A Delay-Sensitive Vehicular Routing Protocol UsingAnt Colony Optimization

Guangyu LiLaboratoire de Recherche en Informatique (LRI)CNRS UMR 8623, University of Paris-Sud 11

Building 650, 91405 Orsay, FranceEmail: guangyu@lri.fr

Lila BoukhatemLaboratoire de Recherche en Informatique (LRI)CNRS UMR 8623, University of Paris-Sud 11

Building 650, 91405 Orsay, FranceEmail: Lila.Boukhatem@lri.fr

Abstract—Vehicular Ad hoc Networks (VANETs) are confront-ed with numerous dif culties and challenges, such as scalabilityissues, rapid changes of network topology and channel capac-ity restriction, which can induce communication deterioration.In this paper, we propose a delay-sensitive vehicular routingprotocol, which uses the intersections as anchors to establishoptimal delay routing paths consisting of a list of intersections.The main feature of our protocol is the periodic estimation ofthe road segment delay expressed in the combination of averagedelay and delay variance using multi-hop vehicle relaying. Asthis estimation is local to road segments, we make use of ACO(Ant Colony Optimization) concept to discover end-to-end bestdelay paths from source to target intersection which is closestto the destination. Route setup process is achieved by reactiveforward ants and backward ants, which are in charge of networkexploration and pheromone dissemination respectively. Routingselection is implemented at each intersection to opportunisticallychoose best next intersection based on a pheromone routingtable. A proactive route maintenance is initiated by source toupdate, expand and improve the routing information during datatransmission period using periodic proactive ants sampling. Inaddition, we make use of simple carry and/or greedy forwardingtechnique to relay packets between adjacent intersections. Thesimulation results indicate that our protocol shows better commu-nication performance compared with a basic geographical routingprotocol (GPSR) and a min-delay routing protocol (CAR) inregard to delivery ratio, average end-to-end delay and overhead.

I. INTRODUCTION

Vehicular Ad hoc Networks (VANETs) are introduced asa subclass of Mobile Ad hoc Networks (MANETs), and con-stitute a promising approach for the intelligent transportationsystems (ITS) [1]. In VANETs, the vehicles are able to act asrouting nodes to exchange information using either vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) commu-nications. V2V communications take place among On-BoardUnits (OBUs) equipped in each vehicle, and V2I communi-cations occur between Road-Side Units (RSUs) and OBUs,as illustrated in Figure 1. VANETs are utilized to support alarge range of distributed applications including traf c control,safety application and driver assistance in ITS [2], [3].

In VANET environment, there are many challenges includ-ing the large network size, the high mobility, the broadcaststorm problems and the limited channel bandwidth, whichmake data transmission frequently suffer from either networkholes or traf c congestion, and lead to great dif culties in

Servicer

Internet

RSUs

RSUs

OBUs

OBUs

Ad Hoc Network Domain

V2V

V2I

V2V

V2I

V2I

V2V

Internet

OBUs

OBUs

V2V

V2I

OBUs

OBUs

OBUs

OBUs

Fig. 1: VANETs fundamental architecture

estimating links relaying quality. To resolve these problems, wepropose an adaptive routing protocol based on the cumulativerelaying delay of road segments (road between two adjacentintersections). Firstly, our protocol exploits the ACO conceptsand makes use of reactive forward ants to explore possiblepaths which is composed of a list of intersections between thesource and the target intersection (the closest intersection to thedestination vehicle). Then, reactive backward ants generated bythe target intersection are sent back to the source following thecorresponding reverse paths of reactive forward ants, and dis-seminate pheromone at intersections using the relaying delayvalues of the visited road segments. The pheromone routingtable available at each intersection helps vehicles opportunisti-cally select best next intersection for packet forwarding, and asimple carry and/or greedy forwarding strategy is used to relaypackets between adjacent intersections. Finally, we initiate aproactive route maintenance phase to periodically adapt to thevariation of routing information.

A. Related Works

Geographic routing is a promising method in VANETs,and it can progressively forward packets to the neighboringnode closest to the destination using the physical location.GPCR [4] assumes that each road segment is an edge of aplanar graph and each vehicle located at an intersection is avertex. Routing decisions are then made at intersections. The

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assumption of GPCR may not exist in realistic environments,once there is not any vehicle on intersections, data packetswill be forwarded over intersections and suffer from routingloops. By detecting potential network partitions in advance ormaking use of channel overhearing capability, [5] and [6] havebeen proposed to decrease the hop counts on recovery paths.However, both proposals do not consider the real-time relayingquality of the current routing paths.The concept of anchor-based routing has been utilized

in VANETs. GyTar [7] utilizes vehicles density and roadcurvilinear distance between the current intersection and thedestination to dynamically select the next candidate roadsegment. However, GyTar does not consider for route selectionthe traf c load condition, which increases packets delay anddropping ratio in case of channel congestion. SADV [8] makesuse of static nodes at road intersections to assist in datarelaying. Packets can then be buffered for a while in thestatic nodes until a suitable vehicle is available along the bestdelivery path to further forward the packet. Although SADVexploits the real-time delay between two adjacent intersections,single average delay can not re ect the whole performance ofan end-to-end routing path.

In the combinatorial optimization eld, ACO [9], [10],[11], [12] is a famous swarm intelligence approach and takesthe inspiration from real ants wandering around their nests tosearch for food. Once reaching the food source, the ants returnback to their nests and simultaneously deposit pheromone trailsalong the paths. The pheromone is used for the followingants to select their next hops. Because of its robustnessand adaptive characters, ACO is applied widely for routingprotocols in wireless networks. [13] proposes a multi-agent antbased routing algorithm, which combines both proactive andreactive components to achieve good performance in regard toend-to-end delay and packet delivery ratio. Ant-E [14] makesuse of the blocking expanding ring search (Blocking-ERS) tocontrol the overhead and limit local retransmissions. Based ondynamic zones, AD-ZRP [15] acts together with ACO, andyet improves the ef ciency of route discovery and the routemaintenance.

B. Main Contributions

The main contributions of our protocol are as follows:

First of all, our protocol sets up optimal routing paths fromsource to target intersection using either unicast or broadcasttransmission, rather than a systematic route between end-to-end vehicles by broadcast. Obviously, our protocol is bene cialin the exploration ef ciency of routes setup procedure, theprovision of more backup routing paths and the alleviationof traf c congestion along paths.

In addition, we design a simple but ef cient model to esti-mate the delay of road segments periodically by combining twolatest parameters (average delay and delay variance) instead ofinaccurate statistical data or instantaneous delay values, whichare subject to rapid variation.

Moreover, in order to resolve the proposed NP (non-deterministic polynomial) hard routing problems, we makeuse of ACO algorithm to implement routing decision at eachintersection along the path. Compared with other routing infor-mation consisting of the total sequence of hops or intersections

D

I3 I5

I4

I2

I1

I6=Itar

SUnoptimizable

Route

UnoptimizableRoute

The route of forward ant A

The route of backward ant BThe route of backward ant BThe route of backward ant A

Fig. 2: A simple illustration of our protocol concept

of the paths, our protocol can increase the stability of routingpaths, relieve the effect of link failure and the amount ofoverhead.

Last but not least, our protocol takes advantage of proactiveroute maintenance to update and extend routing paths. Thismethod can availably cope with the rapid changes of topologyin VANET environment in time compared with other reactivemaintenance processes.

The rest of this paper is organized as follows. SectionII presents our model to estimate the relaying delay of roadsegment. Our adaptive routing protocol based on ACO conceptis described in Section III. Section IV shows the simulationand results analysis. Finally, Section V concludes the paper.

II. ESTIMATION MODEL OF ROAD SEGMENTDELAY

Delay is a key parameter for data relay and can indirectlyre ect the current transmission loads, the vehicles density andthe vehicles distribution on the road segment. However, itis very dif cult to accurately estimate the delay due to itshigh variability, especially in a dynamic environment such asvehicular networks. To resolve this issue, we capture the globalfeature of this parameter rather than its instantaneous value,and propose the following estimating model for delay inspiredby RTT (Round Trip Time) computation in TCP (TransmissionControl Protocol).

Arriving at the intersection Ii, a data packet adds both theidenti er of intersection Ii and the packet sending time Tij snd

into its header when it is forwarded to the next intersection I j .Upon receiving the data packet, Ij can estimate the relayingdelay of the road segment. The expected average delay D ij av

and delay variance Dij var between Ii and Ij are estimatedas follows:

Dij av ← (1− α) ·Dij av + α · dij (1)

Dij var ← (1− β) ·Dij var + β · |dij −Dij av| (2)

where α and β are weighting factors with values between 0and 1, dij is an instantaneous delay illustrated as follows:

dij = Tij rev − Tij snd (3)

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Ii Ij

Type Delay to target Intersection

Pheromone to target Intersection

List of IntersectionSource Target

IntersectionTy S Itar D_bant _bantIlist

Fig. 3: Backward ant packet format

where Tij rev denotes the receiving time of data packet at Ij .

Then, after normalization, Dij av and Dij var are ex-pressed respectively as:

Dij av ← 1− 2

π· arctanDij av (4)

Dij var ← 1− 2

π· arctanDij var (5)

Finally, the delay Dij of the road segment is derived as:

Dij = ν ·Dij av + (1 − ν) ·Dij var (0 < ν < 1) (6)

In order to keep the freshness of road segment delay, weset a timer Texp at each intersection. Once timer expirationoccurs, the relaying delay parameter stored at each intersectionis reset.

In contrast to other estimating models, our model is simplebut effective to avert the direct utilization of instantaneousmetric values, which are subject to rapid variations due tothe highly dynamic communication environment. Besides, wemake use of data packets travelling from neighboring inter-sections rather than hello packets to implement the delayestimation, so our model can signi cantly reduce the amountof overhead and alleviate the traf c congestion.

III. OUR ADAPTIVE ROUTING PROTOCOL

In this section, we describe the different components of ourprotocol in detail. In this routing protocol, we assume that asimple road communication infrastructure (such as Road SideUnits, RSUs) is installed at each intersection, which can helpthe vehicles make routing decisions. We also assume that everyvehicle is equipped with a digital map, a GPS facility and anavigation system. In addition, an available location serviceproviding the geographical location for vehicles, is required.

Our protocol combines both reactive and proactive com-ponents to respectively establish and maintain optimal routesbetween the source and the target intersection (closest to thedestination) in terms of relaying delay mentioned in Section II.At the beginning, data source executes a reactive route setupprocess. Derived from the ACO algorithm, reactive forwardants generated by data source are relayed towards the targetintersection to explore routing paths. Upon reactive forwardants arrive at the target intersection, reactive backward antsare generated and then sent back to the source following thereverse paths of the forward ants, to update pheromone valuesand set up optimal routing paths at intersections. Pheromonetables attached at each intersection are utilized to select the bestnext intersection for data packets with certain probability. Sim-ple carry and/or greedy forwarding is used by vehicles to make

data packets arrive at next candidate intersection. Besides, ourprotocol makes use of proactive ants sampling to carry outroute maintenance mechanism, which can periodically update,expand and improve routing information along routing paths.Figure 2 illustrates a simple concept of our protocol to set

up the best routes from the source S to the target intersectionItar , which is the nearest intersection to the destination D.Based on relaying delay of road segments, our protocol canavoid the selection of the shortest path consisting of [I2, I1]to D where the vehicle density is very sparse. Besides, ourprotocol prefers to choose the routing path composed of [I 3,I4, I5, I6], rather than the one made up of [I3, I6] which isshorter but suffers from far higher vehicles density causingserious congestion collision. Here, solid lines mean the routesof reactive forward ants to explore routing paths towards I tar,and dashed lines denote the routes of reactive backward antsto disseminate pheromone and set up routing tables.

A. Reactive Route Setup

This section elaborates the reactive route setup process,which involves the transmission of reactive forward and back-ward ants between the source and the target intersection.1) Reactive Forward Ants Process: Initially, reactive for-

ward ant packets are generated by the source S to explorethe network and nd routing paths towards the target in-tersection Itar. When arriving at each intersection, reactiveforward ants add the identi er of the intersection to their ownheaders. Based on carry and/or greedy forwarding scheme,reactive forward ants are then relayed progressively to nextcandidate intersection by either broadcast or unicast trans-mission depending on whether the current intersection I i hasrouting information for the target intersection I tar . If therouting information at Ii is available, reactive forward ants areforwarded to the next candidate intersection by unicast withcertain probability. Concretely, Ii chooses next intersection Ij

with probability P Itar

ij , which is declared as follows:

P Itar

ij =(τItar

ij )γ∑k∈N

Itari

(τItar

ik )γ(γ ≥ 1) (7)

where N Itar

i is the set of neighboring intersections of currentintersection Ii over which a path to Itar exists, γ is anexploratory rate parameter of forward ants, and τ Itar

ij denotesthe pheromone value for passing through I j when packetstravel from Ii towards the target intersection Itar. Note thatthe level of pheromone on a path indicates how good that pathis, i.e. re ects its relaying delay.If there is not available routing information for I tar at

intersection Ii, which will broadcast the reactive forward ants

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to con rm them to proliferate rapidly over the whole network.The multiple copies of the ant will then stochastically explorethe different paths to the target intersection Itar . In order torestrict the amount of overhead and avoid transmission loops,only the rst copy of a reactive forward ant can be forwarded.This scheme is able to be achieved using the source identi erand the ant sequence number both piggybacked in the forwardant header.

2) Reactive Backward Ants Process: When reactive for-ward ants arrive at the target intersection Itar , reactive back-ward ants are initiated and then returned back (using unicasttransmission) to the source S following the correspondingreverse paths. The objective of reactive backward ants is tocollect relaying delay of road segments, and then disseminateand update pheromone at each intersection along the paths.The format of a reactive backward ant forwarded from I j toIi is described in Figure 3.

At the beginning, upon arriving at I i (from Ij), a reactivebackward ant updates its delay parameter D bant (see Figure3), which delegates the relaying delay from I i to Itar goingthrough Ij . With respect to the relaying delay Dij between Iiand Ij , the updating process is illustrated as follows:

D bant ← D bant + Dij (8)

Then, the pheromone value τ Itar

ij of routing table at Ii isupdated by the reactive backward ant as follows:

τItar

ij ← (1− ε) · τItar

ij + ε ·D bant (9)

where ε is a factor regulating the in uence of the new measuredvalues on the current pheromone.

Eventually, we make use of the new pheromone value τ Itar

ijto update the pheromone τ bant recorded in the backward ant.The last value of τ bant assigned to the backward ant willbe used by the source S to select the rst intersection fordata packets relay. The concrete processing steps of reactivebackward ants is explicated in Algorithm 1.

In order to avoid a rapid convergence of our algorithmtowards a suboptimal region, we initiate a pheromone evapo-ration process. In every Tev seconds interval, the pheromonelevel of all road segment links decreases following a mathe-matical model, which can display a favorable balance betweenalgorithm convergence and exploration ef ciency of routingpaths. The concrete model is declared as follows:

τItar

ij (t + Tev) =

{η · τItar

ij (t) if τ Itar

ij (t) > τmin

τmin if τ Itar

ij (t) ≤ τmin(10)

where pheromone evaporation factor η < 1 and τmin is aconstant value.

Once the reactive routes setup is nished, the sourceS launches the data communication sessions. Data packetsbetween adjacent intersections are forwarded using a carryand/or greedy forward strategy. Afterwards, upon data packetsarrive at a intermediate intersection Ii, which makes a routingdecision for data packets by selecting next optional intersectionIj with the highest probability P Itar

ij . The detailed expressionof P Itar

ij is declared in equation 7. Note that here the valueof γ is higher than the one in Reactive Forward Ants Process

Algorithm 1 Processing steps of reactive backward ant1: IDk denotes the identi er of intersection or vehicle wherea reactive backward ant arrives. S is the data source. D ij

means the relaying delay of the road segment between I i

and Ij .2: ***********************************************3: if S! = IDk then4: Assume that the backward ant arriving at intersection I i

is from Ij .5: if Ii == IDk then6: (S1: Update reactive backward ant’s delay parameter.)7: D bant ← D bant + Dij

8: *******************************************9: (S2: Update pheromone value τ Itar

ij using the newD bant.)

10: τItar

ij ← (1− ε) · τItar

ij + ε ·D bant

11: *******************************************12: (S3: Update pheromone τ bant of the backward ant

using τ Itar

ij .)13: τ bant ← τItar

ij and then Ii forwards this backwardant to its neighboring intersection towards S.

14: else15: A vehicle forwards this backward ant progressively

using simple carry and/or greedy forwarding to nextintersection.

16: end if17: else18: The backward ant arrives at the source S, and establish-

es an available routing path.19: end if

phase. This con guration can make data packet forwardingmore greedy to the best available routes compared with antpacket forwarding. Finally, upon arriving at I tar , data packetsare relayed by Itar directly to the destinationD by carry and/orgreedy forwarding.

B. Proactive Route Maintenance

A proactive route maintenance process is proposed toupdate the available routing information and nd alternatepaths for data packets relay. This process is initiated by thesource S, using proactive ant sampling (including proactiveforward and backward ant) to gather latest relaying delay ofroad segments.

Initially, the source S validates the latest position of desti-nation D. If D is on the same or a neighboring road segmentcompared to its initial position (during the reactive route setupphase), target intersection Itar remains unchanged and then aperiodic transmission of proactive forward ants towards I tar isimplemented. At each intersection Ii, it stochastically choosesa candidate next intersection for proactive forward ant usingthe same probability formula given in equation 7. Neverthe-less, we give a smaller value of γ parameter for proactiveforward ants than the one used for reactive forward ants in therouting setup phase. This decision is helpful in extending theexploration range and uniformly spreading the traf c load overthe whole network. When the proactive forward ant reachesits Itar, a proactive backward ant is generated and it has thesame operation in comparison with the reactive backward ant

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TABLE I: Simulation setup parametersParameter Value

Number of nodes up to 200Packet size 512 BytesNumber of reactive forward ants M 7Reactive route setup factor γ 12Proactive route maintenance factor γ 5Data packet forwarding factor γ 18Weighting factor in pheromone calculation ε 0.3Weighting factor in delay estimation (α, β, ν) (0.3, 0.3, 0.6)Pheromone evaporation weight (η, τmin) (0.9, 0.1)Evaporating interval Tev 5 s

for pheromone update on the reverse path. In case destinationD has moved out of the neighboring road segments, a newreactive route setup process is executed.

IV. PERFORMANCE EVALUATION

A. Experimental Setup

The performance evaluation of our protocol has been in-vestigated and compared with two reference routing protocols(GPSR [16] and CAR [17]). In the simulation scenario, mobilenodes are placed in a 2000m× 2000m region, which is com-posed of 16 intersections and 26 road segments. In each roadsegment, mobile nodes move with a velocity randomly selectedbetween 25km/h and 55km/h. The nodes transmission rangeis set to 250m. Besides, an IEEE 802.11p mechanism isadopted with a channel capacity of 2Mbps. Further simulationparameters are listed in TABLE I.

In our experiments, the sources send data packets at differ-ent constant bit rates. The simulation time duration is xed to1000 seconds and each simulated scenario is repeated 25 timeswith different seeds to guarantee good con dence intervals forthe results.

B. Results Analysis

1) Average Delivery Ratio: Figure 4 presents the trend ofaverage delivery ratio at different data transmission rate. Thegure reveals that our protocol displays higher delivery ratiothan CAR and GPSR. There are three main reasons to explainthis result. First of all, when data communication sessions arelaunched, our protocol initiates proactive routes maintenanceso that the latest routing information will be gathered and thenadopted for data transmission. Besides, in case of sufferingfrom network partitions in the simulated traf c scenario, GPSRcontinues to forward data packets using the perimeter mode,while our protocol can overcome this problem easily thanksto the cumulative relaying delay of road segments. Last butnot least, our protocol forwards data packets to the optimalnext intersection with certain probability, which can spreadtraf c load more uniformly and alleviate the effect of traf ccongestion.

2) Average Delay: Figures 5 describes the average end-to-end delay as a function of the number of vehicles. Inthis gures, we observe that our protocol outperforms othertwo reference protocols. As using greedy algorithm to thedestination, GPSR may forward data packets along the samepath which can either cause intense traf c congestion orundergo network partition because of the selected path with

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.4

0.5

0.6

0.7

0.8

0.9

1

Transmission rate (packet/s)

Del

iver

y R

atio

Our ProtocolCARGPSR

Fig. 4: Delivery ratio versus transmission Rate

20 40 60 80 100 120 140 160 180 2000

10

20

30

40

50

60

Number of nodes

Ave

rage

del

ay (

seco

nds)

Our ProtocolCARGPSR

Fig. 5: End-to-end delay versus number of vehicles

sparse vehicle density. Both situations result in an increase ofthe overall delay. CAR protocol is not adaptive to the traf cload change and latest channel congestion situation in time,owing to the fact that it is a source routing protocol whereanchor intersections are selected at route setup phase. Besides,CAR forwards data packets along only one best routing pathand does not have any backup paths. While our protocol candynamically make routing decisions at intersections based onthe periodically updated pheromone. Finally, we can see inFigure 5 that all protocols experience a lower delay with theincrease of vehicle density. The reason for this result is thatmoderately high vehicle density (below a certain threshold)can reduce routing holes, which require route repair or packetcarrying procedures leading to the transmission delay increase.

3) Overhead: We de ne the overhead as the average size ofcontrol overhead injected into the network for every success-fully delivered data packet. Obviously, for all protocols, themajor part of the overhead amount concerns the hello packetsperiodically generated by vehicles due to the neighboringdiscovery procedure. Figure 6 illustrates the network overheadas a function of data transmission rate. In this gure, wecan see that the network overhead decreases rapidly at the

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

7x 10

4

Transmission rate (packet/s)

Siz

e of

ove

rhea

d pe

r re

ceiv

ed p

acke

t (bi

ts/p

acke

t)

Our ProtocolCARGPSR

Fig. 6: Overhead versus transmission rate

beginning, as more packets are successfully transmitted to thedestination with the increasing transmission rate. However,as the transmission rate is further rising, the deterioration ofthe relaying quality along routing paths and the initiation ofrouting repairs result in the slight increase of overhead. Inaddition, our protocol displays lower overhead in comparisonwith CAR and GPSR. Two main reasons account for suchbehavior. Initially, our protocol selects optimal routing pathsdepending on the delay, which promises that more data packetscan be transmitted successfully in a certain period of time.Besides, based on pheromone tables, our protocol opportunis-tically chooses for data packets optimal next intersection. Thisscheme helps to spread the traf c load over the network, whichcan relieve the congestion of routing paths and maintain ahigher delivery ratio.

V. CONCLUSION

In this paper, we described a new vehicular routing proto-col. Our protocol can adaptively chooses the optimal routingpaths on the basis of the road segments’ relaying delay, whichis periodically estimated in terms of the average delay and thedelay variance. As the delay information is obtained locally atintersections, we make use of the ACO concept to explore end-to-end paths between end intersections. Route setup process iscarried out by both reactive forward and backward ants whichare utilized to explore network and establish paths respectively.The pheromone dissemination is declared with respect to therelaying delay of the visited road segments. Based on thepheromone routing tables at each intersection, routing decisionin our protocol is made by opportunistically selecting opti-mal next intersection. Simple carry and/or greedy forwardingis used for packet relaying between adjacent intersections.Proactive route maintenance is implemented to continuouslyupdate the pheromone values in routing tables by periodicproactive ants sampling. The simulation results indicate thatour protocol outperforms the reference protocols (GPSR andCAR) in terms of delivery ratio, average end-to-end delay andoverhead. As future work, we will further investigate otherparameters’ effects on relaying quality of road segment, andalso consider more realistic vehicular urban traf c models forsimulations.

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