two metrics analytically and studytheirpropertieswithnumericalexperiments.

4.1.Derivationof

(

N

,

K

)

A road network can be represented by an undirected graph,

G

(

V

,

E

)

,whereV isthe setcontainingevery roadsegments, and anedgeei j

∈

E ifnodesi and j areconnectedbyan intersection.

Fig.5 illustrates the graphrepresentation ofa typical urban grid roadnetwork.

As agentscan onlyhop fromone road segment to any of its neighbourroadsegments,themostbasichoppingbehaviourofan

agent can be modelled asa randomwalk on graph G.With

ran-domwalk,anagentstayingatroadsegmenti willhoptosegment j withprobability pi,j

=

d1(i) ifei j

∈

E,where di isthe degreeof nodei.Obviously,therandomwalk modelwillaccuratelycapture the hopping of agents if, at the time of agent broadcast, every neighbour road segment has at least one vehicle within the ra-diocoverageofthebroadcast.Otherwise,thecondition pi,j

=

_d1_(i)

doesnot hold, causing therandom walk model overestimate the performanceofCarAgent.

Definition1.If at the time of agent broadcast, every neighbour roadsegment has atleast one vehicle within the radio coverage ofthebroadcast,thevehiculartrafficintheroadnetworkiscalled saturatedtraffic.

Clearly, the random walk modelwill provide an upperbound forCarAgentperformance, which canbe achieved withsaturated traffic. InSection 5,usingsimulations, we willidentify the mini-mum vehicledensityrequiredtosatisfy thesaturatedtraffic con-ditionforanyroadnetwork. Interestingly,simulations revealthat even a light traffic withapproximately 20vehicles per kilometre onaveragecanachievethesaturatedtrafficstatusforthepurposes ofrandomwalk.Ourdefinitionofsaturatedtrafficthereforebyno meansindicateheavy orpeaktraffic,whichisexpectedonly dur-ingcertainhoursoftheday.

Assumingtherandomwalkisergodic andirreducible,there ex-istsasteadystateprobabilitythatthewalkerwillvisitnodei with probability

πi

=

di

2|E| [41].Thisalsomeansthatifanagentwas

in-jected or initially placed intothe area following the steadystate distribution,thentheprobabilitythat itwouldvisit roadsegment i inthenexthop wouldalsobe

πi

.Basedonthis,wehave: Lemma1.Undertheassumptionofsaturatedtrafficandsteadystate agent injections, for any arbitrary road network,

(

N

,

K

)

=



i∈V1−(1−2|E|di )N K

|V| .

Proof. For steady state agent injection, the probability that an

agent will not visit road segment i in successive K hops is

(

1

−

π

i

)

K.Let

θ (

N

,

K

)

bethesetcontainingroadsegmentsthatare

visitedby atleastone agent.The expectedcardinalityof

θ (

N

,

K

)

thuscanbeobtainedas:

|θ(

N

,

K

)

| =



i∈V 1

− (

1

−

π

i

)

N×K

.

(3) Since

(

N

,

K

)

=

|θ(_|N_V,_|K)|,wehave:

(

N

,

K

)

=



i∈V1

− (

1

−

2d|Ei|

)

N K

|

V

|

2

(4)

OneproblemwithLemma1isthatitrequiresthetargetareato be firstconverted intoafinite undirectedgraph,andthensolved forthesteadystateprobabilitiesforrandomwalk.Wecould elim-inate this requirement if the graph was regular, i.e., each node havingtheidentical degree. Interestingly,studieshaveshownthat mostroadnetworksare highlyregular [42]. Forregular road net-works,Lemma1thuscanbefurthersimplifiedas:

Lemma2.Undertheassumptionofsaturatedtrafficandsteadystate agentinjections,forregularroadnetworks,

(

N

,

K

)

=

1

−



1

−

_|V1_|



N K . Proof. According to the Handshake Lemma in Graph Theory,



i∈Vdi

=

2

|

E

|

. Thus, for a regular graph, we have ₂d_|i_E|

=

_|1_V|.

By replacing di 2|E| by |V1| in equation (4), we have

(

N

,

K

)

=

1

−



1

−

_|V1_|



N K .

2

FromLemma1and2,weobtainthefollowingresults: 1. The expected coverage reaches its limiting value of 100% as

thenumberofagentsincreases.Arbitrarilyhighcoveragecan beachievedbysimplyinjectingalargenumberofagents. 2. Theexpectedcoveragehasadiminishingreturnwith

Fig. 6. Numericalresultsforcoverageasafunctionofagentpopulationunder satu-ratedtraffic.

For K

=

20, Fig. 6showsnumerical results forthree different sizes of grid road networks containing a total of 180, 264, and 364 road segments, respectively. As we can see fromthe figure, irrespective ofthesizeoftheroadnetwork,the coverage asymp-toticallyreaches100%ofthetargetareaasweincreasethenumber ofagents.Thediminishingreturnis alsoclear.Duetothe dimin-ishingreturn,itmaybedesirabletoachieveatargetcoverage,such as95% ofthetotal roadsegments, instead ofinjecting amassive numberofagentsforasmallimprovementincoverage.

Itisalsointuitivetoseethatforagiventargetcoverage,larger roadnetworksrequiremoreagents.FromFig.6weobservethatfor agiventargetcoverage,theratiobetweenagentpopulationtothe totalnumberof roadsegments inthe roadnetworkmaintains at thesamelevel.This observationinspiresustofurther investigate therelationshipbetweenagentpopulationandthesizeoftheroad network.

Definition2.Agentdensity,

λ

=

_|N_V|,isdefinedastheratiooftotal numberofagentsinjectedtothetotalnumberofroadsegmentsin thetargetroadnetwork.

Theorem1.Undertheassumptionofsaturatedtrafficandsteadystate agentinjections,forlargeregularroadnetworks,therequiredagent den-sity

λ

foratargetcoverage,Ctarget,isindependentofroadnetworksize, whichcanbeapproximatedbytheconstant

−

ln

(

1

−

Ctarget

)/

K .

Proof. FromLemma2,wehaveCtarget

=

1

−



1

−

_|V1_|



N K.Taking logarithmonbothsides,weobtain:

ln

(

1

−

Ctarget

)

=

N K ln

(

1

−

1

|

V

|

)

(5)

Since,ln

(

1

−

x

)

≈ −

x if x

≈

0,forlarge

|

V

|

,wehave: N

|

V

|

≈ −

ln

(

1

−

Ctarget

)/

K

2

(6) Theorem 1formally showsthat

λ

onlydepends onthe target coverage Ctarget and is inversely proportional to K . Fig. 7 shows

therequiredagentdensitytoachieve differenttargetcoveragesas afunctionofthetotalnumberofroadsegmentsofthetargetarea forK

=

20.Wecanseethattheagentdensityisrelativelyconstant fora targetcoveragevalue.Forexample,toachieve99%coverage, the systemneeds to inject only 0.2agents per road segment, or onlyoneagentper5roadsegments,irrespectiveofthesizeofthe targetarea.

Fig. 7. Numericresultsfortherequiredagentdensityasafunctionoftargetarea sizeundersaturatedtraffic.

Fig. 8. Area covered by two consecutive agent broadcasts.

4.2. Derivationof

(

t

)

Supposethat timeisslottedandeachagenthopsateachtime slot



t.Letusassumethatthegeographicalareaofthetarget ur-banregionisD andthetotalnumberofvehiclesintheareaisM. EachvehiclehasaDSRCtransmissionrangeofr.

Lemma3.TheprobabilityP r

(

t

)

thatavehicleoverhearsacirculating agentaftertimet is1

−(

1

−δ)

×(

1

−

0

.

41

δ)

t,where

δ

=

π_Dr2represents thefractionofthetargetareacoveredbyDSRCradiorange.

Proof. ConsiderthescenarioinFig.8,whereanagentisforwarded fromvehicle A to B using aDSRC broadcast.Sincethe agent mi-gration and rebroadcast by B take only a few milliseconds, it is reasonable toassumethatthelocationsofvehiclesdonotchange significantly.Theunshadedcircleistheareacoveredbythe

broad-cast from A, while the shaded region represents the new area

coveredbytherebroadcastfromB.Usingbasicgeometry,thearea oftheshadedregioncanbeobtainedas:

A

(

d

)

=

π

r2

−

4 r



d/2



r2

₋

_x2_{dx (7)}

whered denotesthedistancebetween A andB.

Since d canvary fromavery smallvalue closeto zeroto the

maximumvalue equals totheDSRC communicationsranger, the

averagenewarea,

α

,coveredby each hopexcluding thearea cov-eredintheprevioushopcanbeobtainedas1_:

α

=

r



0 2

π

x

×

A

(

x

)

π

r2 dx

≈

0

.

41

π

r 2 ₍₈₎

Theexpectednumberofvehicles inthe shaded areathen can bederived as α_DM. Ifwe let C

(

t

)

denotethe expectednumberof vehiclesoverhearingagivenagentaftertimet,thenwecanobtain thefractionofallvehiclesin D thatdidnotoverheartheagentas 1

−

C_M(t).Therefore,duringeachtimeslot



t,theexpectednumber ofnew vehiclescoveredbyanagentissimply α_DM

(

1

−

C_M(t)

)

,which isbasically therate atwhich an agent cancovernewvehicles in thetargetarea.Therefore,wehavethefollowingrelationships:



C

(

t

)

−

C

(

t

−

1

)

=

α_DM

(

1

−

C(t_M−1)

)

C

(

0

)

=

π

r2 M D

(9)

Here,C

(

0

)

refers totheexpectednumberofvehicles overhear-ing a givenagent atthe beginning ofthe dissemination process. Equation(9) isthereforealinearnon-homogeneousrecurrence with initialvalueofC

(

0

)

.Bysolvingtherecurrence,weobtain: C

(

t

)

=

M

− (

M

−

π

r2M

D

)

× (

1

−

α

D

)

t ₍₁₀₎

Nowtheprobability P r

(

t

)

thatavehiclewilloverheartheagent aftertimet canbecalculatedas:

P r

(

t

)

=

C

(

t

)

M

=

1

− (

1

− δ) × (

1

−

0

.

41

δ)

t

₂

₍₁₁₎

Lemma3 showsthat under the assumptionof saturated traf-fic, P r

(

t

)

is independentofthevehicle densityandonly depends on

δ

,i.e.,thefractionofthegeographicalarea D coveredbyDSRC communicationranger.

Nowassuming thatthereare N agents migratingamong vehi-clesindependently,the expectednumberofagentsthata vehicle overhearsaftertimet issimplyobtainedasN

×

P r

(

t

)

.Recallthat Lemma1 gives the expected fraction of road segments that can be learned afterreceiving N agents. Therefore the dissemination speed

(

t

)

canbecalculatedbysimplyreplacingN inequation (4) withN

×

P r

(

t

)

.

Fact 1.Under the assumptionof saturated traffic, forarbitrary road network_ and N agents,thedisseminationspeedsatisfies

(

t

)

=

1

−

i∈V(1−πi)K N P r(t)

|V| .

Fact2.Undertheassumptionofsaturatedtraffic,forregularroad net-worksandN agents,

(

t

)

=

1

−



1

−

_|1_V|



K N P r(t)

.

Fig.9showsnumericalresultsof

(

t

)

forN

=

20

/

40

/

60 forthe gridroadnetworkwith180 roadsegments,wheretheareaofthe region is 5.0625 km2 and the DSRC communication range is set to250 m. It isclearthat the expectedfractionofroad segments a vehicle learns asymptotically reaches a certain value. In fact, thislimiting value is defined by

(

N

,

K

)

asthe maximum vehi-cleawarenesscannotexceedthecollectioncoverage.Ontheother hand, each curve also shows a clear diminishing return, which indicates that vehicles can build their knowledge very quicklyin thebeginning,buttherateoflearningdecreasesdramaticallyafter someperiodoftime.Forexample,inthecaseof60 agents,a ve-hicleisexpectedtolearnaround90% oftheroadsegmentsbythe 50thtimestep,butitthentakesanother50 timestepstolearnthe restofroadsegments. Inanotherword,aftera certainthreshold, agentsshould terminate themselves as the benefit of continuing disseminationbecomesnegligible.

Fig. 9. Numericresultsfordisseminationspeedinthegridcontaining180 road seg-mentsundersaturatedtraffic.

Fig. 10. Multi-layer simulation platform. 5. Simulation

In the previous section, we derived analytical models of the proposedCarAgentsystembysimplifyingthephysicalenvironment wherever necessary for mathematical tractability. Using state-of-the-art simulation tools, in this section, we study the effect of many real-world parameters that were ignored by the analytical models. These simulation experimentsthus give usthe opportu-nitytonot onlyvalidate theefficacyoftheanalytical modelsbut alsogaininsightsintoarangeofotherperformanceissuesnot cap-turedbythemodels.Thesimulationtoolsandexperimentalsetup aredescribedfirst,followedbythesimulationresults.

5.1. Simulationsetup

We implemented the proposed CarAgent system in a

multi-layersimulationplatformasshowninFig.10.Thesimulation plat-form includes three interconnected tools, OpenStreetMap,SUMO,

and OMNet++. OpenStreetMap is used for obtaining real-world

maps including detailed information of the road network, traffic light and buildings. This information is then imported to SUMO [43] to generate realistic vehicle traces using microscopic vehic-ularmobilitymodels. OMNet++,whichincludes simuLte[44] and Veins modules[45],isbidirectionallycoupledwithSUMO provid-ingwireless networksimulation.Veins providesacompletesetof modelsforassessingDSRCvehicularnetworks,whilesimuLteis re-sponsibleforLTEcommunicationnetworks.

Fig. 11. The urban maps and their degree distributions.

Table 1

Roadnetworkstatistics.

Parameter Glasgow Sydney Paris Area (km2_{) 5.15 9.34 10.08} Total number of road segments 204 328 410 Total length of roads (km) 27.27 50.84 66.74 Mean of degree 4.863 5.4850 4.4450 Standard deviation of degree 0.9745 0.8210 1.1419

Glasgow, Sydney, and Paris, which fulfil our objective of creat-ingvariationsintopology,regularity,andsize.Transportliterature [42] categorises allroad network intotwo basicgroups, grid and radial. Wetherefore,considerone grid network(Sydney),one ra-dial (Paris), and one hybrid (Glasgow). The simulated maps, as extracted from OpenStreetMap, are shown in Fig. 11 along with theirrespectivedegreedistributions.WeobservethatGlasgowand Sydney have a high level of regularity (lower spread of degree), whileParisislessregular(widerspreadofdegree). Table1 sum-marisesthemainstatisticsofthesethreeroadnetworksandshows thatParisnetworkhastwicethesizeofGlasgownetworkinterms ofarea,numberofroadsegments,andthetotallengthofroads.

Formathematicaltractability,wecompletelyignoredthe mobil-itydetails ofthecarsinouranalyticalmodels.Inthissection,we simulate the following details to capturethe realistic movement of cars on our roads. All cars enter the road network from ran-dom locations withan initial velocity randomly chosen between 40–60 km/h.Eachvehiclechooses randomlyapairofstarting

lo-Table 2

Simulationparameters.

Type Parameter Value

Scenario Maps Sydney/Paris/Glasgow Vehicle density 4 to 50 vehs/km Maximum speed 60 km/hour 802.11p Bit rate 6 Mbps

Bandwidth 10 MHz

Frequency band 5.9 GHz MAC MTU 2312 bytes MAC+PHY header 450 bits

Tx Power 20 mW

Propagation model Nakagami-m LTE eNodeB Tx Power 46 dBm

UE Tx Power 20 dBm Propagation model JakeFading Number of RBs (UL/Dl) 50/50 Network+transport header 48 bytes Number of eNodeBs 5/9/10 CarAgent Aggregate size 100 bytes

K 20

twait 25 ms

cationanddestination,wheretherouteiscomputedinadvanceby using thestandard Dijkstra algorithm. Notethat even though the trafficdemands aremodelledby usingrandomtrips,theresulting vehicle densityisnon-uniformat differentpartsofthe road net-work.Thisisbecauseduringthesimulation,thevehicle’smobility isdefinedbythedefaultcarFollowing-Krauss model,including stop-ping at traffic lights, which ultimatelybreaks the uniformity.We define trafficdensity asthe numberofvehiclesperkm and control theaverage densityforasimulatedroadnetwork ofa giventotal road length by adjusting the totalvehicles injected into the net-work.

The restof the simulation parameters are summarised in Ta-ble 2.Anoteworthy detailnot considered inour analyticalstudy is theradio propagationmodel.Inour simulation,all DSRC com-munications employthe Nakagami-m fadingwithm

=

3,plus the SimpleObstacleShadowing modelinVeinstomimicthesignal shad-owingeffectduetobuildingsintheurban region,wherebuilding detailsareprovidedbytheOpenStreetMap.ToenableLTE commu-nications, we placean LTEeNodeB (base station)foreach square kmineachmap.

Agentsperformlocaldataaggregationaftereachhopusingthe following generic aggregation scheme: individual FCDs from the

same road segment are merged into a single FCD aggregate. We

assume thesize ofexecution codeand the size ofeach FCD

ag-gregate is 100 bytes, which means that a maximum of 20 such

aggregatescan be carriedby asingle DSRC MACframe giventhe MaximumTransferUnit(MTU)forDSRCis2312bytes.Asaresult, we assume K

=

20. Eachroundofsimulationstarts witha200 s of warmup time, andthen a number of agentsare injected into the road network. These agents first collect andupload FCD ag-gregates,andthenroaminside theVANETforanother Tds

=

10 s

fordissemination purpose.Everysimulationis repeated10 times

withindependent randomnumberseeds andthe 95% confidence

intervalhasbeenreportedforeachcollectedperformancemetric. Inadditiontothetwoprimary metrics,CollectionCoverage and DisseminationSpeed, definedinSection 4,we also collectthe fol-lowingstatisticsfromoursimulations:

•

Duplicateratio: It is the fraction ofuploaded aggregates that areduplicates.

Fig. 12. Collection coverage as a function of agent population N.

•

LTEcost:Itismeasuredbyboththenumberofbytesuploaded via theLTE uplink andthe number ofResource Blocks (RBs) consumedatthebasestationsfortransmittingthesedata.We assumeeachuploadedLTEmessageconsistsof48bytesof net-work andtransport headers and a payload consisting of the collectedaggregatesbyanagent.

•

DSRCcost: Itis measuredby thetotal numberofbytesbeing broadcast overtheDSRC channelduring thegivensimulation time, excluding the number ofbytes beingbroadcast due to

CAMsexchange.

5.2.Collectioncoverage

Forallthreecities,Fig.12showsthecollectioncoverageresults obtainedasa function ofthe number ofagents, wherethe ana-lyticresultsareobtainedusingLemma2undertheassumptionof regular roadnetworks andsaturatedtraffic.Forthe simulation re-sults,we considerbothdense trafficwithenoughvehicles onthe road(50 vehs/km) aswell as unusuallylight traffic (4 vehs/km). Wemakethefollowingobservations.

The analytical model of Lemma 2 serves as an upper-bound,

which is achieved when roads are very regular and there are

enoughvehiclesontheroadfortheagentstohopinalldirections. Forexample,forallthreecities,wecanseeaverygoodmatch be-tweenanalyticalandsimulationresultsfor50vehs/km.Thematch forParisisonlyslightlyworse compared toGlasgowandSydney duetohaving a slightlylessregular roadnetwork. Notethat the goodalignmentbetweenanalytical andsimulationresultsare ob-tainedirrespective ofroad topologies, i.e., grid vs. radial. On the other hand, collection coverage obtained fromsimulation is way below the analytical predictions when only 4 vehs/km is simu-lated.Thisisbecausetheagentscannotalways findvehiclesinall directions,hencehoppingbackandforth betweenthesameroad segmentslimitingthepotentialtocovermoreroadsegments dur-ingthe20hops.

FromFig.12,wecandeducethattheanalyticalmodelismore sensitivetothevehicledensity comparedtothetopology or regular-ity oftheroadnetworks.The questionwe thereforemustanswer is:what is the thresholdfor vehicledensity forwhich Lemma2

provides accurate predictions? In order to answer this question, weconductedanothersetofsimulationsbyvaryingtrafficdensity from4 to 50 vehs/kmfora targetcoverageof99%(agentdensity

λ

=

0

.

2). The resultspresentedin Fig.13show that closeto 99% target in collection coverage can be achieved for all three cities forvehicledensitygreaterthan20vehs/km.Thefactthatallthree citiesachievethesametargetcoverage of99% despitehaving dif-ferentnetworksizesfurthervalidatesTheorem1,whichstipulates that a givenagent density will realise a specific target coverage irrespectiveofthesizeoftheroadnetwork.

Notethatthethresholdof20 vehs/kmisverycommoninurban regions.Forexample,theauthorsin[46] showthatthetraffic

vol-Fig. 13. Collection coverage as a function of traffic density forλ=0.2. Table 3

Duplicateratioforthreemaps(λ=0.2).

Glasgow Sydney Paris Traffic density (vehs/km) 20 50 20 50 20 50 Distinct segments 14.95 15.03 14.37 14.93 15.02 15.17 Stored aggregates 5.91 5.96 6.02 5.94 5.90 5.86 Duplicate ratio 0.153 0.145 0.143 0.146 0.152 0.149

ume on all road types in Sydney urban region would be higher

than 800 vehs/hour from 6 AM to 21 PM, which translates to

a traffic density of 20 vehs/km if the average vehicle speed is 40 km/hour.Similarly,therealtrafficdatapresentedin[47] shows thattheaveragetrafficdensityintheroadnetworksforBeijingis higherthan20 vehs/kmformorethan16 hoursperday.

5.3. Reductionofduplicateuploads

Table3showstheperformance ofCarAgentineliminating du-plicateuploads over LTE.Weobserve thatmostof theduplicates are successfully eliminated before uploading. For example, the thirdcolumnshowsthataftercompleting20hops,eachagent col-lectsaround15 distinctroadsegmentaggregates,butonlyaround 5

.

9 ofthem on average are storedand uploaded, indicating that mostofthe duplicates aresuccessfullydetected to saveLTEcost. However, asCarAgentavoidscentralisedcontrol,a small percent-age ofduplicates, about 15% (lastcolumn), still goes undetected. Nevertheless,aswillbe shownlaterinthissection, CarAgent sig-nificantlyoutperformsthestate-of-the-art(OFC)intermsof reduc-ingtheLTEcost.

5.4. LTEcost

Fig. 14. Comparison of OFC and CarAgent in terms of RB usage in LTE base stations.

Table 4

AggregationfactorcomparisonofOFCandCarAgent.

20 vehs/km 50 vehs/km CarAgent OFC CarAgent OFC Collection coverage 96.92% 97.58% 98.02% 98.74% Aggregation factor 4.50 2.69 9.43 4.78 Number of bytes uploaded 39914.57 49730.77 39078.73 76497.33 DSRC packet collisions per second 3885.68 4021.32 26483.52 27032.33

Fig. 15. PMF of aggregation ratio for OFC and CarAgent. approach in [32]. Briefly speaking, the OFC approach employs a

contentionprocess basedon the currentLTEchannel quality and

the number of one hop DSRC neighbours for cluster head

elec-tions,suchthatvehicleswhosetimerfirstexpirebecomethe clus-terheadandbroadcastingan“inhibit”messagetosuppressothers withintheDSRCcommunicationrange.SincetheoriginalOFC per-formsexhaustiveFCDcollection,toconductafaircomparison,we implemented a modified version that assumescluster heads also perform localdata aggregation before uploading, usingthe same aggregation scheme used in CarAgent (i.e. individual FCDs from thesame roadsegment are mergedinto asingle FCD aggregate). The rest of parameters remain the same with the original set-tings.

Asexplained earlier,LTEcost fordata collectioncanbe quan-tified by both the RBconsumptions in the base stations as well asthetotalnumberofbytesuploaded. Forincreasingtraffic den-sityontheroad,RBusageandbytestransferredarecomparedfor OFCandCarAgentinFig.14andTable4,respectively.Weobserve thatRBusage remainsconstant forCarAgentdespitetheincrease intrafficdensity.ThisisbecauseCarAgentproducesasingle aggre-gate(100bytes)forevery roadsegmentthathasatleastonecar. Therefore,aslongastrafficissaturated,i.e.,atleastonecarevery roadsegment,wehavethesameamountdataproduced irrespec-tiveofthevehicledensity.

In contrast, increasing vehicular traffic also increaseswireless traffic(e.g.CAMsexchange),whichresultsinincreasingnumberof packetcollisions dueto hiddenterminalsasshowninTable4.As aresult,thecontentionprocessislessreliableindensetraffic,and alargernumberofclustersareformed,becausevehiclesmightnot beabletooverhearthe“inhibit”messageeventheyarelocatedat the proximity.This directlyleadto alower aggregationefficiency (loweraggregationfactor)andahighernumberofaggregatesper road segment uploaded to the cloud. Fig. 15 plots the probabil-itymassfunction(PMF)oftheaggregationfactorofuploadedFCD aggregatesforthe OFCandtheCarAgent.We observethat inthe case ofthe OFC, the aggregationfactor ofmore than 80% of ag-gregates is lessthan 3;for traffic densityof20 vehs/km, around 50% of uploaded aggregatesare actuallyindividual FCDs. The sit-uation worsensfor50vehs/km.Consequently,both RBusageand totalbytes transferredincrease withincreasing trafficdensity.For 50vehs/km,CarAgentconsumes50%lessLTEresources compared toOFC.

5.5. Disseminationspeed

Fig. 16. Dissemination speed.

Fig. 17. Isoline maps of the known road segments.

localknowledgebasestoring onlythe mostrecent FCDaggregate foreach roadsegment,andeach ATBbroadcastframe containsat most20 entriesrandomlyselectedfromthelist.

For vehicle density of 20 vehs/km and agent density of 0.2 (targetcollection coverageof99%), Fig.16plotsthepercentageof

road segments known by anyvehicle on average for both

CarA-gentandATB asafunctionofelapsedtime.ForCarAgentweplot both the analyticalresults obtained fromFact 2 andthe simula-tionresults.We observe thatthe analytical modelcan accurately predictdisseminationspeedforallthreecitiesunder20vehs/km. Aspredictedby Fact2,therateatwhichvehicleslearn newroad segments diminishes as time progresses, which is further illus-tratedbyplottingtheisolinemapoftheknownroadsegmentsin Fig.17after2,4,and10seconds.As wecan see,during thefirst 2seconds,thevehicleonlyknowsasmallproportionofroad

seg-mentssampled randomlyaround the region.The awareness then

growsquicklyafter 4seconds,covering almost thewhole region. Therateoflearningnewroadsegmentsthenstartstoslowdown. After10 seconds,thevehiclesonlylearnafewadditionalroad seg-ments.

WethencompareCarAgentwiththeATB.ForCarAgent,westill set

λ

=

0

.

2 (e.g.targetcoverage

=

99%). FromFig.16,weobserve thatwithCarAgent,vehiclescanlearnthetrafficconditionsinthe target area much faster than ifthey used the ATB approach. For example, after 5 seconds, vehicles learn 80% of the target area comparedtoonly50%withATB.Similarly,withCarAgent, ittakes only2secondstolearn60%oftheareawhileittakes10seconds forATB.TheimproveddisseminationspeedinCarAgentcanbe ex-plainedbythehighspeedatwhichtheagentscovertheregionby hoppingfromcars to cars, whilethe ATB approach relieson the mobilityofthecarstospreadtheinformation,whichislimitedby themovingspeedofthecars.

5.6. DSRCoverheadofFCDdissemination

Next,weinvestigatetheDSRCoverheadofCarAgentand

com-pare it against ATB. To focus on the DSRC overhead caused by

FCDaggregationdisseminationonly,weexcludethenumberbytes broadcastovertheDSRCchannel duetoCAMsexchangeandonly count the traffic due to the CarAgent and the ATB. For increas-ing traffic density, Fig. 18 compares the DSRC cost for CarAgent andATB.Weobservethatwiththeadaptiveadjustmentof broad-cast interval, the DSRC cost of ATB only increases slightly with theincrease ofvehiculardensity.However,CarAgentutilises even fewer channel resources irrespective ofthe vehicular density.For FCDdissemination,CarAgentcanreduceDSRCcostbyaround45%. ThesuccessofCarAgentisattributedtothefactthatDSRCisonly used by afinite numberofagents,which isindependent of traf-fic density, and theseagents almost do not store anyduplicated informationamongeachothers.

6. Conclusion

Fig. 18. Total number of bytes broadcast on the DSRC channel as a function of vehicle density. closeto100% collectioncoveragewhilevehiclescanlearnthe

col-lectedFCDthroughDSRCwithinafewseconds. References

[1]R.Quintero,A.Llamazares,D.Llorca,M.Sotelo,L.Bellot,O.Marcos,I.Daza, C. Fernández,Extendedfloatingcardatasystem-experimentalstudy,in:Proc. oftheIEEEIntelligentVehiclesSymposium(IV),2011,pp. 631–636. [2]P.Salvo,I.Turcanu,F.Cuomo,A.Baiocchi,I.Rubin,Heterogeneouscellularand

DSRCnetworkingforfloatingcardatacollectioninurbanareas,Veh.Commun. 8(2017)21–34.

[3]J.E.NaranjoHernández,F.JiménezAlonso, F.SerradillaGarcía,J.G. Zato Re-cellado,Comparisonbetweenfloatingcardataandinfrastructuresensorsfor trafficspeedestimation,in:Proc.oftheIEEEInternationalConferenceon Intel-ligentTransportationSystems(ITSC),2010.

[4]F.Rempe,P.Franeck,U.Fastenrath,K.Bogenberger,Onlinefreewaytraffic es-timationwithrealfloatingcardata,in:Proc.oftheIEEEInternational Confer-enceonIntelligentTransportationSystems(ITSC),2016,pp. 1838–1843. [5]R. Blokpoel,J. Vreeswijk,Uses ofprobevehicle dataintrafficlightcontrol,

Transp.Res.Proc.14(2016)4572–4581.

[6]J.B. Kenney,Dedicated short-rangecommunications (DSRC)standardsinthe UnitedStates,Proc.IEEE99 (7)(2012)1162–1182.

[7] ETSI,302665v1.1.1:IntelligentTransportSystems(ITS),communications ar-chitecture,EuropeanStandard(TelecommunicationsSeries).

[8]K.Zheng,Q.Zheng,P.Chatzimisios,W.Xiang,Y.Zhou,Heterogeneousvehicular networking:asurveyonarchitecture,challenges,andsolutions,IEEECommun. Surv.Tutor.17 (4)(2015)2377–2396.

[9]C.Ide,F.Kurtz,C.Wietfeld,Cluster-basedvehiculardatacollectionforefficient LTEmachine-typecommunication,in:Proc.oftheIEEEVehicularTechnology Conference(VTCFall),2013,pp. 1–5.

[10]S.Ucar,S.C.Ergen,O.Ozkasap,Multihop-cluster-basedIEEE802.11pandLTE hybridarchitectureforVANETsafetymessagedissemination,IEEETrans.Veh. Technol.65 (4)(2016)2621–2636.

[11]P.M.D’Orey,N.Maslekar,I.delaIglesia,N.K.Zahariev,NAVI:neighbor-aware virtualinfrastructureforinformationcollectionanddisseminationinvehicular networks,in:Proc.oftheVehicularTechnologyConference(VTCSpring),2015. [12]G.Remy, S.-M.Senouci,F.Jan,Y. Gourhant,LTE4V2X:LTE fora centralized VANETorganization,in:Proc.oftheIEEEGlobalTelecommunications Confer-ence(GLOBECOM),2011,pp. 1–6.

[13]G.Rémy,S.-M.Senouci,F.Jan,Y.Gourhant,LTE4V2X—collection, dissemina-tionandmulti-hopforwarding,in:Proc.oftheIEEEConferenceon Communi-cations(ICC),2012,pp. 120–125.

[14]S.Dietzel,J.Petit,F.Kargl,B.Scheuermann,In-networkaggregationfor vehic-ularadhocnetworks,IEEECommun.Surv.Tutor.16 (4)(2014)1909–1932. [15]R.Du,C.Chen,B.Yang,N.Lu,X.Guan,X.Shen,Effectiveurbantraffic

mon-itoringbyvehicularsensornetworks,IEEETrans.Veh.Technol.64 (1)(2015) 273–286.

[16]K.Abboud, W.Zhuang,Stochasticmodelingofsingle-hopclusterstability in vehicularadhocnetworks,IEEETrans.Veh.Technol.65 (1)(2016)226–240. [17]T.Nadeem,P.Shankar,L.Iftode,Acomparative studyofdatadissemination

modelsforVANETs,in:Proc.oftheIEEEAnnualInternationalConferenceon MobileandUbiquitousSystems-Workshops,2006,pp. 1–10.

[18]C.Sommer,O.K.Tonguz,F.Dressler,Trafficinformationsystems:efficient mes-sagedisseminationviaadaptivebeaconing,IEEECommun.Mag.49 (5)(2011). [19]K.Z. Ghafoor, K.A. Bakar, M. van Eenennaam, R.H. Khokhar, A.J. Gonzalez, A fuzzylogicapproachtobeaconingforvehicularadhocnetworks, Telecom-mun.Syst.52 (1)(2013)139–149.

[20]G.Bansal,J.B.Kenney,Controllingcongestioninsafety-messagetransmissions: aphilosophyforvehicularDSRCsystems,IEEEVeh.Technol.Mag.8 (4)(2013) 20–26.

[21]W.KleinWolterink,G.Heijenk,H. vanden Berg,Analyticallymodelling the performanceofpiggybackingonbeaconsinVANETs,in:Proc.oftheACM Inter-nationalWorkshoponVehicularInter-networking,Systems,andApplications, 2012,pp. 43–52.

[22]M. Chen,S. Gonzalez, Y. Zhang, V.C.Leung, Multi-agent Itinerary Planning forWirelessSensorNetworks,QualityofServiceinHeterogeneousNetworks, 2009,pp. 584–597.

[23]C.Konstantopoulos,A.Mpitziopoulos,D.Gavalas,G.Pantziou,Effective deter-minationofmobileagentitinerariesfordataaggregationonsensornetworks, IEEETrans.Knowl.DataEng.22 (12)(2010)1679–1693.

[24]A.Kanan,T.Eldos,M.Al-Kahtani,Mobileadhocnetworksroutingusingant colonyoptimization,WorldComput.Sci.Inf.Technol.J.3 (6)(2013). [25]D.Kadono,T.Izumi,F.Ooshita,H.Kakugawa,T.Masuzawa,Anantcolony

opti-mizationroutingbasedonrobustnessforadhocnetworkswithGPSs,AdHoc Netw.8 (1)(2010)63–76.

[26]G.Singh,N.Kumar,A.K.Verma,AntcolonyalgorithmsinMANETs:areview, IEEEJ.Netw.Comput.Appl.35 (6)(2012)1964–1972.

[27]S.J.Gudakahriz,S.Jamali,E.Zeinali,NISR:anatureinspiredscalablerouting protocolformobileadhocnetworks,J.Comput.Sci.Eng.Technol.1 (4)(2011) 180–184.

[28]A.Soua,H.Afifi,Adaptivedatacollectionprotocolusingreinforcementlearning forVANETs,in:Proc.oftheIEEEInternationalWirelessCommunicationsand MobileComputingConference(IWCMC),2013,pp. 1040–1045.

[29]R.T.Meier,J.Dunkel,Y.Kakuda,T.Ohta,Mobileagentsforservicediscoveryin adhocnetworks,in:Proc.oftheIEEEInternationalConferenceonAdvanced InformationNetworkingandApplications(AINA),2008,pp. 114–121. [30]T.Hashimoto,J.Aoki,T. Ohta,Y. Kakuda,Performanceevaluationofmobile

agent-basedservicecollectionanddisseminationschemeconsideringentrance andexitofnodesinVANETs,in:Proc.oftheIEEEInternationalSymposiumon ComputingandNetworking(CANDAR),2014,pp. 284–287.

[31]F.Dressler,H.Hartenstein,O.Altintas,O.Tonguz,Inter-vehiclecommunication: quovadis,IEEECommun.Mag.52 (6)(2014)170–177.

[32]I.Turcanu,C.Sommer,A.Baiocchi,F.Dressler,Picktherightguy:CQI-based LTEforwarderselectioninVANETs,in:Proc.oftheIEEEVehicularNetworking Conference(VNC),2016,pp. 1–8.

[33]S.Panichpapiboon,W.Pattara-Atikom,Areviewofinformationdissemination protocolsforvehicularadhocnetworks, IEEE Commun.Surv.Tutor. 14 (3) (2012)784–798.

[34]M.Chaqfeh,A.Lakas,I.Jawhar,Asurveyondatadisseminationinvehicularad hocnetworks,Veh.Commun.1 (4)(2014)214–225.

[35] ETSI,102962,Intelligenttransportsystems(ITS),Frameworkforpublicmobile networksincooperativeITS(C-ITS)(Tech.Rep.No.1.1.1).ETSITechnical Com-mitteeIntelligentTransportSystem.EuropeanTelecommunicationsStandards Institute(2012).

[36]H.Füßler, J. Widmer,M.Käsemann, M.Mauve, H.Hartenstein, Contention-based forwardingfor mobile adhocnetworks, AdHoc Netw. 1 (4) (2003) 351–369.

[37]F.Klingler,F.Dressler,C.Sommer,IEEE802.11punicastconsideredharmful, in:Proc.oftheIEEEVehicularNetworkingConference(VNC),2015,pp. 76–83. [38]S.Kuhlmorgen,I.Llatser,A.Festag,G.Fettweis,PerformanceevaluationofETSI geonetworkingforvehicularadhocnetworks,in:Proc.oftheIEEEVehicular TechnologyConference(VTCSpring),2015.

[39]S.Kuhlmorgen,A.Festag,G.Fettweis,Impactofdecentralizedcongestion con-troloncontention-basedforwardinginVANETs,in:Proc.oftheIEEEWorldof Wireless,MobileandMultimediaNetworks(WoWMoM),2016,pp. 1–7. [40] ETSI,302636-4-1v1.2.1:IntelligentTransportSystems(ITS);Vehicular

Com-munications,EuropeanStandard(TelecommunicationsSeries).

[41]L.Rodero-Merino,A.F.Anta,L.López,V.Cholvi,Performanceofrandomwalks inone-hopreplicationnetworks,Comput.Netw.54 (5)(2010)781–796. [42]Y.Duan,F.Lu,Robustnessofcityroadnetworksatdifferentgranularities,Phys.

[43]M.Behrisch,L.Bieker,J.Erdmann,D.Krajzewicz,SUMO–simulationofurban mobility:anoverview,in:Proc.oftheThirdInternationalConferenceon Ad-vancesinSystemSimulation,2011,pp. 63–68.

[44]A.Virdis,G.Stea,G.Nardini,SimuLTE-a modularsystem-level simulatorfor LTE/LTE-Anetworksbased onOMNeT++, in:Proc. oftheIEEEInternational ConferenceonSimulationandModelingMethodologies,Technologiesand Ap-plications(SIMULTECH),2014,pp. 59–70.

[45]C.Sommer,R.German,F.Dressler,Bidirectionallycouplednetworkandroad trafficsimulationforimprovedIVCanalysis,IEEETrans.Mob.Comput.10 (1) (2011)3–15.

[46]N.Orthongthed,B.Wang,J.Legaspi,Estimatingcostexpansionfactorsinthe SydneyurbanandNSWruralroadnetworksforeconomicevaluationofroad projects,in:AustralasianTransportResearchForum(ATRF),2013.