Proceedings of the 9th International Conference on Mobile and Wireless Communications Networks, Cork, Ireland, September 19-21, 2007

Performance Analysis ofPosition-BasedRouting Approaches in VANETS

Miguel Garcia de la FuenteTelecom Unit, Robotiker-Tecnalia

Zamudio - SpainEmail: mgarcia@robotiker.es

Abstract-This article presents a performance analysisbetween two location-based routing protocols: SIFT (SImpleForwarding over Trajectory), a novel, scalable, spatial-aware,trajectory-based approach, and DREAM (Distance RoutingEffect Algorithm for Mobility), a stable, largely tested position­based scheme. The study was accomplished under a realisticurban mobility model for VANETS (Vehicular Ad hocNETworks), within a highly deployed evaluation network of upto 1000 nodes. Classical ad hoc routing schemes do not performwell in VANETS because they were not designed to handleefficiently mobility handicaps. Position-based techniques performbetter in dynamic scenarios, but in some highly dynamicscenarios, like VANETS, they do not always perform efficiently.Trajectory-based protocols perform more efficiently in VANETSsince they are spatial-aware. We demonstrate that SIFTperforms better than DREAM in a realistic VANET scenarioconcerning delivery ratio, control overhead, delivery delay, androute length.

Index Terms- Vehicular ad hoc networks, routing,geographical routing, trajectory based forwarding, positionbased routing, mobility model, spatial aware.

1. INTRODUCTION

A VANET (Vehicular Ad hoc NETwork) is a specialvariety of Wireless Ad Hoc Networks, where nodes arevehicles that move at high speed, and their movements areconstrained to roads layout and traffic rules. VANETSprovide communications among vehicles to carry out differenttasks like cooperative driver-assistance, traffic management orcommercial services. Network topology is strongly linked toroad topology, since vehicles mobility is restricted to roadslayout. Nodes mobility may change network topology andinvalidate existing routes. With these special characteristics,classical routing approaches for generic ad hoc networks donot perform efficiently in VANETS.

An important number of routing protocols have beendeveloped for wireless ad hoc networks. Depending on thetype of information used for routing, they can be classifiedinto two categories: topology-based [1] and position-based[2]. Classical ad hoc protocols are topology-based as routingdecisions are based on existing links among nodes. OLSR[14], DSR [13], AODV [9] or DSDV [8] are some examplesof proactive and reactive protocols. All they are link table-

978-1-4244-1720-9/07/$25.00 ©2007 IEEE

Houda LabiodEcole Nationale Superieure des Telecommunication (ENST)

Paris - FranceEmail: labiod@enst.fr

driven since nodes build a routing table where they record avalid path for each possible destination. Paths depend onexisting links, and links are dependent on network topology.When nodes move, links change, and paths must berecalculated. In dynamic scenarios, the control overheadgenerated to calculate routes can be extremely high, resultingin low-performance networks [7]. To prevent that, it isrequired a different forwarding paradigm more suitable forvery dynamic scenarios.

In Position-Based (PB), routing decisions are based on thegeographical coordinates of nodes. Some examples of PBprotocols are GPSR [4], LAR [10], or DREAM [3]. PBprotocols are more efficient [7] for dynamic scenarios, likeVANETS. In PB methods, each node must create andmaintain updated a location table, containing the geographicalposition of its neighbours. To maintain these tables up-to-date,when nodes move, they send a control message with its newposition. PB methods reduces appreciably control overhead,since the information amount necessary to build a locationtable is smaller than to build a link table. Anyhow, in PBprotocols, control overhead may also cause low performancein highly deployed networks.

Routing paths, defined as sequence of forwarding nodes,are unstable due to topology changes, while geographicalroutes, defined as lines, are quite stable due to the physicalcharacteristics of the service area. Trajectory-BasedForwarding (TBF) [5] exploits this basic observationproposing a PB routing scheme that requires the source nodeto encode a geographical line, referred to as trajectory, into thepacket header. Since the sequence of forwarding nodes is notspecified, packets are routed hop-by-hop according to nodesposition with respect to the trajectory. The forwardingschemes proposed in [5], [6] and [17] are based on point-to­point transmissions. Differently from previously proposedTBF schemes, SIFT [15] is based on broadcast transmissionsand does not require neighbours position knowledge, since theforwarding decision is shifted from the transmitter to thereceiver. As each node does not need to know anything aboutthe rest of the network nodes, SIFT does not send any kind ofcontrol message, solving the problem of control overhead.Hence, this routing strategy makes SIFT to become a verysuitable forwarding protocol for VANETS.

Research in VANETS is mostly simulation-based due to thehigh cost of testing and evaluating routing protocol in real

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Proceedings of the 9th International Conference on Mobile and Wireless Communications Networks, Cork, Ireland, September 19-21, 2007

environments. The main difference between classical ad hocnetworks and VANETS is the way nodes move. Thereby,simulation-based evaluations in VANETS must make use ofappropriate realistic mobility models.

In this paper we present a performance comparison studybetween SIFT, a scalable, spatial-aware, TBF approach, andDREAM, a stable, largely tested PB routing scheme. Theperformance was evaluated within a real-world urbanscenario, deploying up to 1000 nodes that move according toSSM (Stop Sing Model) [12], a realistic mobility pattern forVANETS.

The remainder of this article is organized as follows. Insection II we describe the operating scheme of DREAM andSIFT. Section ill exposes the mobility model used for thisstudy. The performance comparison results are analysed insection IV. We conclude this work in section V.

II. SIFT AND DREAM DESCRIPTON

In this section we give a brief description of the protocolsevaluated, SIFT and DREAM.

A. DREAM

DREAM is a directional, restricted flooding, PB routingapproach. It makes use of several techniques to reduce controloverhead. DREAM implements 2 algorithms: one todisseminate location information packets, and another one todisseminate data packets. The first one is based on a restrictedflooding scheme. Each node, periodically, sends locationpackets to update the position tables of the other nodes. Nodesrestrict this flooding introducing a travel distance threshold,that is, the maximum distance that a location packet willreach. This flood is also restricted by means of controlling thefrequency at which nodes send location updates. Thisfrequency is proportional to nodes mobility rate. Thealgorithm used to disseminate data packets is directionalflooding. When node S wants to send a data packet to node D,it checks its location table to find D's position. Based on thisinformation, S selects from its neighbours those nodes that arein the direction ofD and forwards the packet to them. Each ofthese nodes, in turn, do the same, forwarding the message tothose nodes in the direction ofD until D, is reached.

The comparison study carried out in [3] states that DREAMcould find a route to a given destination in 80% of the times.End-to-end delay of DSR is between 25% and 250% longerthan in DREAM.

B. SIFT

SIFT is a new scalable, spatial-aware, TBF approach.Differently from previously proposed TBF schemes, SIFTuses broadcast instead of point-to-point transmissions.Wireless transmissions are broadcast in nature and allowreaching possibly all active neighbours at the same time.Moreover, forwarding decisions are shifted from thetransmitter to the receiver. Each node that receives a packettakes the decision to forward it or not based only on its ownposition, the last transmitter position and the trajectory. This

reduces control overhead down to 0, that is, SIFT sends nocontrol packet. Once received a packet, each node sets a timeraccording to its position with respect to the trajectory and thelast transmitter. The closer to the trajectory and the fartherfrom the last hop a node is positioned, the shorter the timer isset. If a copy of the same packet, forwarded by another node,is received before the timer expires, the timer is stopped andthe packet is dropped. Otherwise, the packet is transmittedwhen the timer expires. Therefore, the node with the shortesttimer will forward the packet. Packets include into the headerthe trajectory and the coordinates of the last node thatforwarded the packet. Trajectories can be obtained fromdigital maps. Since intermediate nodes get all the requiredrouting information from the packet header, they do not needto know anything about its neighbours; hence, they exchangeno control packets. This issue is very interesting in highlydynamic environments.

III. MOBILITY MODEL DESCRIPTION

Research in VANETS is mostly simulation-based due to thehigh cost of testing and evaluating protocol implementationsin real environments. The main difference between classicalad hoc networks and VANETS is the way nodes move.Thereby, the simulation-based evaluations of routingprotocols for VANETS must make use of appropriate realisticmobility models, in such a way that evaluation results arecoherent with the performance that those protocols wouldhave if they were evaluated in a real-world VANET scenario.The most popular simulation environments for ad hocnetworks are generic discrete-event simulators that weredesigned for modelling generic communication networks.These tools provide also generic mobility models that do notaddress the special motion features of VANETS.

In these simulation tools, nodes commonly are placedrandom and uniformly within the simulation field, and nodesmove according to a certain kind of random mobility model.Random Waypoint, Manhattan Grid, Purse or Reference PointGroup are some examples of commonly used, genericmobility models [11]. In models like Random Waypoint, eachnode randomly selects a waypoint in the simulation area andmoves from its current location to the waypoint with a randomand constant speed. Once a node has reached the waypoint, itpauses for a random amount of time before selecting a newwaypoint. However, in VANETS, nodes move according tothe following facts: A) nodes are not random and unifonnlydistributed, they are placed according to roads layout. B)Nodes do not move according to a random trajectory, nodesroutes are built on roads layout. C) Nodes speed is not randomand constant; speed is variable and depends on trafficconditions, roads layout and traffic rules. Random movementpatterns have no similarity to the behaviour of vehiclemovements in real-world scenarios. This type of mobilitymodels is not appropriate to research in VANETS.

To perform a reliable study, we made use of a simplemobility model for VANETS that addresses movement

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Proceedings of the 9th International Conference on Mobile and Wireless Communications Networks, Cork, Ireland, September 19-21, 2007

patterns of vehicles on an urban scenario. It is based on SSM[12]. According to this model, initial and destinations nodesposition are chosen randomly according to the streets layoutof a given city map. The simulation field map represents asimple grid-shaped urban scenario. Streets are placed creatinga non-uniform grid since streets are not equidistant. Weassume that streets have 2 lanes, one for each direction, andthat vehicles are not allowed to pass each other. Thus, vehiclesmovement depends on the preceding vehicle movement. Somecrossroads has a stop sign; when a vehicle approaches ajunction with a stop sign, it must stop for a period of time,which is proportional to the number of vehicles that aremoving near that junction. This way, the more crowded a zoneis, the longer it takes to pass through it. Each node follows theshortest path lying on the street topology towards itsdestination and, once it is reached, nodes start again theprocedure choosing randomly another destination. Sincenodes speed is dependent on traffic conditions, only maximumspeed limit can be established for each vehicle.

IV. PERFORMANCE COMPARISON

This section presents the simulation results carried out withthe purpose of analysing the performance of DREAM andSIFT. We studied the effects of critical factors such as: 1)nodes density and 2) distance between source and destinationnodes. The performance was compared through the followingparameters: a) delivery ratio, b) end-to-end delay, c) routelength in terms ofnumber ofhops and d) control overhead.

A. Simulation scenario

The simulation scenario was implemented with Omnet++Simulation Engine and its Mobility Framework [18]. Networknodes are distributed within the simulation area according tothe mobility model described in section III. The simulationarea is a square of 1000m x 1000m. The simulation map is asimple lOx lOnon-uniform street grid. All the nodes areprovided with an IEEE 802.11b communications interface,and all they operate with a radio range of 100m. In all thesimulations, the source and the destination nodes do not move.The source node sends 1 message per second to thedestination node. The simulation lasts 2 hours.

B. Simulation results

1) Network densityThrough the frrst simulations we have studied the impact of

nodes density on network performance. The source node wasplaced at point Po(0,300), according to the Cartesiancoordinate system of the simulation area, and the destinationnode was fixed at point Pd(200,500). For the rest of the nodesthe maximum speed limit was set up at 12m/s. Networkdensity is oscillating, from 100 to 1000 nodes.

Many studies [7], [16] assert that network density isdecisive on routing performance. This way, the higher thedensity is, the higher the network connectivity is, and thence,the more packets reach the destination. Based on this premise,Figure 1 shows that the delivery ratio of SIFT improves in

accordance with network density. DREAM delivery ratio alsoimproves in terms of network density; however, in this case,performance decreases when the number of nodes deployed inthe network exceeds 500 devices. If network density is toohigh, DREAM does not perform well because nodes send toomany control packets that get the channel overloaded and,thus, many transmission errors occur, decreasing deliveryratio.

Generally, VANETS protocol performance within realisticmobility models is lower than the results showed by otherstudy [16] based on non-realistic models. This study affirmsthat, under a certain value of density, the delivery ratio ofSIFT and DREAM could reach 100%; however, for the samevalue of density, and making use of a realistic mobility modelfor VANETS, the delivery ratio in SIFT does not exceeds40% and in DREAM this parameter does not exceeds 4%, asFigure 1 shows. This low performance is due to the fact that,in VANETS, protocol performance does not only depend onnetwork density, but also on nodes distribution. Genericmobility models often assume that nodes are uniformlydistributed. But in VANETS that assumption is not correctsince distribution is restricted to roads topology. With a non­uniform allocation, the probability of finding a low­connectivity zone between source and destination nodes ishigher that with a uniform distribution; thus, delivery ratiodecreases in a realistic environment. SIFT delivers morepackets than DREAM in any case under a realistic scenariobecause SIFT is a spatial-aware protocol as it gets the routingtrajectories from digital maps, while DREAM uses a simplegreedy technique. Nodes distribution is a key factor; so that,knowing that nodes are distributed according to streets layout,a routing protocol aware of that layout will be more efficientas it will be able to avoid low-connectivity regions. On theother hand, DREAM is not aware of road topology and ithappens often that there is a low-connectivity zone betweensource and destination nodes. Consequently, we obtain lowdelivery ratio in DREAM, as showed in Figure 1.

Figure 2 shows that DREAM is not capable of deliveringany data packet when the number of nodes is lower than 400;the delay is infinite as no packet is delivered. SIFT delaydecreases when density increases; the higher density, thebetter positioned is the next hop node with respect to the givenrouting trajectory, and therefore, the shorter its timeout lasts.This way, SIFT delay decreases when density increases. Witha high-deployed network, DREAM delivery delay is very highdue to the location-information dissemination procedure. Thisprocess generates too much control traffic, which overloadsthe channel. In an overloaded network, collisions occur, andpackets must be retransmitted. However, if density is not veryhigh, DREAM does not overload the channel and gets lowerdelay than SIFT, which is timer-based. Thus, in low-deployedbut high enough connected networks, DREAM performsbetter than SIFT regarding delivery delay.

Figure 3 shows route length in terms of network density. Inthis chart, the number of hops is represented with an infinitevalue when packets do not reach the destination. When the

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Proceedings of the 9th International Conference on Mobile and Wireless Communications Networks, Cork, Ireland, September 19-21, 2007

2Ot-------------------------i

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Figure 2. End-to-end delay as function of density.

2) DistanceThe second simulations group performed in this work

attempted to evaluate the effects of the distance betweensource and destination nodes, that is, how performance variesin tenns of the distance that a packet must travel towards itstarget. For these simulations, 437 nodes were distributedwithin the simulation area. The source node was alwaysplaced at point Po(0,300), and the destination node was placedin several positions, in such a way that distance betweensource and destination nodes oscillates from 100 to 1100m.For the rest of the nodes the maximum speed limit was set upat 12m/s getting an average speed of4.7m/s.

Figure 5 shows that delivery ratio decreases in accordancewith distance; the more distant the source and destinationnodes are, the less data packets are delivered. This result isdue to the fact that the probability of finding a low­connectivity zone in the network is proportional to the numberof intermediate hops that forward a packet. As showed inFigure 3, the number of intermediate hops is always lower inSIFT than in DREAM; thus, SIFT can deliver more packetsthan DREAM.

In Figure 6 we can observe that SIFT delivery delayincreases when destination node moves away. This is relatedto the fact showed in Figure 5; delay is proportional to thenumber of intermediate hops, and the number of hops isproportional to the travelled distance towards the destination.DREAM delivery delay is shorter than in SIFT when distanceis short, up to 400m. Within this limit, DREAM perfonnsbetter that SIFT because SIFT is timer-based and DREAM istable-driven. However, when distance exceeds that threshold,DREAM finds more low-connectivity zones between sourceand destination nodes, and packets do not reach thedestination. It is represented in Figure 6 with an infinite delayvalue. The number of low-connectivity zones between sourceand destination is the same for both protocols since both theyoperate under the same network; however SIFT performsbetter that DREAM because it can define an appropriaterouting trajectory to avoid low-connectivity regions.

Figure 7 shows that the number of intermediate hops that adata message passes through is increased proportionally to thedistance that the data packet must travel. In this chart, thenumber of hops is represented with an infinite value whenpackets do not reach the destination. In our scenario it occurswith DREAM when the distance between source anddestination nodes is larger than 400m. SIFT performs betterthat DREAM due to the same reasons already explained inFigure 3.

Figure 8 points out the same situation showed in Figure 4with respect to the control overhead. Similar conclusions canbe affirmed. With respect to DREAM, in this case, densityremains constant, but the larger the distance is, the more nodesforward location packets generated by the locationinformation dissemination procedure of DREAM. So that,control overhead is also proportional to distance betweensource and destination nodes.

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number of nodes is lower than 400, DREAM can deliver nopacket. SIFT chooses, at each intermediate step, the node thatis more distant from the last node that forwarded the packet.Thus, in SIFT, the number of hops depends on transition radiorange, but not on density. So that, SIFT delivery delay isconstant, lightly decreased, when density increases becausethe more nodes there are in the network, the better positionedthey are regarding the given trajectory, and the shorter theintermediate timeouts are. However, DREAM hops numberdepends on density since all the nodes that are within therouting cone forward the given packet; hence, the number ofintermediate hops is proportional to the number of total nodesin the network.

Figure 4 represents how control overhead changes in tennsof network density. SIFT exchanges no control messages;therefore, control overhead is always o. However, DREAMcontrol overhead is proportional to density: the more nodes,the more control packets are exchanged.

DeIlveryRlltio

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Proceedings of the 9th International Conference on Mobile and Wireless Communications Networks, Cork, Ireland, September 19-21, 2007

Overhead

Delivery Delay

not uniform but constrained to roads layout. This distributiongenerates low-connectivity zones within the network. Anefficient routing protocol for VANETS must be able to avoidrouting packets through those regions. TBF approaches seemto be an efficient solution to handle low-connectivity zonesdrawbacks, as trajectories provide spatial-awareness. Classicalrouting strategies like simple greedy, commonly used bylocation-based protocols, do not perform properly since theyare not spatial-aware. In our future work, we will analyse theimpact of vehicles speed in TBF and PB protocols, comparingthem to dissemination protocol specifically designed forVANETS environments.

REFERENCES

[1] E. Royer and C. Tob, "A Review of Current Routing Protocols for AdHoc Mobile Wireless Networks", in IEEE Personal Communications,April 1999.

[2] M. Mauve, 1. Widmer and H. Hartenstein, "A Survey on Position-BasedRouting, in Mobile Ad-Hoc Networks", in IEEE Network Magazine,Nov. 2001.

[3] S. Basagni, I. Chlamtac, V. Syrotiuk, and B. Woodward, "A DistanceRouting Effect Algorithm for Mobility (DREAM)", in ACM/IEEEMOBICOM,1998.

[4] B. Karp and H.T. Kung, "GPSR: Greedy Perimeter Stateless Routing forWireless Networks", in ACM-IEEE MOBICOM, 2000.

[5] D. Niculescu and B. Nath, "Trajectory Based and Its Application", inproc. Of ACM Mobicom '03, 2003.

[6] B.Nath and D. Niculescu, "Routing on a curve", in ACM SIGCOMMComputer Communication Review, vol. 33, no. 1, January 2003.

[7] H. FuBler, M. Kasemann, and D. Vollmer, "A Comparison of Strategiesfor Vehicular Ad-Hoc Networks", in Dept. of Compo Sc., Univ. ofMannheim, Tech. Rep. TR-3-2002, 2002.

[8] C. E. Perkins and P. Bhagwat, "Highly Dynamic Destination-SequencedDistance-Vector Routing (DSDV) for Mobile Computers", inSIGCOMM: Computer Communications Review, 24(4), 234-244,October 1994.

[9] C. E. Perkins and E. M. Royer, "Ad Hoc On-Demand Distance VectorProtocol", in C. E. PERKINS (Ed), Ad Hoc Networking, pp. 173-219.Addison-Wesley, 2000.

[10] Y. Ko, and N. H. Vaidya, "Location-Aided Routing (LAR) in Mobile AdHoc Networks", in MOBICOM, 1998.

[11] T. Camp, 1. Boleng, and V. Davies. "A survey of mobility models for adhoc network research" in Wireless Communications & MobileComputing, 2(5):483--502, 2002.

[12] A. Mahajan, N. Potnis, K. Gopalan, and A. Wang, "Evaluation ofmobility models for vehicular ad-hoc network simulations", TechnicalReport TR-051220 Florida State University, 2005.

[13] D. B. Johnson and D. A. Maltz, "Dynamic Source Routing in Ad-HocWireless Networks", in Mobile Computing, 1996.

[14] T. Clausen and P. Jacquet. "Optimized Link State Routing Protocol(OLSR)", in. RFC 3626, IETF Network Working Group, October 2003.

[15] A. Capone, M. Garcia de la Fuente, L. Pizziniaco and I. Filippini, "SIFT:An efficient method for Trajectory Based Forwarding", in IEEEInternational Symposium on Wireless Communications Systems(ISWCS'05), September 2005.

[16] M. Garcia de la Fuente and H. Labiod, "A Perfonnance Comparison ofPosition-Based Routing Approaches for Mobile Ad Hoc Networks",submitted to IEEE Vehicular Technology Conference VTC-Fall, 2007.

[17] M. Yuksel, R Pradhan and S. Kalyanaraman, "An ImplementationFramework for Trajectory-Based Forwarding in Ad-Hoc Networks", inIEEE ICC'04, June 2004.

[18] OMNET++ Community Site - http://www.omnetpp.orgl

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V. CONCLUSION AND FUTURE WORK

Ad hoc networks literature highlights that control overheadis the most critical issue that must be faced. Classical ad hocrouting schemes do not perform well in VANETS becausethey need to send too much control packets, getting thechannel overloaded.

PB methods reduce appreciably control overhead since theyuse more efficient routing techniques, like restricted ordirectional flooding. However, simulations show that, inVANETS, those techniques may not be efficient enough. SIFTseems to be more efficient for highly dynamic environmentsbecause it solves the control overhead problem. SIFT,performs better than simple location-based protocolsconcerning delivery ratio, delivery delay, control overheadand route length.

The other main difficulty than routing protocols forVANETS should address is the fact that nodes distribution is

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Figure 5. Delivery ratio as function of distance.

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