A Realistic Mobility Simulator for Vehicular Ad Hoc Networks 2

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1 Institut Eurécom 1 Department of Mobile Communications Sophia-Antipolis France Politecnico di Torino Dipartimento di Elettronica Torino Italy Research Report RR A Realistic Mobility Simulator for Vehicular Ad Hoc Networks 2 October 28 th, 2005 Jérôme Härri, Marco Fiore, Fethi Filali, Christian Bonnet, Claudio Casetti, Carla-Fabiana Chiasserini Tel : (+33) Fax : (+33) Tel : (+39) Fax : (+39) {Jerome.Haerri,Fethi.Filali,Christian.Bonnet}@eurecom.fr {Marco.Fiore,Claudio.Casetti,Carla.Chiasserini}@polito.it 1 Institut Eurécom s research is partially supported by its industrial members: Bouygues Télécom, France Télécom, Hitachi Europe, SFR, Sharp, ST Microelectronics, Swisscom, Texas Instruments, Thales. 2 This work has been supported partially by the European Community through the NoE NEW- COM, partially by the NoE EuroNGI, and partially by the Institut Eurécom and the Politecnico di Torino.

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3 A Realistic Mobility Simulator for Vehicular Ad Hoc Networks Jérôme Härri, Marco Fiore, Fethi Filali, Christian Bonnet, Claudio Casetti, Carla-Fabiana Chiasserini Abstract Vehicular Ad-hoc Networks (VANETs) have been recently attracting increasing attention from both the research community and the world of industry. Many related projects have been developed, involving analytical modeling of vehicular traffic, simulation of vehicular environments and real world measurements. Regardless of this effort, no really complete simulation tool is today freely available to researchers wishing to simulate VANETs. The goal of our framework within Newcom JPA3 is to provide such a tool. This paper presents the framework, ideas and methodologies at the basis of our work, which is currently ongoing. Index Terms Mobility modeling, framework, macromobility, micromobility, vehicular ad hoc networks, VANET.

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5 Contents 1 Introduction 1 2 Preliminaries Related work Proposed framework The NEWCOM Mobility Simulator: A Realistic Model Framework outline Random topology generation Intersection management Conclusions and Future Work 9 v

6 List of Figures 1 Proposed concept map of mobility model generation for inter-vehicle communications Illustration of the random topology generation The NEWCOM Mobility Simulator vi

7 1 Introduction One emerging new type of ad hoc networks is vehicular ad hoc networks (VANETs), in which the network nodes are vehicles. VANETs are a particularly challenging class of Mobile Ad Hoc Networks (MANETs), characterized by high node mobility but with limited degrees of freedom in the mobility patterns. While it is crucial to test and evaluate protocol implementations in a real testbed environment, simulations are still commonly used as a first step in any protocol development as well as for validation of analytical models for VANETs. While simulating VANETs, it is important to use mobility models that reflect as closely as possible the real behavior of these systems. In their studies, researchers often use random mobility models, which may be unsuitable for VANETs. Unfortunately, no complete, realistic models of vehicular mobility are currently freely available to researchers. In this paper, we present the framework for a realistic mobility simulator that is compliant with the principles of mobility model generation described in [5]. Our objective within the NEWCOM project is to provide the research community interested in vehicular networks with a tool that models, as realistically as possible, the particular motion behavior of motor-vehicles. Among the features of our tool are a realistic random vehicular topology generation, an improved micromobility modeling and an enhanced intersection management. The rest of the paper is organized as follows. Section 2 briefly discusses some related work and describes the framework for mobility model generation proposed in [5]. Section 3 outlines the main features of the tool we are developing and describes what has been already completed. Finally, Section 4, draws some conclusions and highlights on-going and future work. 2 Preliminaries In this section, we review some related work in vehicular mobility modeling. Next, we give a short description of the proposed framework; for a more detailed description, refer to [5]. 2.1 Related work When mobility was first taken into account in simulation of wireless networks, several models to generate mobility patterns of nodes were proposed. The Random Waypoint model, the Random Walk model, the Reference Point Group (or Platoon) model, the Node Following mode, the Gauss-Markov model, just to cite the most known ones, all involved generation of random linear speed-constant movements within the topology boundaries. Further works added pause times, reflection on boundaries, acceleration and deceleration of nodes. Simplicity of use conferred success to the Random Waypoint model in particular, however, the intrinsic nature 1

8 of such mobility models may produce unrealistic movement patterns when compared to some real world behavior. As far as Vehicular Ad-hoc Networks (VANETs) are concerned, it soon became clear that using any of the aforementioned models would produce completely useless results. Consequently, the research community started to seek more realistic models. The simple Freeway model and Manhattan (or Grid) model were the initial steps, then more complex projects were started involving the generation of mobility patterns based on real road maps or monitoring of real vehicular movements in cities. However, in most of these models, only the macromobility of nodes was considered. Although car-to-car interactions are a fundamental factor to take into account when dealing with vehicular mobility [1], little or no attention was paid to micromobility. More complete and detailed surveys of mobility models can be found in [2 5]. Recently, new open-source tools became available for the generation of vehicular mobility patterns. Most of them are capable of producing traces for network simulators such as ns-2 [6], GloMoSim [7] or OpNet [8]. In the rest of this section, we review some of these tools, in order to understand their strengths and weaknesses, and eventually indicate which one we selected as the starting point for our framework. Notice that, in the following survey, we did not take into consideration many tools that are not open-source or freely distributed; a complete review and comparison of such tools can be found in [9]. The IMPORTANT tool [10], and the BonnMotion tool [11] implement several random mobility models, plus the Manhattan model; no micromobility is considered and the structure of the tools is definitely non-compliant with the framework we propose. Thus, they are inadequate to simulate realistic vehicular mobility, and cannot be extended to generate more complex movement traces. The mobility traces used by Saha et al. [12] are obtained by monitoring buses movement patterns in Seattle, WA. Despite the realism of this approach, it is completely different from our driving idea, since we want to be able to generate realistic, although pseudo-random, movement of nodes. In another paper ( [13]), the same authors proposed a tool to extract road topologies from real road maps obtained from the TIGER database [14]. The possibility of generating topologies from real maps is considered in our framework, but we also seek alternate solutions, such as random or manual topology generation. Moreover, the complete absence of micromobility support makes it difficult to extend the tool to a complete mobility generator. The Mobility Model Generator for Vehicular Networks (MOVE) was recently presented as an on-going work [15]. It seems a quite complete tool, featuring real map extrapolation from the TIGER database as well as pseudo-random and manual topology generation. An efficient Graphical User Interface (GUI) is also provided. However, no micromobility is considered, and the in-progress status of the project discouraged us from considering this tool as a possible starting point for the implementation of our framework. 2

9 The Street Random Waypoint (STRAW) tool [16] is a mobility simulator based on the freely available Scalable Wireless Ad Hoc Network Simulator (SWANS) [17]. Under the point of view of vehicular mobility, it provides road topology extraction from the maps of the TIGER database, as well as micromobility support. STRAW also contains implementations for several transport, routing and media access protocols, since they are not present in the original SWANS software. The main drawback of the tool is the very limited diffusion of the SWANS platform, which is in contrast with our aim to develop a tool as widely accessible as possible. In other words, we preferred to have a standalone tool capable of producing traces which can then be used by different well-known network simulators, rather than an integrated mobility and traffic simulator such as STRAW. The GrooveSim tool [18] is a mobility and communication simulator, which again uses files from the TIGER database to generate realistic topologies. Being a self-contained software, GrooveSim neither models vehicles micromobility, nor produces traces usable by network simulators. As a matter of fact, it was developed to test a specific routing protocol exploiting the capability of the software to mix simulated and real car movements. However, its lack of micromobility, and its self-contained nature, make this tool unfit for the proposed framework. Finally, the CanuMobiSim tool [19,20] is a tool for the generation of movement traces in a variety of conditions. Extrapolation of real topologies from detailed Geographical Data Files (GDF) are possible, many different mobility models are implemented, a GUI is provided, and the tool can generate mobility traces for ns-2 and GloMoSim. Unlike many other tools, the CanuMobiSim tool keeps micromobility in consideration, implementing several car-to-car interaction models such as the Fluid Traffic Model [21] or the Intelligent Driver Model (IDM) [22]. Moreover, the general approach of the tool, even if in a simplified way, fits our framework. As a consequence, we decided to use the CanuMobiSim tool as the starting point for our framework. 2.2 Proposed framework In the literature, vehicular mobility models are usually classified as either microscopical or macroscopical [1]. When focusing on a macroscopic point of view, motion constraints such as roads, streets, crossroads, and traffic lights are considered. Also, the generation of vehicular traffic such as traffic density, traffic flows, and initial vehicle distributions are defined. The microscopic approach, instead, focuses on the movement of each individual vehicle and on the vehicle behavior with respect to others. Yet, this micro-macro approach is more a way to analyze a mobility model than a formal description. Another way to look at mobility models is to identify two functional blocks: Motion Constraints and Traffic Generator. Motion Constraints describe how each vehicle moves (its relative degree of freedom), and is usually obtained from a topological map. Macroscopically, motion constraints are streets or buildings, but microscopically, constraints are modeled by neighboring 3

10 Attraction/ Repulsion Points Speed Constraints Improve Car s type and particularities Describe cars capabilities Centers of Interest Determine initial location Determine preferred motion Social Habits Mobility Predictions Obstacles Topological Maps Car Generation Engine Driver Behavior Engine Describe mutual behaviors Describe Time Patterns n Compose 1 n Drivers Danger Assessments Motion Constraints Alter Traffic Generator 1 Compose 1 Mobility Model 1 Figure 1: Proposed concept map of mobility model generation for inter-vehicle communications cars, pedestrians, or by limited roads diversities either due to the type of cars or to drivers habits. The Traffic Generator, on the other hand, generates different kinds of car, and deals with their interactions according to the environment under study. Macroscopically, it models traffic densities or traffic flows, while microscopically, it deals with properties like inter-distances between cars, acceleration or braking. The framework we proposed in [5] stated that a realistic mobility model should include: Accurate and Realistic topological maps including different categories of streets and associated velocities. Smooth deceleration and acceleration. Obstacles including both mobility and wireless communication constraints. Attraction points including preferred roads depending on drivers habits. Simulation time performed on particular driving patterns such as Morning and evening rush hours, Lunch Break, or Night life. Non-random distribution of vehicles between homes, offices, or shopping malls; in other words: center of interests. Traffic generator controlling vehicles mutual interactions such as overtaking, traffic jam, preferred paths. We graphically illustrated our approach by a concept map for vehicular mobility models, as depicted in Figure 1. 4

11 3 The NEWCOM Mobility Simulator: A Realistic Model CanuMobiSim, although being more complete than many other tools, lacks several of the desired features for our tool. In this section, we point them out and outline the main directions our framework will take. We also briefly describe those parts of the framework that we have already completed and those that are still ongoing. 3.1 Framework outline The main difference between CanuMobiSim and our tool is the absence of a pseudo-random topology generator. In absence of GDF files, the only choice is to manually generate graphs describing road topologies, which could be a timeconsuming, tiresome task. Thus, our first goal is to provide a tool capable of randomly generating realistic topologies. Secondly, our attention goes to the micromobility models, and, since Treiber s IDM appears to be the most realistic one, we focus on enhancing such a model. Car-to-car interaction are well modeled by the IDM, but, since this is a model operating on single-lane, straight road, no realistic management of vehicles behavior is performed at road intersections. This turns out to be a major limitation, because, in real VANETs, the presence of intersections with traffic lights or roundabouts can lead to very particular nodes distributions, which should not be ignored. Thus, as far as micromobility is concerned, our priority is the implementation of an intersection management. Next, we plan to extend both macromobility and micromobility at once, by introducing multiple-lane roads. This would affect the description of the roads topology and would allow us to implement a vehicle overtaking model, such as MOBIL [23]. Also, this step introduces interesting problems in the intersection management, where multiple lanes must be handled. Finally, further extensions are planned, for instance i) the generation of trips based on Points of Interest instead of the current random ones, ii) the presence of classes of vehicles with different mechanical (e.g., speed, acceleration, deceleration) and driving (e.g., selfishness, attractive Points of Interest) characteristics, and iii) the introduction of the hour of the day, which affects the number and distribution of vehicles. In the following, we give a more detailed description of the completed and on-going CanuMobiSim enhancement activities, random topology generation and intersection management, respectively. 3.2 Random topology generation As mentioned in the previous section, CanuMobiSim is able to extract an urban topology from precise GDF files. However, with the exception of US topological maps, access to those information are restricted, further limiting research teams 5

12 ability to simulate accurate vehicular mobility. The objective of this part is precisely to solve this issue by generating random urban topologies whose features remain as close as possible to real urban configurations. Roughly described, an urban topology is a graph where vertices and edges represent, respectively, junction and road elements. As proposed by [25], a good solution to randomly generate graphs on a particular simulation area is Voronoi tessellation. We therefore begin by distributing points over the simulation area, representing obstacles (e.g., buildings). Then, we draw the Voronoi domains, where the Voronoi edges represent roads and intersections running around obstacles. Accordingly, we obtain a planar graph representing a set of urban roads, intersections and obstacles. Although being an interesting feature, these graphs too lack realism. Indeed, the distribution of obstacles should be fitted to match particular urban configurations. For instance, dense areas such as city centers have a larger number of obstacles, which in turn increases the number of Voronoi domains. By looking at topological maps, we can see that the density of obstacles is higher in presence of points of interests. To address these issues, our tool generate clusters of obstacles with different densities, which in turn creates clusters of Voronoi domains. Figure 2(a) presents a random topological map with uniformly spread obstacles, while Figure 2(b) depicts a topological map considering three different types of clusters with different obstacle densities. Accordingly, we show in Figure 3 a screen-shot of the proposed NEWCOM Mobility Simulator using a cluster-based topology, where dark dots followed by a pound sign represent the vehicles evolving on the topological map. (a) Uniform Topology (b) Cluster Topology Figure 2: Illustration of the random topology generation 6

13 3.3 Intersection management We are currently developing a realistic intersection management. In the Intelligent Driver Model no attention is paid to vehicles interactions in presence of intersections, as cars just drive through them, and possibly collide (see Figure 3). To model a more realistic traffic behavior, we are enhancing the existing IDM implementation to support intersection management. To this end, we first need to introduce deceleration and acceleration in proximity of road intersections, so that vehicles approaching a traffic light or a crossroad reduce their speed or stop. We took inspiration from Akcelik s acceleration/deceleration model [24] to derive our own model, described below. Figure 3: The NEWCOM Mobility Simulator At each time step t, vehicle i checks what its distance from the ahead intersection will be at next time step t + 1, d i t+1. Then it computes the minimum distance d i stop needed to come to a full stop at its current speed, with the comfortable deceleration specified by the IDM. If we have: d i t+1 < di stop then vehicle i enters intersection management mode, starting to decelerate, since otherwise it would not be able to stop or slow down in time. Once in intersection management mode, the IDM behavior is overridden, and, at each time step, the speed of vehicle i is changed according to the information that the micromobility model receives from the macromobility topology. In case of green traffic light, the vehicle maintains its speed, whereas in presence of red or yellow traffic lights, it decelerates in a way to stop right at the intersection. 7

14 When approaching an intersection a vehicle stops and acts depending on the presence of other nearby vehicles. When vehicle i arrives at the intersection, the micromobility model keeps interacting with the macromobility model, determining when it is allowed to move again (e.g., when the traffic light turns green from red, or when there is space in the roundabout), thus restoring IDM control and accelerating again to the desired speed. Notice that this scheme requires interaction between the micromobility model and the macromobility road topology, a feature which is rarely present in tools for the generation of mobility patterns. 8

15 4 Conclusions and Future Work We presented a framework for realistic mobility modeling, involving complex features that cannot be found in similar tools freely available today. We surveyed existing tools, and we chose the CanuMobiSim tool as a starting point to implement it. We outlined the major features of our tool, highlighting the tasks that we already implemented as well our on-going and future work. Specifically we are planning to extend our framework to include i) multiple-lane roads, ii) the generation of trips based on Points of Interest, iii) different classes of vehicles and iv) the hour of the day, which affects the number and distribution of vehicles. 9

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