Modeling VANET Deployment in Urban Settings

Atulya Mahajana, Niranjan Potnisb, Kartik Gopalanc, Andy Wangb

aLehman Brothers, New York, NY, USAbComputer Science, Florida State University, Tallahassee, FL, USA

cComputer Science, State University of New York at Binghamton, Binghamton, NY, USAContact: kartik@cs.binghamton.edu, awang@cs.fsu.edu

ABSTRACTThe growing interest in wireless Vehicular Ad Hoc Networks(VANETs) has prompted greater research into simulationmodels that better reflect urban VANET deployments. Still,we lack a systematic understanding of the required level ofsimulation details in modeling various real-world urban con-straints. In this work, we developed a series of simulationmodels that account for street layout, traffic rules, multi-lane roads, acceleration-deceleration, and RF attenuationdue to obstacles. Using real and controlled synthetic maps,we evaluated the sensitivity of the simulation results towardthese details. Our results indicate that the delivery ratio andpacket delays in VANETs are more sensitive to the cluster-ing effect of vehicles at intersections and their acceleration-deceleration. The VANET performance appears to be onlymarginally affected by the simulation of multiple lanes andcareful synchronization at traffic signals. We also found thatthe performance in dense VANETs improves significantlywhen routing decisions are limited to a wireless backbone ofmesh nodes, whereas in sparse VANETs, performance im-proves when vehicles also participate in ad hoc routing. Fi-nally, through measurement and analysis of signal strengthsaround urban city blocks, we show that the effect of signalattenuation due to physical obstacles can potentially be pa-rameterized in simulations. Our work provides a startingpoint for further understanding and development of moreaccurate VANET simulation models.Categories and Subject Descriptors: C.2, I.6General Terms: Measurement, Performance

1. INTRODUCTIONWireless vehicular ad hoc networks (VANETs) consist of

vehicles traveling on urban streets, capable of communi-cating with each other with/without the aid of a fixed in-frastructure. VANETs have generated considerable researchand commercial interest with promising applications, suchas mobile communication, traffic monitoring, safety, andpublic utility management. The current simulation modelsused in popular wireless simulators such as NS-2 [15] tend

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to ignore real-world constraints. For example, the widelyused Random-Waypoint Model (RWM) [13] assumes thatthe nodes move in an open field without obstructions and ig-nores important factors such as street layouts, intersectionswith traffic signs, or inter-vehicle interactions. Similarly, thecommonly used two-ray ground radio propagation model ig-nores effects such as RF attenuation due to buildings andother obstacles. Consequently, the simulation results are un-likely to reflect the protocol performance in the real world.In addition, a wide spectrum of VANET deployment scenar-ios are possible ranging from pure vehicle-to-vehicle (V2V)communication, routing through a fixed wireless mesh net-work (WMN) backbone, or even a hybrid deployment ofV2V and mesh-based routing. These range of deploymentpossibilities are not adequately captured by current models.

To address these limitations, researchers have become in-terested in modeling ’realistic’ VANET deployments [7, 19,22, 4, 10, 8, 24, 3, 12, 9]. Although these studies capturedifferent levels of simulation details and realism, existingresearch has shed little light on the actual level of detailsrequired and the sensitivity of simulation results to thosedetails. Excessive details may prolong the running time of asimulation with little or no impact on the results, while toofew details invariably lead to inaccurate results.

This paper addresses the following question: what is thesensitivity of VANET simulation results toward individualmodeling details? We aim to identify factors that signifi-cantly affect the VANET performance, and also factors thatmarginally affect the performance and could potentially beignored. We developed a series of simulation models thatsystematically capture various urban constraints at increas-ing levels of detail. Our purpose is not to advocate onemodel over another, but to use them as a means to gainbetter insights into the sensitivity of simulations results todifferent levels of realism.

• Our results indicate that the clustering effect of vehi-cles at intersections significantly affects the VANETperformance. Increasing either the wait times at theintersections or the number of nodes lead to increasedclustering. Consequently, increased clustering can pro-duce different results depending upon whether the neigh-boring intersections are within or beyond each other’stransmission range, the former leading to higher deliv-ery ratios than the latter.

• We also find that VANET performance is sensitive tophysical topology (block sizes and street layouts) andthe acceleration and deceleration of vehicles.

• On the other hand, we find that adding additional

complexity to the models, such as simulating multiplelanes and the synchronization of traffic lights, yieldsmarginal impact on VANET performance.

• Comparing two mesh-enhanced VANET deploymentstrategies, we found that the delivery ratio in denseVANETs improves when routing decisions are limitedto a wireless backbone of mesh nodes, whereas, insparse VANETs, the performance improves when ve-hicles also participate in routing.

• We performed empirical signal strength measurementsaround urban city blocks and analyzed the data toshow that the effect of signal attenuation due to obsta-cles can potentially be parameterized in simulations.

We now describe a series of enhancements to VANET sim-ulation models, each successive model capturing increasinglevels of detail.

2. STOP SIGN MODELIn the Stop Sign Model, every street at an intersection has

a stop sign. Any vehicle approaching the intersection muststop at the signal for a specified time, which is configurable.On the road, each vehicle’s motion is constrained by thevehicle in front of it. That is – a vehicle cannot move furtherthan the vehicle in front of it, unless it is a multi-lane roadand the vehicles are allowed to overtake each other. Whenvehicles follow each other to a stop sign, they form a per-street queue at the intersection. Each vehicle waits for atleast the required wait time once it gets to the head of theintersection after other vehicles ahead in the queue clearup. Vehicle crossings at the intersection are not coordinatedamong different directions. Although in practice, an urbanlayout is unlikely to have stop signs at every intersection,this model does serve as a useful first step to understandthe effect of mobility dynamics on VANET performance.

3. PROBABILISTIC TRAFFIC SIGN MODELNext, we refined SSM by replacing stop signs with traf-

fic signals. Although it is possible to simulate the detailedcoordination of traffic lights from various directions, we didnot do so at this stage. The rationale was to understandwhether such levels of detail would produce a significantimpact on routing performance. As an intermediate step,we developed the Probabilistic Traffic Sign Model (PTSM),which approximates the operation of traffic signs by not co-ordinating among different directions. When a node reachesan intersection with an empty queue, it stops at the signalwith a probability p and crosses the signal with a probabil-ity (1− p). If it decides to wait, the amount of wait time israndomly chosen between 0 and w seconds. Any node thatarrives later at a non-empty queue will have to wait for theremaining wait time of the previous node plus the startup de-lay between queued cars. Whenever the signal turns green,the vehicles begin to cross the signal at the delay interval,until the queue becomes empty. The next vehicle to arriveat the head of an empty queue again makes a decision tostop with a probability p and so on. Like SSM, there is nocoordination among vehicles crossing an intersection fromdifferent directions. This model reduces excessive stopping,while approximating the behavior of traffic lights.

4. TRAFFIC LIGHT MODELSSM and PTSM coarsely model vehicular mobility. In or-

der to understand which other level of detail besides street

topology is absolutely essential, we further refined PTSMwith incremental levels of mobility details. We call thismodel and its variants, the Traffic Light Model (TLM).

Coordinated traffic lights: Under TLM, traffic lightsat each intersection are coordinated. First, consider an in-tersection with an even number of roads with single-laneopposing traffic. The lights turn green for one pair of oppos-ing sides at a time to cross the intersection simultaneously,while the remaining lights stay red. Vehicles that need toturn follow the free turn rule once they reach the head ofthe queue. After a fixed period, green signals are rotated toanother pair of roads with opposing traffic. The case of anodd number of roads meeting at an intersection (e.g., a Tintersection) is modeled by permitting one of the roads toperiodically have a green light by itself.

Acceleration and Deceleration: The next level of de-tail we added to TLM was the acceleration and decelerationof vehicles. With this feature, vehicles do not change theirstate between rest and the top speed abruptly. Instead, theyaccelerate gradually from rest up to the maximum speed.Similarly, when approaching a stop sign or red light, theydecelerate gradually to a stop.

Multiple Lanes: Another feature of the TLM is the in-troduction of multiple lanes. For real maps, the number oflanes can be determined by the type of the road specifiedin the street database. When a vehicle enters a new road,e.g. when crossing or turning at an intersection, it selectsthe lane with the least number of vehicles.

Generating Variants of TLM: To study the sensitivityof VANET performance to mobility details, various featuresof TLM can be independently enabled or disabled to obtaindifferent TLM variants. In particular, four variants of TLMcan be obtained by enabling or disabling the acceleration-deceleration and multi-lane features. Hence, the basic TLMwith neither features still has one enhancement over PTSM,namely the coordinated traffic lights.

5. MESH-ENHANCED DEPLOYMENTWe also implemented two alternative VANET deployment

models in urban settings, where a wireless multi-hop net-work of stationary mesh nodes enables or supplements thenetwork connectivity among mobile nodes. Mesh nodes canbe strategically positioned at a subset of street intersec-tions. Such a wireless backbone of mesh nodes can po-tentially reduce churn in network connectivity and increaseroute stability when the static mesh nodes participate inthe ad hoc routing protocol. We implemented and evalu-ated two deployment scenarios: (1) mesh-enhanced peer-to-peer routing (MEPPR) where both the mobile nodesand static mesh nodes participate in routing, and (2) mesh-enhanced infrastructural routing (MEIR) where onlythe static mesh nodes route packets generated by the mo-bile nodes. For both MEPPR and MEIR, the TLM mobilitypattern generator was altered to designate a subset of nodesas mesh nodes that are positioned at street intersections andremain stationary throughout the simulation.

6. MODELING OBSTACLESBesides confining the vehicular movements to streets, phys-

ical obstacles also affect radio signal propagation through at-tenuation, reflection, diffraction, and refraction. This is inaddition to the free-space attenuation of radio signals withdistance. Since a receiver needs a minimum signal-to-noise

Figure 1: Signal strengths (dBm) around a down-town Tallahassee block. AP is the access point.

ratio to receive data, accounting for the obstacles is impor-tant when evaluating VANETs through simulations.

Traditional analytical models have limitations in captur-ing complex real-world factors that influence radio signalstrengths. Our approach is to use empirically measured datafrom real urban settings to capture the impact of differentfactors on radio signals in a few simulation parameters.

We measured the signal strength variation from a com-modity access point around two city blocks in downtownTallahassee – including a 100m x 100m block with severalthree-story buildings and a 200m x 50m block with one-story buildings. We placed an 802.11b Linksys wireless ac-cess point at a corner of the block being measured. Wethen used the Wavemon [23] tool running on a Linux laptopequipped with a wireless PCI card to take signal strengthmeasurements at various locations around the block. Theempirical data were composed of the distances from the ac-cess point and the associated signal strength A logarithmictransformation was performed on collected distances beforea linear regression was applied on the signal strength S (indecibels/milliwatts or dBm), as a function of distance d (inmeters) [11]. Logarithmic linear regressions yielded the fol-lowing formulas, with R2 (coefficient of determination) of0.6836 and 0.9698, indicating that 68% and 97% of the vari-ances in data are explained by these equations respectively.

Block1 : S = −25.809− 29.773 ∗ log(d) (1)

Block2 : S = −20.089− 33.012 ∗ log(d) (2)

From the structure of Equations 1 and 2, we can derive asimplified parameterization of the received signal strength.

Pr = Pt + A−B log(d) (3)

Pr and Pt are the signal strengths (in dBm) at the receiverand the sender respectively; d is the distance between thetwo in meters, and A and B are tunable parameters (whosesignificance we will explore below). Intriguingly, the prop-agation models used in NS2 can also be represented in theform of Equation 3. The NS2 propagation models are theFriis model for free space and the two-ray ground model thataccounts for multipath reflection from the ground.

Friis : Pr(d) =PtGtGrλ

2

(4π)2d2L(4)

Two ray ground : Pr(d) =PtGtGrh

2t h

2r

d4L(5)

Pr(d) is the received power (watts) at distance d, Pt is thetransmit power (watts), Gt and Gr are antenna gains fortransmitter and receiver respectively, ht and hr are the an-tenna heights for the transmitter and the receiver respec-tively, L is the system loss, and λ is the wavelength. Both

Equations 4 and 5 could be represented in the form of Equa-tion 3, after converting watts into dBm. The default valuesof A and B in NS2 for the Friis model are A = −31 andB = 20; for the two-ray ground model, A = 7.5 and B = 40.

Assuming that the received signal strength largely de-pends on the presence of obstacles and the distance fromthe sender, we can interpret A and B as follows. ParameterA captures the constant factor reduction in signal strengthdue to the presence of obstacles in a particular terrain. Pa-rameter B captures the order of magnitude reduction in thesignal strength with the distance from sender, the order ofmagnitude being determined by nature of the obstacles. Wewill refer to A as the constant factor and B as the distancefactor in the remainder of the paper.

Of course, the actual values of A and B would be quitedifferent for various urban settings, and even across differentregions within a single urban setting. Regardless, these twoparameters give us convenient knobs to capture and explorethe effect of obstacles in VANET simulations.

7. IMPLEMENTATIONThe mobility models are implemented as independent C++

programs that output mobility files, which serve as input tothe NS2 simulations. The initial vehicle positions and theirdestinations are uniformly random. Each vehicle follows theshortest path to its destination. Upon reaching a destina-tion, the vehicle begins its journey to another random des-tination. Each model takes a time parameter (in seconds).For SSM, this parameter denotes the duration each vehiclestops at intersections; for PTSM it denotes the maximumduration that each vehicle stops at the head of empty in-tersections; for TLM, it denotes the green light duration foreach opposing pair of roads at an intersection. PTSM andTLM have a 1s vehicle startup delay.

The street topology is specified in a file that stores theroad identifiers and the starting and ending road coordi-nates. All roads are modeled as bidirectional roads. SSMand PTSM assume a single lane in each direction of everyroad, whereas TLM provides the option for modeling multi-ple lanes. In SSM and PTSM, vehicles always travel within5 miles/hour of the street speed limit. TLM has a slightlydifferent mechanism with vehicles accelerating from rest toreach the speed limit, and then decelerating to stops. Theacceleration and deceleration rates were 3 meter/second2.

Implementations of MEPPR, MEIR, and obstacle mod-els used the TLM mobility model. MEPPR and MEIR in-volved enhancing the NS2 simulator. The identity of themesh nodes and their positions are specified from a separatefile. Mesh nodes were positioned at a subset of street inter-sections chosen using the uniform random distribution. Theimplementation of MEPPR reuses AODV’s implementationin NS2, which allows every node to participate in routingdecisions. However, the MEIR deployment required changesin AODV to ensure that only the mesh nodes participate inrouting and forwarding, whereas mobile nodes act as sourcesand destinations. For modeling obstacles, NS2’s radio prop-agation model is modified according to Equation 3, such thatparameters A and B can be configured.

8. PERFORMANCE EVALUATIONSTable 1 summarizes the default values of parameters using

NS-2 simulator [15]. We compared SSM, PTSM, TLM, theRandom Waypoint Model (RWM) [13] and the Rice Uni-versity Model (RUM) [19]. RWM captures mobility in an

Parameter Default Value(s)Simulation Time 900s (plus 450s warmup)Routing Protocol AODVTransmit Range 250m

Number of Nodes 100CBR Sources 4 pkts/sec and 64 byte pkt

Mobility Models RWM, RUM, SSM, PTSM, TLMTopologies 1200 × 1200m Grid, Real Map

Max. Wait Time SSM–3s, PTSM–30s(p = 0.5), TLM–30sMax. Node Speed 35 mph

Accel./Decel. 3 meters/sec2 for TLM

Table 1: NS2 Wireless Simulation Parameters

open field with no obstacles, roads, or intersections. RUMsimulates roads in a real map, but vehicles do not stop at in-tersections. For controlled experiments, we varied the blocksizes in a grid topology over a 1200m × 1200m area. Wealso used real world street maps extracted from the US Cen-sus Bureau TIGER [21] database, which specifies road type,speed limit, and number of lanes. Each simulation run lasted900s, preceded by 450s warmup period. Each data point isaveraged over 5 to 10 different mobility patterns and nodeplacements to attain a 95% confidence interval.

8.1 Number of Mobile NodesThis section compares various mobility models with vary-

ing numbers of mobile nodes in a 1200m×1200m grid topol-ogy with a block size of 200m× 50m. Figures 2 and 3 com-pare the delivery ratio and end-to-end delay among all mo-bility models. SSM had a wait time of 3 seconds. PTSM hada maximum wait time of 30 seconds. TLM switched signalswith a periodicity of 30 seconds and used two lanes in eachdirection with acceleration-deceleration of vehicles enabled.

The results indicated that the RWM yields the lowest de-livery ratio and the maximum end-to-end delay, for this par-ticular topology. The range of performance variation acrossvarious models highlights our point regarding the impor-tance of fidelity of mobility models in VANET simulations.

We observe that the delivery ratio increases with the num-ber of nodes, up to 100 nodes, as the connectivity of the com-munication graph increases. Then the delivery ratio startsdecreasing as the number of nodes increases further. Thisbehavior is due to the increased channel contention as thelarge number of nodes leads to a flood of control messagesin the network. The end-to-end delay in Figure 3 displaysthe opposite trend: it first decreases as the number of nodesincreases, and then there is a sharp increase thereafter. Wealso experienced certain NS2 constraints as the number ofnodes increased. The simulation time became a concern be-cause we needed to explore a large parameter space. Also,the resource requirements of memory and storage (for out-put traces) became prohibitive. Additionally, with a largenumber of nodes, the confidence intervals of performancenumbers widen significantly, further requiring more repeti-tions to reduce the variance of the results. Unless specified,we used 100 nodes in the remaining evaluations.

To understand the sensitivity of various mobility features(e.g. multi-lane roads and acceleration-deceleration of ve-hicles), we repeated the same experiment on TLM, withcombinations of features enabled/disabled (Figure 4). Theresults indicate that acceleration-deceleration led to a sig-nificant increase in the delivery ratio because this featurereduces the average speed of vehicles. Thus, network routesare more stable. Additionally, the performance differencebetween the single-lane and multilane models is not notice-able below 100 nodes. However, with acceleration-decelerationdisabled, it becomes noticeable beyond 100 nodes as the

channel contention begins to rise. It is interesting to notethat, once the acceleration-deceleration is enabled, the dif-ference between the single-lane and multi-lane models be-comes negligible. At first glance, multiple lanes withoutacceleration and deceleration differ from single lanes with-out acceleration and deceleration. However, the confidenceinterval is rather wide. After checking the average vehi-cle speed (Figure 10), and the average percentage of nodesmoving at a given time (Figure 8), the two models appearto have the same clustering effects at intersections with carsmoving at a similar average speed. Therefore, we believethat the difference is largely within margins of statistical er-rors. Thus, with our experimental settings, the additionalcomplexity of modeling multiple lanes does not seem to sig-nificantly affect performance.

8.2 Maximum Speed and Wait TimesWe varied the maximum speed limit of vehicles in Figure 5

and observed that mobility models that place greater restric-tions on node movement yield higher delivery ratios. Onesuch restriction is the wait time at intersections. To furtherunderstand this effect, we varied the maximum wait timeof nodes at intersections (Figure 6). The results broughtout an interesting aspect of this study. As expected, theRUM model yields the lowest delivery ratio due to its highlydynamic pattern of mobility. However, in contrast to ourearlier experiments, SSM yields a higher delivery ratio com-pared to PTSM. The reason is that SSM results in a morestatic network than PTSM does, where nodes are forcedto stop at all intersections. On the other hand, PTSMnodes at intersections decide with a 50% probability to wait.The single-lane, no acceleration-deceleration TLM displaysa marginally lower delivery ratio than PTSM for the samewait time because coordinated traffic lights provide a slightlyhigher rate of churn compared to PTSM. However, the addi-tion of multiple lanes and acceleration-deceleration to TLMyields the highest delivery ratio among these models. Thisresult, combined with our earlier observation about negligi-ble impact of modeling multiple lanes, suggests that the in-troduction of acceleration-deceleration effectively slows downthe vehicle speeds most significantly and dampens the changesin the network topology. However, these results are also de-pendent upon other factors, such as block sizes, which wewill consider next.

8.3 Effect of Block SizesThe block sizes in the topology play an important role in

determining the performance of VANETs. With large blocksizes, vehicles spend more time in traversing between inter-sections; thus, nodes are mobile more often. This increasedmobility leads to a weakened connectivity in the network,and a corresponding drop in the delivery ratio. To vali-date this hypothesis, we conducted experiments with vary-ing block sizes in a 1200m × 1200m area. Figure 7 largelyconfirmed our hypothesis – as the block size increases, thedelivery ratio decreases. The RUM model is not sensitive toblock sizes, since nodes do not stop at intersections. Withthe largest evaluated block, SSM outperforms PTSM dueto a lower churn rate of routes, illustrating the interplaybetween block sizes and wait times in VANET simulations.

8.4 Analysis of Increased MobilityThe results of our experiments showed a distinct trend

between the performance of various mobility models -TLMresulted in the highest delivery ratios, and the performance

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Figure 2: Delivery ratio vs. numberof mobile nodes.

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Figure 4: Delivery ratio vs. numberof mobile nodes for TLM variants.

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Figure 5: Delivery ratio vs. maxi-mum speed of vehicles.

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Figure 7: Delivery ratio vs. increasein block size.

did not degrade considerably with an increase in the num-ber of mobile nodes; PTSM showed a higher delivery ra-tio than SSM, and the throughput obtained through useof these models was considerably higher than RUM. Thisbrings into context our hypothesis that varying the degreeof mobility (node speed) within these networks is the reasonfor differing performance. In SSM, each node is forced tostop at each intersection. On the other hand, PTSM nodesstop only at non-empty intersections and some of the emptyintersections. However, the default wait times for PTSMare higher as compared to SSM. This leads to a networkthat is effectively more static when compared to SSM, withbetter connectivity and corresponding performance improve-ments. TLM eliminates the probabilistic behavior of trafficlights and introduces acceleration and deceleration of vehi-cles, which leads to an even more stable network. To gain adetailed understanding, we identified metrics that measuredthe mobility of the nodes and the clustering of vehicles atintersections. The first metric provided us with a measureof the fraction of nodes we expected to actually be mobileat any given instant. The second metric was the extent ofclustering at intersections. The number of clusters of vehi-cles could be treated as an effective number of nodes in thenetwork, since all the nodes in a cluster displayed similarconnectivity to nodes outside the cluster. The third metricmeasured the average speed.Average Number of Mobile Nodes: To compute thismetric we determined the number of nodes that are notwaiting in a queue at any intersection. We took sampleseach second, averaged them over the simulated lifetime, andrepresented the result as a percentage of total nodes.

The first observation is that for the same wait time, vary-ing the number of nodes does not appear to affect the per-centage of mobile nodes significantly. This implies that thetopology and wait time are more influential to the percent-age of moving nodes compared to the number of nodes, upto 400 nodes within a 1200m × 1200m area. Under simi-lar conditions of wait time and topology, SSM is less mo-

bile when compared to PTSM as expected. The introduc-tion of acceleration-deceleration of vehicles to TLM increasesthe percentage of moving nodes in the network significantly,as slower average speeds reduce the chance of nodes beingqueued at intersections. To illustrate the effect of the waittime, we also evaluated both PTSM and TLM with a similarvalue of the wait time. The plots indicate that for the samewait time of 10 seconds, PTSM is more mobile than SSM,with PTSM having an average of 85% of the nodes movingat any time compared to 68% for SSM.Average Number of Clusters: Stopping of nodes at in-tersections effectively creates many clusters all over the net-work. Connectivity among the nodes within a cluster is nearperfect (minus the network contention effects). On the otherhand, if one node in the cluster cannot reach a distant nodeoutside the cluster, then most likely all nodes in the clus-ter are unable to reach the same distant node. The numberof such clusters can be treated as the effective number of(logical) nodes in the VANET at any time. Thus, we pos-tulate that clustering has an effect similar to decreasing thenumber of nodes in the network.

To estimate the number of effective nodes, we divided thetopology into 60m × 60m regions, counted the number ofregions containing at least a node each second, and took theaverage. Figure 9 shows that when the number of nodesincreases, the number of effective nodes grows sub-linearlyas more nodes are clustered at intersections. TLM resultedin a marginally greater number of effective nodes as com-pared to PTSM, for a similar wait duration of 30 seconds.This indicates a reduced clustering effect in TLM – a con-sequence of the reduced average speeds of the vehicles. Wealso studied the variation of this effect with the maximumwait time at intersections. With a wait time of 10 seconds,we observed that SSM with a wait time of 3 seconds resultedin a similar value as that of PTSM, which is consistent withour findings in Figure 8. Interestingly, we observed thatacceleration-deceleration and multiple lanes do not signifi-cantly impact the difference in clustering level between the

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Figure 8: Percentage of mobilenodes at a given time.

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Figure 9: Clustering Effect: De-creasing slope of plots indicates in-creased clustering.

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Figure 10: Average speed for variousmodels.

various versions of TLM. This indicates that the differenceacross TLM variants is mainly due to average speed.Average Speed of Vehicles: We computed the averagespeed for each vehicle as the ratio of the entire distance ittravels during the simulation and the simulated time (Fig-ure 10). We observed that PTSM results in lower averagespeeds compared to SSM, because of the longer wait timesinvolved at intersections. For TLM variants, the additionof acceleration-deceleration leads to a significant decrease inaverage speed, which translates into higher delivery ratios.Also observe that TLM with multiple lanes does not notice-ably affect the average speed compared to single lane.

8.5 Real Map ResultsHaving the insights into the various factors affecting VANET

performance in grid topologies, we conducted experimentsusing real maps extracted from the TIGER database. Weperformed a set of experiments using a smaller section ofthe map used by RUM [19]. The original map was 2400m×2400m, but the NS2 simulations at this size do not scaledue to the large number of nodes required (or conversely,one needs to set unrealistic transmission ranges) to main-tain meaningful delivery ratios. To address this problem,RUM [19] used a transmission range of 500 meters, whichwe considered to be too large for our settings. Hence, wedecided to maintain the original default NS-2 setting of 250meters transmission range, with a truncated map size of1200m× 1200m. In our experiments using this map, we ob-served that the delivery ratio for each model increased withthe number of nodes up to 100 nodes, followed by a rapiddegradation in performance thereafter. However, the per-formance using TLM remained constant up to almost 200nodes. These results reconfirm our understanding regardingthe correlation between topology and mobility, and betweenthe mobility and performance.

For another experiment, we extracted a map of Tallahas-see, over an area of 2000m × 2000m. The results in thiscase were different from what we had seen so far, owing toa much larger area as compared to the first map. In thisexperiment, we were able to observe the effect of networkpartitioning due to the large area and the initial low den-sity of nodes. This effect was also strengthened due to thestoppages enforced by our mobility models – once a node isin the waiting state at an intersection, it is highly likely tocommunicate with other nodes in other intersections due tothe large size of the map. The delivery ratios were initiallyvery low with a small number of nodes, and the performanceactually improved as the number of nodes was increased upto 200. This was in contrast to the results obtained withthe smaller map, where the performance went down with anincrease in the number of nodes, perceivably due to network

saturation. This reinforces our observation that these simu-lation results must be analyzed with the topology in mind.However, our basic understanding remains valid. The de-livery ratio with TLM still remains higher than that withPTSM, SSM, and RUM due to a lower network churn.

8.6 Mesh-Enhanced VANETIn this section, we try to understand the performance of

MEPPR and MEIR, without considering obstacles. Figure11 shows the effects of varying the number of mobile nodeson the delivery ratios and end-to-end delay in the MEPPRdeployment scenario. The two plots correspond to fixednumber of mesh nodes at 171 mesh nodes (one per inter-section) and 40 (approximately 23% of intersections). For171 mesh nodes, as the number of nodes participating in therouting process increases and the network becomes dense,the resulting channel contention increases. Consequently,the delivery ratio of MEPPR deployment degrades. On theother hand, with only 40 mesh nodes, the MEPPR deploy-ment maintains a high delivery ratio (and low end-to-enddelay) with the addition of mobile nodes. To rule out theperformance degradation due to only the total number ofmobile and static nodes, we also extended the number ofmobile nodes to 170 (not shown in the plot) and observedno significant performance degradation with only 40 meshnodes. These results confirm our hypothesis that the chan-nel access contention generated by the number of nodes par-ticipating in the routing process is an important factor inVANET’s performance.

Figure 12 show the effects of increasing the number ofmobile nodes on the delivery ratio under the MEIR deploy-ment. We again used 171 and 40 mesh nodes. The plotshows that the delivery ratio does not vary significantly foreither setting as the number of mobile nodes increases. Sincethe mobile nodes do not participate in the routing process(they are merely sources and sinks for data packets), andthe mesh nodes that participate in routing decisions are sta-tionary, the resulting routes are much more stable than inMEPPR routes. MEIR routes change only when the mobileendpoints move out of range of their immediate mesh node.

Interestingly, in the sparser case of 40 mesh nodes inMEIR, since the mobile nodes do not participate in the rout-ing process, the resulting network coverage and connectiv-ity is poorer than the case of 171 MEIR mesh nodes (inFigure 12) and 40 MEPPR mesh nodes (in Figure 11. Con-sequently, the delivery ratio is also lower (around 90%).

Figure 13 shows the effect of varying the number of sta-tionary mesh nodes, with fixed the number of mobile nodes(30 or 35). Since both mesh nodes and mobile nodes performrouting, a relatively small number of mobile nodes combined

with mesh nodes can achieve good routing coverage anddelivery ratio. On the other hand, too many mesh nodesseverely limit the number of mobile nodes due to channelcontention. This is seen in the case of 35 mobile nodes and171 mesh nodes in the above graphs.

Figure 14 shows how the number of mesh nodes affects thedelivery ratios with MEIR. Clearly, a sufficient number ofmesh nodes are needed to achieve good routing coverage anddelivery ratio. However, MEIR scales better when comparedto MEPPR because the routes are more stable, resulting infewer route breakages, route discovery, and recovery events.

Thus, in a dense network, where the total number of nodesis high, MEPPR deployment can lead to decreased perfor-mance as a result of channel contention. In this case, theMEIR deployment scenario is preferable. In addition, MEIRdeployment can scale better with increased network loads.On the other hand, in a sparse network with a smaller num-ber of nodes, MEPPR deployment provides better routingcoverage and higher connectivity.

8.7 Obstacle RepresentationFigure 15 shows how obstacle factor A in Equation 3 af-

fects network performance for both routing configurations.An increasingly negative value of obstacle factor A shouldlead to a decrease in signal strength at receivers and lowerperformance. This is observed when A < -35 for both rout-ing configurations. The default value of A in the NS2 prop-agation model is -31, which corresponds to a total absenceof obstacles. However, it is interesting to note that in theMEPPR deployment scenarios, when A > -15, performancedegrades. For such a negative value of obstacle factor A, thesignal strength at the receiver is high enough to cause un-wanted reception and interference among these receptions.This is not observed for MEIR because the static mesh nodesmaintain a fixed distance from one another throughout thesimulations. Figure 16 shows how the distance factor Bin Equation 3 affects network performance for both mesh-enhanced routing configurations. A more positive value ofdistance factor B should reduce signal strength at receiversand decrease performance. This is observed in cases of val-ues of B > 21 for both routing configurations. However,MEPPR deployment performs better as compared to MEIRdeployment. This is the result of the mobile nodes’ partici-pation in routing to enhance connectivity and coverage. Fora high value of distance factor, network connectivity is stillbetter in the MEPPR deployment as more nodes are reach-able through the mobile nodes. Thus, we see that obsta-cles could potentially be represented in network simulationthrough a few key parameters. MEPPR improves coverageand network performance in the presence of obstacles.

9. RELATED WORKTo date, studies in the fields of mobility modeling, obstacle

modeling, and mesh networks have been largely performed inisolation. To the best of our knowledge, our work is the firstattempt to synthesize and systematically evaluate the indi-vidual/combined effects of various factors on urban VANETdeployment. In contrast to earlier works, the focus of ourpaper is not to recommend any one model over another butto understand and evaluate the performance impact and sig-nificance of various factors on VANET simulations.

The most used mobility model in literature is the RandomWaypoint Model (RWM) [13]. Every node selects a randomdestination and speed and then moves to that destination

with the chosen speed, pauses, and then moves again to an-other random destination. Other similar open-field modelsinclude the Random Walk, Random Direction Model andthe Boundless Simulation Area Model [2]. Camp [2] ob-served that the spatial distribution of nodes in such modelsis toward the center of the simulation area. [1, 16] have at-tempted to improve RWM to make it more realistic, thoughnot within the context of VANETs. Bai [7] introduced theFreeway and Manhattan mobility models on roads specifiedthrough maps. A vehicle’s path from an intersection was de-cided using a fixed probability. The vehicles did not pause,stop, or queue up at intersections. Saha et al. [19] modeledmobility of vehicles on real street maps, obtained from theTIGER database [21]. Their model, which we call RUM inthis paper, does not enforce any traffic rules with the result-ing performance similar to RWM. We used RUM as one ofthe base cases for comparison. Choffnes and Bustamante [4]recently introduced a vehicular mobility model for urbanenvironments. With their simulators configured to generatedelivery ratios between 0.05 and 0.3, they observed that thenetwork performance in such a network was significantly dif-ferent from the RWM. Our evaluations confirmed their find-ings regarding the correlation between network performanceand the simulated topology. Additionally, our evaluationsin this paper used reasonable parameter settings to gener-ate delivery ratios over 90% that are within the usable range.Because the model was implemented in the SWANS simula-tor, it was difficult to evaluate it without significant portingeffort to NS2. [10] proposed several theoretical models likecity area, area zone, and unit street. [6] points out thatthese models lack specific details for actual node movementcalculations. Proprietary tools for modeling transportationsystems are also available [17, 24, 5], though their detailsare not fully understood.

While wireless mesh networks have been studied exten-sively in isolation, we evaluate their impact on urban VANETsusing realistic mobility models. Microsoft [20] has proposedself-organizing community mesh networks. RoofNet [18] pro-vides broadband Internet access to residential subscribers.Motorola has proposed mesh-based network solutions for in-telligent transport systems and communication [14].

The effect of obstacles on wireless networks is relativelylittle studied. [3] points out that commonly used radio prop-agation models for indoor MANET evaluations are highlyinaccurate and relative protocol performance varies highlydepending upon the model. [12] models a terrain by spec-ifying the shapes and sizes of obstacles. The effect of ob-stacles on signal propagation is determined by a static tablebased on the type of obstacle. [9] presented the principlesof a WCDMA radio network simulator that accounts forpath-loss, shadowing, and fast fading effects in radio signalpropagation. In contrast to earlier efforts, our work usesempirical measurements around urban city blocks to derivea parameterized model that can be tuned to study the effectof obstacles on radio signal propagation in VANETs.

10. CONCLUSIONSSimulation models play a critical role in the evaluation

of Vehicular Ad Hoc Networks (VANETs). In this paper,we have systematically evaluated the sensitivity of variousmodeling details on VANETs through step-by-step imple-mentation of details in the mobility, radio propagation, anddeployment models in urban settings. We proposed a series

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of simulation models that account for various urban con-straints such as street layout, traffic rules, multi-lane roads,acceleration-deceleration, and RF attenuation due to obsta-cles. Our evaluations, using both real and controlled syn-thetic maps, provide many interesting insights. VANET per-formance is sensitive to the clustering of vehicles at intersec-tions and acceleration-deceleration of vehicles. Simulationof multiple lanes and synchronization at traffic signals onlymarginally impact VANET performance. However, model-ing of multiple lanes might be fundamental to applicationssuch as Forward Collision Warning and Lane Change Assis-tance systems, which we do not consider. In dense VANETs,performance improves significantly when routing decisionsare limited to a wireless backbone of mesh nodes compared,whereas in sparse VANETs, performance improves when ve-hicles also participate in ad hoc routing. Finally, we mea-sured signal strengths around urban city blocks and showedthat the effect of signal attenuation due to obstacles canpotentially be parameterized via empirical real-world mea-surements. Our evaluations provide a starting point to un-derstand and develop better VANET simulation models.

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