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15MOBILITY MODELS, TOPOLOGY, ANDSIMULATIONS IN VANET

Francisco J. Ros, Juan A. Martinez, and Pedro M. Ruiz

ABSTRACT

Vehicular networks are likely to be the very first deployed large-scale instance ofmobile ad hoc networks. The design of reliable and adaptive protocols in vehicu-lar context is challenging, especially due to the high dynamicity of the underlyingtopology and its intermittent connectivity in most scenarios. Yet, the movement ofcars is constrained by the road structure, and this fact can be exploited to improvenetworking tasks. It is also expected that a partial infrastructure is still to be availableat some strategic places (e.g., at intersections inside cities) to improve the connecti-vity and provide dedicated services to drivers and passengers. This chapter discussessome aspects related to the modeling of roads and traffic. In particular, it reviews dif-ferent models and tools for the realistic simulation of vehicular networks, includingcurrent simulators employed in VANET research. Moreover, a connectivity analysisin a highway scenario is conducted.

15.1 INTRODUCTION AND MOTIVATION

Vehicular ad hoc networks (VANETs) have attracted the interest of most relevantplayers in the development of future Intelligent Transportation Systems (ITS). In fact,many of the envisioned services for the vehicular environment rely on the provision

Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition. Edited by Stefano Basagni,Marco Conti, Silvia Giordano, and Ivan Stojmenovic.© 2013 by The Institute of Electrical and Electronics Engineers, Inc. Published 2013 by John Wiley & Sons, Inc.

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546 MOBILITY MODELS, TOPOLOGY, AND SIMULATIONS IN VANET

of an effective communication platform among the vehicles themselves. Internationalstandardization bodies are pushing technical specifications for vehicular ad hoc com-munications, which are expected to be adopted by the industry. In such scenario,VANETs are likely to be the first real large-scale deployment of a mobile ad hoc net-work. VANETs offer a great number of possibilities for the development of vehicularservices. For instance, a safety service in a car that has been involved in an accidentcan take advantage of the ad hoc network to communicate this dangerous situationto nearby vehicles. In a similar way, a traffic management application can announcethat a given road is congested, so that incoming vehicles could take an alternativeroute. Many other services are to be developed by exploiting the ad hoc networkingparadigm. Among them, we can highlight location-based applications like toll-payservices and advertisement of petrol station prices.

In order to develop effective vehicular services, the particular characteristics ofthis mobile environment must be well understood. Luckily, this subject has beeninvestigated for a long time by companies and institutions interested in buildingefficient roads and highways, with the objective of improving driving quality andreducing traffic congestion. Such studies originated the development of different toolsrelated to traffic mobility.

Mobility models are one of these tools. Many of them are based on the idea of a carfollowing another and how it behaves depending on the distance to the leading one.In Section 15.2 we review some of the most relevant car following and multi-lanetraffic models.

We also review some mobility simulators in Section 15.3. They allow for thesimulation of vehicles moving throughout a given scenario. Hence, researches canevaluate the impact that new roads would have in a specific area, or gather relevantinformation on traffic density, average speed of the vehicles, occupancy degree ofeach lane, and the like.

Despite these powerful mobility simulators, VANET researchers are interested inobtaining results within the communication technology context. Many well-knownnetwork simulators (for instance, NS-2 or OMNet++, just to name a few) are ableto receive as input a trace file that describes the movement of a set of nodes along aperiod of time. However, the simulation of vehicular services has revealed new needsto be covered by the simulation tools. In particular, communications and vehiclemovements are not independent any more, since the former can influence the latter.For instance, when a crash occurs, a safety application can trigger the disseminationof a broadcast message that is received by nearby vehicles. When processing this typeof message, these vehicles must reduce their current speed (coming to a standstill ifneeded), preventing them from being involved in the accident. Traditional mobilitysimulators are not able to deal with these scenarios, since there is no coupling betweenthe communication and mobility simulators. Therefore, integrated simulators thataccount for these types of coupled behavior have been developed. Section 15.4 reviewssome of the most relevant ones.

Simulation is a very useful tool for the design of communication-based vehicularservices and protocols. However, the simulation model must be properly configuredin order to produce realistic results. One of the key parameters that must be taken

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into account when modeling a vehicular scenario is how the radio signal behaves insuch an environment. It is well known that simplistic assumptions in this regard canlead to wrong conclusions. Therefore, simulators for VANET research must includerealistic wireless signal propagation models, like the ones reviewed in Section 15.5.

In addition to fine-tuned signal propagation models, VANET simulations mustaccount for realistic movement patterns. Vehicles constrain their movement to the roadlayout, traffic signals, and other vehicles’ movement, among others. These featuresmake the network topology very dynamic, causing frequent network partitions andjoins. Section 15.6 studies the connectivity level that can be expected in a highway,as a function of the effective radio range that can be obtained with VANET interfacecards.

Finally, Section 15.7 concludes this chapter. We summarize the main features ofthe vehicular networking paradigm and provide some hints on how to deal with thesimulation of this kind of scenario.

15.2 MOBILITY MODELS

In general, vehicular mobility models can be classified into microscopic, macroscopic,and mesoscopic models. Macroscopic models are aimed at dealing with traffic density,traffic flows, and initial vehicle distribution modeling. On the other hand, microscopicmodels are in charge of modeling the location, velocity, and acceleration of each ve-hicle that participates in the simulated scenario. Finally, as an intermediate approach,mesoscopic models aggregate the movements of different nodes.

In this chapter we focus on the behavior of each vehicle as an independent unit.Therefore, we constrain our review to microscopic models, namely car following andmulti-lane traffic models.

15.2.1 Car Following

One of the most studied tasks involved in driving is that of a vehicle following thevehicle ahead along a lane of the roadway. Car following is simpler than other facetsof driving, and it has a great impact onto the macroscopic characteristics of trafficflow. Therefore, this topic has been the focus of deep study for several decades.

According to the literature [1,2], car-following models can be classified in thefollowing groups:

• Stimulus-Response Models. Chandler model (1958), generalized GM model(1961).

• Safe Distance Models. Gipps model (1981), Krauss model (1997).• Psychophysical Models. Leutzbach model (1986).• Cell-Based Models. cellular automata model (Nagel (1992)).• Optimum Velocity Models. Bando et al. (1995).• Trajectory-Based Models. Newell model (2002).


Let us briefly summarize these approaches and highlight their key aspects:

15.2.1.1 Stimulus–Response Model. It is assumed that the reaction of a driver isproportional to the stimulus he perceives. Following this statement, Chandler proposeda simple model [3] in which the relative speed with respect to the leading vehicle is theonly stimulus that the driver receives. The corresponding response takes place after agiven response time T . Since not every driver reacts at the same time given the samestimulus, the Chandler model also introduces a sensitivity factor λ. General Motorsconducted additional research on the subject, introducing new parameters into themodel such as the speed of the vehicles and the distance between them. This moregeneral model is commonly referred to as the GM model [4].

15.2.1.2 Safe Distance Model. One of the most important recommendations whichis followed by a good driver, consists of choosing the speed according to a safe distancewith the leading vehicle (in order to prevent a possible collision). This idea was firstintroduced into a car following model by Kometani and Sasaki [5]. Gipps extendedthe former model by making some common-sense assumptions about acceleration,deceleration, or maximum speeds, among others [6]. Later on, Krauss [7] proposeda variant of the Gipps model by introducing a stochastic term.

15.2.1.3 Psychophysical Model. This model considers the acceleration of thevehicle ahead as a stimulus for the following vehicle. It also considers the differ-ence between the current spacing and the desired following distance. This model wasproposed in Leutzbach and Wiedemann [8].

15.2.1.4 Cell-Based Model. This model, also known as cellular automata, was in-troduced by Nagel and Schreckenberg [9]. It considers two parameters to be opti-mized: the acceleration and the desired maximum speed. The particularity of thismodel is that it divides the traffic scenario into a set of cells of equal size. The sizeof the cells normally do not exceed the size of a vehicle, therefore only one vehiclewill be in each cell at a time. This model can be seen as a set of rules that control themovement of a vehicle from a cell to the next one.

15.2.1.5 Optimum Velocity Model. The first proposal based on the optimum ve-locity concept was presented in [10]. Within this model, the optimum velocity isthe required speed to maintain a given distance with the vehicle ahead. Thus, at anytime, the response of the following driver is proportional to the difference betweenhis optimum speed and his current speed.

15.2.1.6 Trajectory-Based Model. Finally, Newell [11] introduced a new modelthat takes into account the trajectory of the leading vehicle. It is assumed that thetrajectory of the leading vehicle and that of the following one are the same, exceptfor a translation in space and time. Thus, the following vehicle drives as a shiftedtrajectory of the vehicle ahead.

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Figure 15.1 Nearest neighbors of vehicle c considering lane change to the left (new andold successor are denoted by n and o respectively; acceleration after possible lane change isdenoted with a tilde).

15.2.2 Multi-lane Traffic

15.2.2.1 MOBIL. Single-lane car-following models were the first step to modelthe traffic of an entire road. However, real traffic flows consist of different types ofvehicles (cars, trucks, motorbikes, buses, and so on) traveling along several lanesat different speeds, thus generating heterogeneous traffic streams along roads. Thatis the reason why realistic traffic can only be simulated by including a multi-lanemodeling framework, in order to let faster vehicles overtake slower ones. In addition,traffic safety is directly affected by the lane-changing behavior of the drivers.

The following strategy for modeling lane changes consists of minimizing the over-all braking induced by the lane change (MOBIL) [12]. In Figure 15.1, vehicle c con-siders changing to the left lane. The decision generally depends on the vehicles inthe current lane (vehicle o) and in the target lane (vehicle n). Furthermore, within thelane-changing criteria, two main aspects are often differentiated. On one hand, themodel must provide an incentive for the vehicle to change its current lane. On theother hand, there are some safety restrictions that must be accomplished in order tomake a safe lane change.

Therefore, the safety criterion guarantees that, after a lane change, the decelera-tion of the successor (vehicle n) does not exceed a given safe limit. The former isrepresented in equation (15.1), where an is the acceleration of a vehicle after the lanechange, and bsafe is the limit for a safe deceleration.

an >= −bsafe (15.1)

Single-lane car-following models are aware of the difference of speed betweenvehicles. This dependence is also transferred to the lane-changing decisions. Thus,larger gaps between the new follower vehicle in the target lane (vehicle n) and theown position are required if the follower is faster than the own vehicle. On the otherhand, lower values for the gap are allowed if the speed of the following vehicle islower.

Safe-braking decelerations are modeled in longitudinal car-following models,therefore crashes due to lane changes are automatically excluded. The maximumpossible deceleration (bmax) is about 9 m/s2 on dry surfaces. Hence, depending onthe bsafe value employed in simulations, accidents are prevented even in the case ofselfish drivers (bsafe < bmax), whereas higher values than bsafe will provoke strongerperturbations due to individual lane changes.


The incentive criterion determines if the individual traffic situation of a driver isimproved by a lane change. This model also extends this evaluation to the immediatelyaffected neighbors as well. Thus, the incentive condition for making a lane-changingdecision for symmetric overtaking rules is given by equation (15.2).

ac − ac︸︷︷︸driver

+p( an − an︸︷︷︸new follower

+ ao − ao︸︷︷︸old follower

) > �ath (15.2)

Taking a look at the expression, the first two terms denote the improvement, interms of traffic conditions, of a possible lane change for the driver c (that is, thedifference between the new acceleration that the vehicle will have in the new laneand the acceleration it has in the current one). The third term of the equation givesthe same advantage of the two neighbors weighted by the politeness factor p. Finally,the switching threshold �ath on the right-hand side of the equation models a certainresistance to make the decision of lane changing that is identified by the “keep lane”directive. In fact, this equation also contains a safety restriction for the lane-changingvehicle. Lanes are only changed if the deceleration in the target lane is lower than inthe current one weighted by the politeness factor. It is worth noting that whereas thethreshold �ath models the overall vehicle behavior, the politeness factor only affectsthe local lane-changing behavior depending on the involved neighbors.

Although symmetric lane-changing rules can be applied in many highways, inmost European countries the driving rules also legislate the lane usage. For instance,in Spain, left lanes must be used only for overtaking other slower vehicles, and whilethere is no slower vehicle to overtake, vehicles must move using the right lane. Thus,MOBIL also formulated an asymmetric lane-changing criterion implementing the“keep right” directive.

The following traffic rules are assumed in this new criterion:

• Overtaking Rule. Overtaking is forbidden using the right lane, unless trafficflow is congested. In this case, the symmetric lane-changing rule is applied. If avehicle is driving at a speed lower than some specified velocity vcrit, congestedtraffic will be assumed (for example, vcrit = 60 km/h).

• Lane Usage Rule. The default lane is the right lane. The left lane should be onlyused in case of overtaking.

So the new passing rule is implemented by replacing the acceleration of the vehiclein the right-hand lane, as in the equation (15.3).

aeurc =

{min(ac, ac), vc > vlead > vcrit

ac, otherwise(15.3)

Therefore, this new passing rule influences the acceleration in the right-hand laneif the acceleration in the target lane is lower than the acceleration in the current lane.On the other hand, the keep-right directive is implemented by a constant bias �abias

in addition to the threshold �ath. The new traffic rules for asymmetric lane-changing

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lead to the following criterion for lane changes from left to right, as represented inequation (15.4).

L → R : aeurc − ac + p(ao − ao) > �ath − �abias (15.4)

On the other hand, the incentive criterion for changing lanes from the right lane tothe left one is given by equation (15.5).

R → L : ac − aeurc + p(an − an) > �ath + �abias (15.5)

From these equations we can deduce that the parameter �abias is small, but it hasto be larger than the threshold �ath. Otherwise, lane changes from left to right willnot occur even on an empty road.

With equations (15.4) and (15.5), a vehicle in the right lane will consider thedisadvantage measured in terms of the braking deceleration of the approaching vehiclein the target line depending on the politeness factor p. Thus, the MOBIL lane-changingmodel reflects the driver behavior of considering whether an overtaking is dangerousby taking a look at the rear-view mirror. The decision is made according to theperceived speed of the approaching vehicles.

15.3 MOBILITY SIMULATORS

15.3.1 Commercial Mobility Simulators

The study of the behavior of vehicles along urban and inter-urban scenarios startedlong time ago. It is of paramount importance to design the appropriate traffic infra-structure for modern intra-city and inter-city transportation. In such context, mobilitysimulators are a great tool to evaluate the impact of new roads and highways.

Therefore, we can find a lot of mobility simulators that are able to generate vehiclesmovements even for really complex scenarios. Among them, some of the most relevantones are [13]: TSIS-CORSIM [14] (Traffic Software Integrated System–CorridorSimulator), PARAMICS [15], and VISSIM [16].

They are so powerful that they can model nearly everything related to road building.These simulators allow to vary the number of lanes, the shape of the roads, add on-ramps and off-ramps, traffic lights, and so on. In terms of vehicles mobility, theyusually implement a car-following model, as well as a multi-lane changing model tosimulate overtaking among vehicles.

Starting with TSIS-CORSIMTM, this simulator was developed by the Universityof Florida and was funded by the Federal Highway Administration (FHWA). It iswidely used in the transportation research community, and it can simulate very com-plex traffic scenarios. TSIS-CORSIMTMis comprised of several components, withNETSIMTMand FRESIMTMamong the most important. NETSIMTMsimulates thesurface of the streets, whereas FRESIMTMsimulates the freeways. Such componentsmake this simulator able to model highways, freeways, intersections, and road seg-ments of different sizes, add or remove lanes, and determine number of lanes, free flow


Figure 15.2 TRAFVUTMcomponent of TSIS-CORSIMTMmobility simulator. CourtesyUniversity of Florida. Source: http://mctrans.ce.ufl.edu/featured/tsis/Version6/trafvu.htm.

speeds, roadway curvature, and roadway grade. Besides, it can produce animationsof the simulated traffic network by using TRAFVUTM, another of its components(Figure 15.2). Regarding the mobility models, both vehicle and driver behavior mod-els are commonly employed as a reference for comparison with other simulators.

PARAMICS, developed by Quadstone Limited, also implemented its own car-following and lane changing models. It is able to simulate intersections, urban areas,highways, and the like. Its modeler program, illustrated in Figure 15.3, is able toedit the traffic network and view the result as an animation. Simulation and editionare simultaneously supported. 2D and 3D animations employ enhanced rectangularshapes for car, trucks, buses, and trains, using different colors. On the other hand,PARAMICS Analyzer can be employed to display the performance measures that areobtained as a result of the simulation.

The final commercial microscopic simulator that we review, VISSIM, was devel-oped by PTV AG. It introduces several improvements in terms of driver behavior,multimodal transit operations, interface with planning/forecasting models, and 3Dsimulations. For the mobility, it implements the Wiedemann car-following modelwith time steps as low as 1/10 seconds. The model is able to simulate overtakinginside wide lanes with cars and motorbikes/cycles driving in the same lane. It alsoimplements powerful lane-changing behavior models that include motorway junctionsand zip merge, among others. Like PARAMICS, VISSIM also offers 3D animationsthrough the V3DM module. In addition, it can generate AVI movies that show thetraffic flow of a city center or a highway.


Figure 15.3 PARAMICS modeler component. Source: http://www.paramics-online.com/paramics-media.php.

These simulators have been evaluated for designing and simulating a particularhighway [17]. From this analysis, different results are obtained and discussed. Allthe evaluated simulators require three or four days to set up the scenario. However,there is little or no documentation in the corresponding user manuals about how tocalibrate the simulation to make it closer to reality.

Each simulator provides an estimation of the use of the road. For freeways,CORSIM does not provide a direct estimate of density for each lane while the othersdo. Moreover, the more flexible one is VISSIM offering different ways for obtainingthis metric.

CORSIM and PARAMICS use link-based routing, which can incur inaccurate laneutilization for closely spaced intersections. This problem is solved in VISSIM becauseit uses a different routing approach based in paths.

To sum up, results generated by PARAMICS and VISSIM simulations were betterin terms of traffic engineering principles and perception/expectation of the reviewingagencies.

15.3.2 Noncommercial Mobility Simulators

From the point of view of a VANET researcher, it is more interesting to analyzeand evaluate network protocols on an approximated vehicular scenario without high-resolution movement of vehicles than to spend a great amount of time in setting uppowerful and complex mobility simulators. This is the reason why a series of non-commercial mobility simulators have been developed by the vehicular networking


Figure 15.4 Screenshot of SmartAHS. Source: http://gateway.path.berkeley.edu/SMART-AHS/sampleImages.html.

research community. Actually, mobility trace files generated by these simulators areusually employed as input for communication simulators. In this way, vehicular move-ment patterns are incorporated into the simulation of VANET protocols and services.In this chapter we review the most relevant mobility simulators, highlighting theirmain features like the supported mobility models or the possible interaction withnetwork simulators.

SmartAHS [18] is a framework for the specification, simulation, and evalua-tion of Automated Highway Systems (AHS) which was developed as part of theCalifornia PATH project at UC-Berkeley in 1997. A snapshot of SmartAHS is shown inFigure 15.4. Its components are able to model the layout of highways, different trafficpatterns, weather conditions, vehicle dynamics, actuators, communication devices,sensors, and controllers. In addition, for the purpose of simulation, various environ-ment processors are provided.

Another tool to simulate traffic flows is the Microscopic Traffic Applet [19], de-veloped by Volkswagen AG. It describes the traffic flow on freeways near and atlocations of roadwork, and how to improve it, by means of simulations and a vehicle-based implementation of traffic-adaptive cruise control (ACC). The project appliesthe results obtained in a previous project called INVENT. It integrates the IntelligentDriver Model (IDM) car-following model and the MOBIL lane-change model in sixdifferent scenarios within two basic topologies: ring and oval-shaped road. Since this


tool is an applet designed to illustrate IDM, it does not include any mechanism toimport maps from other sources or to obtain a trace file for a network simulator.

VanetMobiSim [20] was developed as an extension for CanuMobiSim [21], a mo-bility model for mobile ad hoc networks (MANET). IDM–intersection management(IDM-IM) and IDM–lane changes (IDM-LC) mobility models were implemented inorder to deal with vehicular scenarios. This simulator outputs trace files suitable forexisting network simulators such as ns-2 and GloMoSim. As input, it accepts mapsfrom the U.S. Census Bureau’s TIGER/Line database. Furthermore, this simulatorhas been validated against CORSIM, showing the accuracy of its mobility modelsand, therefore, the behavior of the vehicles along the road. Regarding traffic genera-tion, it is done according to different stochastic processes (Poisson or Erlang, amongothers). In addition, it simulates obstacles that can be parsed by a network simula-tor, in order to compute the effect of such obstacles into the propagation of wirelesssignals.

Finally, SUMO [22] (Simulation of Urban Mobility) is a microscopic road trafficsimulation package designed to handle large road networks. It is mainly developedby employees of the Institute of Transportation Systems at the German AerospaceCenter. This simulator can represent different vehicle types. Vehicle movements arespace-continuous (using float numbers to represent their positions) and time-discrete.SUMO is able to deal with multi-lane streets with lane changing and different trafficlights. As we will see later, it is able to interoperate with other applications at runtimeand it also supports maps from TIGER/Line, ESRI [23], and Google Earth. Regardingthe mobility models, SUMO can handle the Krauss car-following model with somemodifications (by default), the Intelligent Driver Model, and more. Furthermore, italso includes a visualization tool to observe the movement of the simulated vehicles,as shown in Figure 15.5.

Table 15.1 provides an overview of the main features of the aforementionednon-commercial mobility simulators, highlighting the main capabilities that theyoffer.

Then, among the reviewed mobility simulators, which one would be a good choice?Regarding SmartAHS, the source code can be easily obtained from its website. How-ever, this tool seems not to be maintained since 1997, making the installation moredifficult to be installed on current operating systems.

As previously said, the Microscopic Traffic Applet does not offer any mechanismto modify the scenarios. We cannot introduce a new highway with a different shape.Moreover, it is not able to interact with a simulator by providing output mobilitytraces.

On the other hand, both VanetMobiSim and SUMO offer mechanisms to interactwith network simulators. VanetMobiSim provides helpful documentation to installand configure the simulator. In fact, the user manual gives us a lot of informationand examples to define scenarios, as well as to select the mobility model of thevehicles.

Finally, SUMO offers in its website a lot of information and documentation forthe installation and configuration processes. Although the scenarios are described bymeans of XML files, there exists a tool to facilitate the definition of the simulation


Figure 15.5 SUMO GUI.

scenario. This tool is called Realistic Mobility Generator for Vehicular Networks(MOVE) [24], a Java application that organizes the needed steps to create the scenario,including a map editor, a vehicle movement editor, and a simulation setup editor. Inthis way, MOVE generates a XML file with the scenario description that is employedafterward by SUMO to run the simulation.

Table 15.1 Comparative Table of Mobility Simulators

Mobility Mobility Models Integration withSimulators Implemented Network Simulators

SmartAHS Particle hopping model —Kinematic car following

Microscopic Intelligent-driver model (IDM) —traffic applet Lane-change model MOBIL

VanetMobiSim IDM–intersection management Traces suitable forIDM–lane changes ns-2 and GloMoSim

SUMO Krauss, IDM, among others ns-2, omnet++

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15.4 INTEGRATED SIMULATORS

There are several well-known network simulators that are commonly employed bythe vehicular ad hoc networking community, including NS-2 [25], OMNET++ [26],and SWANS [27], in addition to other commercial solutions like OPNET [28] andQualNet [29]. They allow to simulate different kind of fixed and mobile networks.Focusing on mobile networks, these simulators accept different network topologiesas input, as well as a trace file describing the mobility of the nodes. Therefore, nodesin simulations are able to modify their location and speed as time passes by, lettingthe simulator deal with the communication issues. Regarding the physical layer, thesesimulators are also able to implement different 802.11 technologies, also dealing withthe vehicular-specific 802.11p standard with its specific features. They also offerdifferent radio propagations models, such as Log-Normal Shadowing, Nakagami,Rayleigh, and Rice. Recently, NS-3 [30] has been established as a substitute for itspredecessor. Although it is not as complete as NS-2 yet, it already features supportfor 802.11p and several wireless propagation models.

A comparison of these network simulators was conducted in reference 31, focusingon two different metrics: the simulation run-time and the memory usage. In light ofthe results gathered in this paper, SWANS performs quite similarly to OMNet++ andns-3, in terms of simulation runtime. Ns-2, on the contrary, takes about 300 s more toperform the same simulations. In terms of memory usage, ns-3 is the one that obtainsthe best results with a gap of 15 MB with respect to ns-2 and OMNET++, and about80 MB with respect to SWANS.

These simulators have been widely used by the mobile networking community,mainly employing synthetic random mobility models. However, VANETs introducenew applications in which the communication among nodes directly affects theirmovements. For instance, some safety and traffic management services influence thedriver behavior in order to avoid an accident or choose an alternative route to thedestination.

For such kind of experiments, the aforementioned simulators cannot deal with thistype of interaction. They just take as input a movement trace file, without allowing theinteraction between the network and the mobility simulators. This is the reason whyVANET researchers are interested in developing new simulators in which mobilityand communications are coupled. With this purpose, new simulators like TraNS [32],Veins [33], NCTUns [34], and VGSIM [35] were developed.

As a first approach to this new type of simulator, SWANS++ [36] or GrooveNet [37]were developed. SWANS++ is an extension of the JiST/SWANS framework thatincludes the implementation of GPSR and DSR protocols and the STRAW mobil-ity model, including lane-changing. However, although this simulator generates themovement of the vehicles, it does not provide feedback between the mobility andthe network modules. The same happens with GrooveNet, whose main ability is tosimulate both real and virtual vehicles.

Nevertheless, the interaction between both mobility and network simulators issuccessfully achieved in the simulators that we describe below. This interaction canbe implemented in several ways: For instance, TraNS [32] and Veins [33] use a new


interface called Traffic Control Interface (TraCI) to interlink NS-2 (network simulator)and SUMO (traffic simulator). ASH [38], on the other hand, implements several meth-ods to provide feedback between the application layer and the mobility model. Thus,both approaches create a pair of queues or a double communication queue, allowingthem to exchange events between the mobility and the network traffic simulators.

TraNS (traffic and network simulation environment) is considered the first fullyintegrated VANET simulator. It combines the network simulator NS2 with SUMO,providing feedback from the network simulator to the mobility simulator. This sim-ulator offers two operating modes: (1) a network-centric mode in which there is nofeedback and (2) an application-centric mode in which the feedback is provided bya new interface called traffic control interface (TraCI). Since TraNS is based on NSto simulate network traffic, it is able to generate realistic simulations with 802.11pand probabilistic signal models. Thanks to this new interface, mobility commandscoming from ns-2 are interpreted as movements instructions such as “stop,” “changelane,” “change speed,” and the like. Since the two simulators are run separately, twoseparate event queues are needed to interact with each other.

SUMO has also been integrated with another network simulator, OMNeT++/INET,by using a TCP connection between both simulators. This gave birth to a new simulatorcalled Veins [33]. Like TraNS, it is able to generate realistic simulations. Regardingthe communication between both modules, it is also achieved by implementing TraCI.Therefore, OMNeT++ is extended with a new module that is able to send commandsover the TCP connection to SUMO. Besides, like other simulators, Veins can im-port maps from OpenStreetMap, including buildings, speed limits, lane counts trafficlights, and access and turn restrictions, and it can also simulate obstacles simulatingshadowing effects caused by obstacles like buildings.

NCTUns 6.0 [34,39] (National Chiao Tung University Network Simulation 6.0)was born as a network simulator. However, the latest versions also integrate mapdesign and vehicles mobility. Among its features, this simulator is able to use twokinds of channel model: a “theoretical channel model” and an “empirical channelmodel.” Within the first channel models, NCTUns supports three theoretical pathlossmodels, namely the “free space,” “two-ray ground,” and the “free space and shad-owing.” In addition, three different fading models are supported: “no-fading (none),”“Rayleigh fading,” and “Ricean fading.” Regarding the “empirical channel model,”it collects channel models that are developed based on real-life measurement results.So far, NCTUns supports 23 empirical channel models, such as “LEE Microcell,”“Okumura,” “COST 231 Hata,” and so on. Unlike previous simulators, it only imple-ments two vehicular movement patterns: prespecified mode and autopilot mode. Inthe first one, the user is restricted to just set the path and the speed of the vehicles.In the second mode, it is necessary to set some more parameters like the initial andmaximum speeds, the initial and maximum accelerations, and so on. A car-followinglane-changing model is also implemented, allowing overtaking between vehicles aswell as turning. Traffic lights are also supported. This simulator also has a commercialversion called EstiNet [40].

ASH [38] (Application-aware SWANS with Highway mobility) is an extension ofSWANS. This simulator, unlike SWANS++, implements methods to provide feedback

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between the application layer and the mobility model. It also implements the IDMcar-following model and the MOBIL lane-changing model, along with an implemen-tation of the Inter-Vehicle Geocast broadcasting technique. Moreover, it also supportsthe simulation of noncommunicating vehicles, roadside units, and obstacles. Never-theless, it can simulate lots of scenarios but only a highway segment that can beconfigured as a one-way or two-way highway, including variations of the number oflanes, entries, and exits, as well as their location within the highway.

Finally, VGSim [35] is also based on SWANS. Vehicle mobility is modeled by usinga modified version of the Nagel and Schrekenberg (N-S) model, in which a series ofimprovements were introduced (a finer spatial and temporal solution and the lane-changing capability). In this simulator, SWANS can communicate with the mobilitymodule by updating the position of the vehicles, which is in turn reflected within thesimulated network. Therefore, the decision about how to change speed/position canbe made depending on the traffic conditions and the received traffic control messages.Authors validated the N-S model with real-world traffic data provided by the NGSIMproject [41], obtaining results close to reality.

We have summarized the main features of the simulators in Table 15.2. Remarks onthe kind of interaction between the mobility and the network modules have been pro-vided, as well as information about the mobility models that have been implementedwithin each one. In addition, the signal propagation models that are implemented andthe variety of supported scenarios are also indicated.

Finally, in order to provide some hints on the use of the aforementioned simulatorsby the researchers investigating in the vehicular environment, Table 15.3 shows thenumber of occurrences of these simulators in the proceedings of the 72nd IEEE

Table 15.2 Comparative Table of Integrated Simulators

Mobility Signal Fading Scenario’sSimulators Interaction Models Models Models Description

TraNS NS-2 Krauss Free-space (FS) Rayleigh Notto IDM C-F Two-ray ground Ricean restrictedSUMO L-C Nakagami

Veins OMNeT++ Krauss FS Rayleigh Notto IDM C-F restrictedSUMO L-C

NCTUns Full IDM C-F FS No-fading NotMOBIL L-C Two-ray ground Rayleigh restricted

FS and shadowing RiceanEmpirical models

ASH Full IDM C-F — — Only aMOBIL L-C highway

segmentVGSim Full STRAW FS No-fading Only a

Simple C-F Two-ray ground Rayleigh highwayRicean segment


Table 15.3 Number of Occurrences of the AboveSimulators within the 72nd IEEE VehicularTechnology Conference 2010—Fall Proceedings

Simulators Occurences in Papers

NS-2 19OPNET 6QualNet 3OMNET++ 2SWANS 1NCTUns 1SUMO 4VanetMobiSim 1

Vehicular Technology Conference 2010—Fall Proceedings. Although NCTUns isused only once within this conference, other well-known network simulators like ns-2 or OMNET++ are employed receiving as input the mobility traces from SUMO andVanetMobiSim. This can be interpreted as a first step in using integrated simulatorslike TraNS or Veins within the research area.

15.5 MODELING VEHICULAR COMMUNICATIONS

15.5.1 Wireless Links

When modeling a wireless ad hoc network, one of the first questions we have toanswer is when we can state that a node u is able to communicate with anothernode v. In such case, we say that a link exists from u to v. Throughout this chapter,we assume that nodes employ omnidirectional antennas. This is the most commonscenario, although works on ad hoc networks with directional antennas have also beenundertaken [42].

The simplest approach to model a wireless link is derived from a uniform diskgraph (UDG). All nodes are assumed to feature a communication range of radius r.In this way, a bidirectional link between u and v exists if and only if distance(u, v) ≤ r.Note that a UDG represents an ideal network in the sense that perfect communicationoccurs up to r distance units from the source. This model does not take into accountreception errors that might be provoked by radio interferences. It has been shownthat real wireless links do not follow this ideal model at all [43]. However, the UDGmodel has often been employed in the literature and provides a rough estimation ofnetwork connectivity.

In order to model realistic wireless links, signal propagation must be accuratelydefined (Section 15.5.2). This determines how signal power dissipates as a functionof the distance. In the absence of interferences, the receiver will be able to decode

MODELING VEHICULAR COMMUNICATIONS 561

the wireless signal, and therefore reconstruct the original message, whenever thesignal-to-noise ratio (SNR) satisfies the following condition:

SNR = S

N≥ β (15.6)

where S is the received signal power, N is the noise power, and β is a thresholddependent on the sensitivity of the wireless decoder. Noise represents the undesiredrandom disturbance of a useful information signal.

Since wireless medium is shared by the nodes in an ad hoc network, transmissionsfrom a node interfere with concurrent communications between different nodes. Thismay cause great disturbance in the resulting signal, so that receivers would not beable to decode the message. In such case, we say that a collision has occurred. Thus,in the most general case, correct reception of a message by a node must satisfy thatthe signal-to-interference-noise ratio (SINR) holds the following requirement:

SINR = S

I + N≥ β (15.7)

where I is the cumulative power of interfering signals. Physical simulation modelsemployed throughout this chapter [44] compute the SINR for each transmission inorder to decide whether the message can be decoded or not. Next we describe someof the commonly employed propagation models for wireless signals.

15.5.2 Wireless Signal Propagation

As we have seen in the previous subsection, signals lose energy as they propagatethrough space. Several factors contribute to this phenomenon, such as the naturalpower dissipation as the signal expands, the presence of obstacles which reflect anddiffract the original signal, and the existence of multiple paths that may lead to signalcancellation at the receiver. Let us briefly describe some models that account forseveral of these factors.

The Friis’ free space propagation model [45] assumes the ideal condition that thereis just one clear line-of-sight (LOS) path between sender and receiver. If we considernodes located in a plane, this model represents the communication range as a circlearound the transmitter. Transmission power is therefore dissipated as distance fromthe transmitter increases. Friis proposed the following expression to estimate signalstrength at reception Pr as a function of the distance d from the transmitter.

Pr(d) = PtGtGrλ2

(4π)2d2L(15.8)

where Pt is the transmitted signal power, Gt and Gr are the antenna gains of thetransmitter and receiver, respectively, L ≥ 1 is the system loss (because of electroniccircuitry), and λ is the signal wavelength.

In ordinary terrestrial communications, ideal conditions to apply the Friis modelare rarely achieved. For instance, reflection from the ground is not considered in


this type of model. For long distances, the two-ray ground reflection model providesmore accurate predictions. Both the direct path between sender and receiver and theground-reflected path are considered in the following equation.

Pr(d) = PtGtGrh2t h

2r

d4L(15.9)

where ht and hr are the heights of the transmit and receive antennas, respectively. Forshort distances, this model does not provide accurate results due to the oscillationscaused by the constructive and destructive combination of both rays. In such cases,the free space model must be used instead. Therefore, a crossover distance dc iscalculated. When d < dc, equation (15.8) is employed. When d > dc, equation (15.9)is used. At the crossover distance dc, Friis and two-ray ground models must providethe same result, so that dc can be calculated as follows.

dc = 4πhthr

λ(15.10)

The two-ray ground model predicts higher attenuation of signal power with respectto distance (d4) than does the free space model (d2). However, it still provides anideal view in which all nodes in the communication range receive the message, whilenodes outside do not receive it. In reality, wireless communications do not feature adeterministic radio range describing a perfect circle around the transmitter.

Path loss can be described more accurately by using probabilistic (nondeterminis-tic) models. These models reflect random variations in signal strength over distancesthat are large compared to the wavelength. Such variations are known as large-scalefading (or shadowing) and are due to the presence of objects which obstruct and scat-ter the wireless signal. Among these models, the log-normal path loss model is verywidespread and its generic form is given as

Pr(d) = Pr(d0) − 10γ log10

(d

d0

)

+ Xσ (15.11)

where d0 is a reference distace, γ is the path loss exponent, and Xσ is a zero-meannormally distributed random variable with standard deviation σ. By means of sets ofexperiments, parameters γ andσ can be obtained via regression, adjusting the model toproduce realistic random values for a given scenario. However, more accurate resultscan be found by using dual-slope piecewise-linear models such as the next one [46].

Pr(d) =

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

Pr(d0) − 10γ1 log10

(dd0

)+ Xσ1 , d0 ≤ d ≤ dc

Pr(d0) − 10γ1 log10

(dc

d0

)

− 10γ2 log10

(ddc

)+ Xσ2 , d > dc

(15.12)

Under this model, the crossover distance dc can also be empirically determined.Log-normal models are useful for large-scale fading, but they do not capture small-scale fading due to interference between multipath components. This kind of fading

MODELING VEHICULAR COMMUNICATIONS 563

occurs on the scale of a wavelength, and it might be the dominant component in asevere multipath environment like the one encountered in vehicular networks.

The Rician distribution accurately models a stronger LOS in the presence of scat-terers. On the other hand, Rayleigh distributions are used to model dense scattererswhen no line-of-sight (NLOS) is present. Finally, the Nakagami distribution is a gen-eral model in which different severities of fading can be modeled, depending on thedistribution parameters. In fact, both Rician and Rayleigh fading can be seen as aspecial case of the Nakagami model. This distribution approximates the amplitude ofthe wireless signal according to the following probability density function f (x; μ, ω).

f (x; μ, ω) = 2μμx2μ−1

ωμ(μ)e

−μx2

ω (15.13)

where μ is a shape parameter, ω = E[x2] is an estimate of the average power inthe fading envelope, and is the Gamma function. Given that signal amplitude isNakagami-distributed with parameters (μ, ω), signal power obeys a Gamma distri-bution with parameters (μ, ω

μ).

Every kind of signal suffer these phenomenons. Despite the fact that these modelsare not specific for VANETs, they are relevant for certain aspects of the VANET signalpropagation modeling like reflection, path loss, or shadowing effects.

15.5.3 Communication Technology

Vehicular networks have several features that make them different from other mo-bile networks. These specific features create a series of requisites in order to providesuccessful wireless communications among vehicles. Such communications need tobe fast, with short range making them scalable as well as with low latency. Thus,the IEEE 1609 Family of Standards for Wireless Access in Vehicular Environments(WAVE) was born to fulfill these specific requisites defining an architecture, a set ofstandardized services, and interfaces that collectively enable secure vehicular com-munications between vehicles and with the infrastructure.

This family consists of the following standards:

• IEEE P1609.0: Draft Standard for WAVE—Architecture. This standard de-scribes the Wireless Access in Vehicular Environments (WAVE/DSRC) archi-tecture and services necessary for multichannel DSRC/WAVE devices to com-municate in a mobile vehicular environment.

• IEEE 1609.1-2006: Trial Use Standard for WAVE—Resource Manager. It de-scribes the management services and data offered in the WAVE architecture,specifying the command message format that must be used by applications.

• IEEE 1609.2-2006: Trial Use for WAVE—Security Services for Applicationsand Management Messages. It defines secure message formats and how theymust be processed.


• IEEE 1609.3-2007: Trial Use for WAVE—Networking Services. It defines net-work and transport layers supporting secure WAVE data exchanges. It also de-fines the Management Information Base (MIB) for the WAVE protocol stack.

• IEEE 1609.4-2006: Trial Use for WAVE—Multi-Channel Operations. It pro-vides improvements to the 802.11 Media Access Control to support WAVEoperations.

• IEEE P1609.11-2006: Over-the-Air Data Exchange Protocol for IntelligentTransportation Systems (ITS). It defines the services and secure messagesformat necessary to support secure electronic payments.

These standards rely on IEEE P802.11p [47] as the physical layer that providesthe needed mechanisms for high-speed (up to 27 Mbps), short-range (up to 1000 m),low-latency wireless communications in the vehicular environment.

Focusing on the lowest layer, 802.11p is quite similar to 802.11a and 802.11g. Infact, it maintains the same structure, using the same modulation scheme (Orthogo-nal Frequency-Division Multiplexing, OFDM) and the same medium access scheme(Carrier Sensing Multiple Access with Collision Avoidance, CSMA/CA).

However, it has two key aspects different from 802.11a and 802.11g. The US Fed-eral Communications Commission (FCC) as well as the European Commission (EC)allocated the frequency-band of 5.9 GHz (5.850–5.925 GHz) for Dedicated Short-Range Communications (DSRC). In addition, in 802.11p the duration of the OFDMsymbols is doubled from 4 μs to 8 μs. Thus, having a specific frequency spectrumreserved for communication, it reduces the interferences with other networks. In-creasing the duration of the OFDM symbols, it also reduces the OFDM inter-symbolinterference (ISI) in outdoor channels.

Regarding the medium access control, 802.11e proposed Enhanced DistributedChannel Access (ECDA) to allow different kinds of traffic to be served with differentpriority. For this purpose, it uses two different parameters, Arbitration Inter-FrameSpace (AIFS) and Contention Window (CW), whose variations make it possible tohave different kinds of flows with different quality of service (QoS). If a node wantsto send some urgent data, it can use low values for both AIFS and CW, thus reducingthe waiting time to gain access to the medium.

This strategy is also adopted in 802.11p by defining four available data traffic cate-gories with different priorities: background traffic, best effort traffic, voice traffic andvideo traffic. The corresponding values for AIFS and CW are illustrated in Table 15.4.

Table 15.4 AIFS and CW Values for Different Application Category

Application Categories CWmin CWmax AIFSN

Video traffic 3 7 2Voice traffic 3 7 3Best effort traffic 7 225 6Background traffic 15 1023 9

ANALYSIS OF CONNECTIVITY IN HIGHWAYS 565

Summing up, the IEEE 1609 family has been developed upon IEEE 802.11p tocompletely address the problem of secure vehicular communications, namely, vehicle-to-vehicle and vehicle-to-infrastructure, offering quality of service for high-speed,short-range communications.

15.6 ANALYSIS OF CONNECTIVITY IN HIGHWAYS

In this section we perform a simulation-based study on the connectivity that is to beexpected on typical highways. First, we determine the communication ranges that canbe obtained by using IEEE 802.11p as VANET communication technology. Then, wemodel highways and different traffic flows by means of the SUMO package [22].Vehicles move according to the Krauss car-following model, as implemented withinthis tool. Simulations of road traffic are run during one hour of simulation time. Weignore the first six minutes in order to get the highway in a steady state.

Unlike the study in OPERA [48], we do not consider a uniform car distributionnor a fixed radio range. However we shall see below that our results confirm theassumption of OPERA that in general there is no end-to-end connectivity.

The key aspect of our work is that we consider the different modifications of802.11p and realistic mobility. Our goal is to measure their influence into end-to-endconnectivity and also into the store-carry-forward connectivity.

We will focus on several simulated intervals of one minute which present differenttraffic properties. In this chapter we will employ the nomenclature of the HighwayCapacity Manual (HCM) [49], which classifies traffic conditions according to a givenlevel of service (LOS). Level of services are designated by letters A to F, with A beingthe least congested situation and F the most. Each section of the highway under studycan feature a different LOS, as it happens in reality. In order to decide the LOS foreach section, we employ as reference the classification found in the Skycomp report[50], where highways of New York are classified into a LOS according to their density(vehicles per mile per lane) at different timeframes.

Classification of the traffic state into several LOS allows us for considering thosetime intervals in which the highway feature enough vehicle density. From the con-nectivity viewpoint, this is a “good case” in the sense that many vehicles are travelingalong the highway and therefore the subjacent network should be more connected.We will try to determine when communications can succeed if 802.11p is employed.

15.6.1 Determination of the Communication Radio Range

We assume the IEEE 802.11p technology [47] for VANET communications. In thissubsection we perform a realistic simulated analysis of the effective radio range r

that can be obtained in a highway. Such study is optimistic in a number of ways,so that results can be seen as an upper bound on the physical communication rangethat can be actually achieved in a given deployment. On one hand, we assume thatthe maximum allowable transmission power is employed by each vehicle. Com-monly, this situation is not desired because higher interference is generated for the


remaining communications within the network. However, from a connectivity view-point, the highest possible radio range is obtained. In addition, we further assume aninterference-free scenario. That is, no additional data flows are considered. Further-more, intraflow interference is not considered either. If a data flow is to be routedalong the vehicular network, or if concurrent communications occur, the effectiverange would be shorter than r, the one considered within this section.

The simulation work has been done with The Network Simulator ns-2 [25], version2.34. It incorporates an enhanced implementation [44] of the physical and mediumaccess layers of the IEEE 802.11 standard. In addition, it allows for the easy configu-ration of parameters to simulate the 802.11p amendment [47]. This standard specifiesdifferent transmission rates in the 5.9-GHz band using 10-MHz channels, from 3to 27 Mbps. Each of them corresponds to a different combination of a modulationscheme (BPSK, QPSK, 16-QAM, or 64-QAM) and a coding rate (1/2 or 3/4 of usefulbits, redundancy employed for forward error correction). All compliant devices mustsupport bit rates of 3, 6 and 12 Mbps. At higher rates, frames are harder to decode be-cause the signal is more sensitive to interference. Hence, we consider different ratesin our study in order to obtain their corresponding radio ranges rrate for vehicularcommunications.

Our simulation setup includes realistic modeling of wireless signal propagation inhighways. As discussed in Section 15.5.2, deterministic models are not able to capturesome features of signal propagation like multipath fading [51,52]. The Nakagamidistributionf (x; μ, ω) is often used to represent the amplitude of a signal that reaches areceiver by multiple paths. The former distribution has been employed to model fadingin highway scenarios [53]. The model is adjusted by means of a set of experimentscarried out on highway 101 in the Bay Area. Estimate ω is obtained from a logarithmicpath loss model, as implemented in Chen et al. [44].

For each run, we define a static scenario in which a data source issues 1000 unicastpackets at the maximum transmission power, which is of 33 dBm (2 W) for privateuse within the 5.9-GHz band. Destination is placed at increasing distances (step of50 m) from the source. Four different packet sizes have been considered. We assumethe Internet model in this experiment, so that such packets are generated by an appli-cation and there is an associated IP overhead (IP header, routing protocol overheadnot included). We also consider the ARP request/reply overhead1 in order to obtainthe layer-2 address of the next hop. Given that no interfering transmissions are simu-lated, reception probability does not depend on the packet size. By the same reason,RTS/CTS exchange is disabled in this study. The maximum number of retransmis-sions is fixed to 7, the maximum default value in the 802.11 standard [54]. Simulationparameters are summarized in Table 15.5.

Figures 15.6 and 15.7 show the probability of reception with respect to the distancebetween sender and receiver, as well as the number of transmissions that are neededto deliver the packet to the next vehicle. Results are shown for packets of 500 bytes,

1Assuming IPv4, although the same functionality is present in IPv6 by means of the Neighbor Discovery(ND) protocol.


Table 15.5 Simulation Parameters for Determining802.11p Unicast Communication Ranges

Packet size 250, 500, 1000, 1500 bytes

Bit rate 3, 6, 12, 27 MbpsTx power 33 dBmMax retries 7RTS/CTS DisabledPath loss Log-normalFading Nakagami

since very similar figures are obtained for the other simulated packet sizes. Sincewe are using realistic propagation models, the reception of a packet (Figure 15.6) israndom in nature but it is heavily influenced by the distance between communicatingstations. For close distances, reception probability remains at 100%. From a givencrossover distance dc

rate, reliability decays until it reaches 0%.As the destination is farther from the source, more retransmissions are needed

to deliver the packet (Figure 15.7). In order to provide high reception probabilityfor unicast frames, 802.11 employs an acknowledgment (ACK) mechanism in whichnew transmissions take place until an ACK is received. The number of transmis-sions features an absolute maximum near the crossover distance dc

rate. Afterwards,the probability of receiving a single frame is so low that the maximum number ofretransmissions is not enough to deliver the packet. At the same time, the number ofissued packets decreases because the ARP mechanism is not able to succeed. That

1

0.8

0.6

0.4

0.2

00

Rec

eptio

n P

roba

bilit

y

500 1000 1500 2000

Distance (m)

2500 3000

3 Mbps6 Mbps

12 Mbps27 Mbps

Figure 15.6 Rx probability.


7

6

5

4

3

2

1

00

Tran

smis

sion

s pe

r P

acke

t

500 1000 1500 2000

Distance (m)

2500 3000

3 Mbps6 Mbps

12 Mbps27 Mbps

Figure 15.7 Transmissions.

is, some packets get lost without ever being sent because the ARP request was neverreceived by the other end. If we did not account for the ARP mechanism, the numberof transmission would remain constant at the maximum level for higher distances.

For each transmission rate, we define the effective communication range rrate asthe one that guarantees data delivery to the next hop with high probability (≥99%).This concept is analogous to the crossover distance, so that rrate � dc

rate. Table 15.6shows the effective radio range that we obtain in our simulations for each of theconsidered transmission rates. In the following, we will employ these ranges to studythe connectivity properties of vehicular networks in different highway setups.

15.6.2 Connectivity in a Single Lane

We first consider a single-lane highway, as shown in Figure 15.8. While one-lanehighways are not realistic, we focus on this scenario first and construct a two-lanescenario afterwards. Our highway is comprised of a 5-km section on which we studythe connectivity properties of traffic, plus an initial section, a final section, an onramp,

Table 15.6 Effective Communication Rangesa

Bit Rate Range (m) Rx Probability Transmissions

3 (Mbps) r3 = 1500 0.990 ± 0.0065 6.43 ± 0.0986 r6 = 1250 0.991 ± 0.0061 3.36 ± 0.14312 r12 = 800 0.996 ± 0.0043 3.18 ± 0.13827 r27 = 400 0.999 ± 0.0022 2.55 ± 0.114a95% confidence intervals.


Figure 15.8 Simulated highway with one lane.

Table 15.7 Vehicle Types Employed in our SUMO Simulations

Type Acceleration (m/s2) Deceleration (m/s2) Max Speed (mi/h)

Slow 0.5 4.0 50Standard 0.8 4.5 65Fast 1.1 5.0 80

and an off-ramp. We assume that the on-ramp leads vehicles from downtown to thehighway, while the off-ramp leads vehicles from the highway to downtown. Therefore,two traffic lights are also simulated in order to mimic traffic bursts coming into thehighway and bottlenecks in the direction of downtown.

We define three different types of vehicles, namely slow, standard, and fast cars.Their different characteristics are summarized in Table 15.7. In all our experimentswe employ two different traffic flows, both with a proportion of slow (10%), standard(70%), and fast (20%) cars. For each flow, SUMO injects a vehicle into the simulationat a rate of one per second.

In this subsection we consider two different simulated intervals, α and β, of 60 seach. The different densities that show up along the segments of the 5-km section ofinterest are shown in Table 15.8. Such levels of service are derived according to themean density that is recorded during the evaluated intervals. As we can see, vehicledensity is not homogeneously distributed along the highway, so that drivers wouldfeel different level of services according to their current position within the highway.

Let us assume that a data message is to be routed along the 5 km of highway understudy. In order to guarantee an end-to-end path between a hypothetical source (beginof the area of interest) and destination (end of the area of interest), the underlyingtopology graph must be connected. For each time instant of the intervals α and β, we

Table 15.8 LOS in Different Segments of the One-Lane Simulated Highway

0– 250– 750– 1500– 2250– 2500– 3250– 4000–Interval 250 m 750 m 1500 m 2250 m 2500 m 3250 m 4000 m 5000 m

α F D D C C D C Bβ F D E D C C B A


Table 15.9 Minimum Communication Ranges to AchieveConnectivity in the One-Lane Simulated Highway

Interval r′avg (m) r′

min (m) r′max (m)

α 806.05 ± 18.23 766 1049β 1278.97 ± 66.74 742 1581

compute the minimum communication range r′ that is needed to obtain a connectedgraph (see Table 15.9).

In the case of interval α, communication is not possible when vehicles employ atransmission rate of 27 Mbps. In fact, r27 = 400 < r′

min = 766, so that a path betweensource and destination does not exist at any moment during the 60 s of α. With a datarate of 12 Mbps, a path exists during 48 s out of 60. Shortest paths in this caserange from 7 to 8 hops, with a mean value of 7.83 ± 0.11 hops. In the case of 6 and3 Mbps, a path remains during the whole interval (r3 > r6 > r′

max). Given that radioranges increase as the transmission rate decreases, the number of hops for the shortestpath also gets reduced: 5 and 4 hops, for 6 and 3 Mbps, respectively. To summarize,connectivity in a one-lane highway of moderately high density could be achieved, atleast when low transmission rates are employed (analogously, when high radio rangesare achieved).

On the other hand, radio range has a direct impact onto the number of neighborsof each vehicle, as expected. Clearly, the higher the communication range, the moreneighbors each vehicle has. In graph theory terminology, we say that the node degree δ

increases. However, node degree greatly varies depending on the position of vehiclesalong the highway. Vehicles tend to travel forming platoons, causing the heteroge-neous distribution of node density. For the best connected scenario, namely when3 Mbps are employed to transfer data, we have a mean degree δavg = 38.27 ± 1.79,with minimum δmin = 9 and maximum δmax = 54. Therefore, while low data ratesimprove potential connectivity, topology control algorithms are needed to reducethe number of interfering communications. Multichannel approaches as proposed bythe WAVE standard [55] also help alleviate this issue. However, the need for topo-logy control schemes prevails given that the potential number of neighboring vehicles(possibly participating in a communication task) is much higher than the number ofavailable orthogonal channels.

Interval β is more challenging from the connectivity viewpoint, since vehicle den-sity is lower in the last section of the highway (LOS B and A). As it happened withinterval α, no connectivity is possible at 27 Mbps. At 12 Mbps, a path exists onlyduring 3 s out of 60. For 6 Mbps, we obtain 24 s of connectivity, while 46 s of com-munications could be achieved at 3 Mbps. Given that r3, r6, r12, and r27 are upperbounds on the actual communication range that could exhibit a given deployment,it seems that a connected path of vehicles that traverses a highway could be hard toachieve.

Lower connectivity does not necessarily means lower mean degree. In fact, it ishigher in the 3 Mbps case δavg = 41.81 ± 2.00. However, connectivity gets broken


Figure 15.9 Simulated highway with two lanes.

when isolated platoons of vehicles are formed. In this particular case, some of theseplatoons are comprised of an individual vehicle, since δmin = 0.

We consider now that vehicles employ the store-carry-forward paradigm, so thatpackets can be transported by the vehicles themselves. Instead of a connected pathbetween source and destination, we look for a path over time (journey) between them.Again, we compute the minimum transmission range r′′ that is needed to deliver apacket. In the case of interval α, r′′ = 761 < r′

min = 766. This means that vehicles canwait for forwarding the packet until they are closer to the next hop, so that the minimumcommunication range which is needed gets slightly reduced. For interval β, r′′ =r′min = 742 m. Therefore, we can conclude that the store-carry-forward paradigm

does not suppose a high gain in the case of a one-lane highway. Let us focus next ona more realistic setup.

15.6.3 Connectivity in Two Lanes

In this case we extend the former highway with a second lane (Figure 15.9). The sametraffic flows are simulated, but this time the second lane allows for passing maneuvers.Let us see how this influences the connectivity of the underlying network.

Minimum communication ranges that are needed to obtain a connected path arelower in this case than when we consider a single lane (Table 15.10). This means thatthe network is more connected, since vehicles do not necessarily get stuck behind aslower car. They can overtake by using the leftmost lane.

Let us focus first on intervalα. As it happened previously, no path can be establishedat 27 Mbps. However, the network is connected during 42 s out of 60 s for 12 Mbps(r′

min < r12 < r′max), while it is always connected for higher bit rates. By using the

Table 15.10 Minimum Communication Ranges to AchieveConnectivity in the Two-Lanes Simulated Highway

Interval r′avg (m) r′

min (m) r′max (m)

α 750.32 ± 18.30 575 824β 837.45 ± 7.33 756 863


store-carry-forward paradigm, the minimum transmission range for connectivity overtime can be reduced to r′′ = 448 m.

As it happened in the one-lane case, interval β is less connected than α. At 12Mbps, just 9 out of 60 s feature a path between source and destination. However, it isalways connected for 6 and 3 Mbps (r3 > r6 > r′

max). The store-carry-forward doesnot help reduce the transmission power in this case, since r′′ = r′

min = 756 m.In any case, we would like to highlight that longer intervals (more than 60 s) would

allow the store-carry-forward approach reduce the number of transmissions along thehighway, as well as the required communication range. Obviously, those vehicles ap-proaching the destination can carry data until they are close to the destination. In suchmoment, they could forward the packet, at the cost of augmenting the communicationdelay.

15.7 CONCLUSION AND FUTURE WORK

Along this chapter we have tackled several aspects related to vehicular connectivityincluding also a taxonomy of the main vehicular simulators used within this researcharea.

We started this chapter dealing with the problem of modeling the movement ofvehicles along roads. The most common technique is the car following model, inwhich a vehicle reacts to the changes in direction and speed of the vehicle in frontof it. Other approaches model lane changing too, being able to describe the generalbehavior of these vehicles.

Vehicular simulators have also a lot of importance in this area because they arethe tools used by the researchers to obtain an idea of how these VANETs behave inlarge scenarios. Initially, VANETs were simulated by using network simulators thatreceived as input a mobility file with the positions and directions of the vehicles alongthe simulation. However, there are several research topics in which the interactionbetween a vehicle mobility simulator and a network traffic simulator is required.For example, this is the case of safety applications in which the reception of a datamessage can affect the movement of a vehicle. Thus, new integrated simulators weredeveloped in order to cover this new requisite.

Another important aspect related to simulations is how the wireless communica-tion between vehicles is modeled. In the beginning, the communication between twovehicles was possible if they were within a radio range. However, wireless signals donot behave so abruptly in reality. They suffer different phenomena that can prevent thesuccessful reception of a message even if a vehicle is within the intended radio range.Thus, new signal propagation models were developed to deal with the reception ofa message as a probability distribution. That is, the communication between vehi-cles will be possible according to a mathematical function that depends on differentparameters like the frequency of the signal, the distance between the two vehicles,and the like. If the resulting reception power is greater than a threshold value, thereception of the message is possible. Therefore, more realistic communications aremodeled obtaining more precise protocols.

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Finally, in the last section we have first analyzed the issue of obtaining the maxi-mum communication range that can be obtained using 802.11p under ideal connecti-vity conditions in vehicular networks. The simulations feature some interesting re-sults. Under ideal conditions without interferences and with the highest transmissionpower level, a node can send traffic up to 1700 m allowing the node to retransmit thepacket up to 7 times. The other aspect treated in the last section is the connectivityin two kind of scenarios. As a first step in our research, we have simulated only onelane of a road. Although this is an abnormal situation is a starting point to also verifythe correctness of our proposal. Within this scenario, we have simulated a traditionalstrategy and the store-carry-forward paradigm. Since platoons are commonly led bythe slowest vehicles (vehicles cannot overtake them), the connectivity will be relatedto the maximum distance between platoons. Thus, the store-carry-forward paradigmdo not feature a remarkable improvement with respect to the traditional one. On theother hand, simulating only one direction of a double-lane highway, the store-carry-forward paradigm increases the connectivity in nearly 100 m, also augmenting thedelay to deliver the messages to the destination.

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