Mobility Models for Vehicular Ad Hoc Network Simulations Niranjan Potnis
Atulya Mahajan
KCRG, Learning Systems Institute 2000,Levy Avenue, Suite 320 Tallahassee, Florida 32310 001-850-644-4720
Department of Computer Science Florida State University Tallahassee FL 32306 001-850-644-2525
npotnis@lpg.fsu.edu
mahajan@cs.fsu.edu generated by these models can be used by the network simulator ns2 [1] to carry out realistic simulations.
1. ABSTRACT Considerable amount of research has been going on in the area of Vehicular Ad-Hoc networks (VANETS), where vehicles moving along streets in an urban city establish a network among themselves. There has been an increasing commercial and research interest in the development and deployment of such networks and the routing protocols used in these networks. Unfortunately the current state of the art mobility models for VANET simulations do not reproduce the characteristic movement of vehicles on the urban streets. They do not take into consideration the constrained movement of vehicles subject to street boundaries, stop signs, traffic lights and obstacles like buildings.
2. BACKGROUND There are several mobility models proposed for mobile ad-hoc network simulations. The ones that are widely used in the network simulation are the Random Way point model [6] and the Random Direction model [3]. In the Random Waypoint model, every node chooses a random destination and the speed is uniformly chosen from a given range of minimum and maximum speed distribution. The node then moves to that destination with the chosen speed. It then rests for a certain pause time and repeats this process till the simulation continues. Through the study and analysis of this kind of movement Camp et al [3] mention that the node concentration or the node spatial distribution is towards the center of the simulation area as the simulation progresses. The nodes appear to converge and diverge repeatedly at the center.
Simulation is a very important tool for performance evaluation of ad-hoc networks. It enables us to conduct repeatable experiments under a controlled environment by isolating different parameters and varying them to study their affect on network performance. This would be highly costly and infeasible to accomplish in the real world. For close to accurate prediction of results, it is desirable that real world behavior is closely replicated while carrying out the simulations. The evaluation results need to be good predictors of protocol performance in the real world. The movement pattern of the nodes in the ad hoc networks is a factor that has a considerable impact on network performance. Mobility pattern is important in the sense that the position of nodes at any point of time, impacts the network connectivity which is central to the performance of the network. In this context, the mobility pattern of vehicles would play a crucial role in the performance evaluation of any VANET protocol done using simulations.
In the Random Direction Model the nodes chose a direction and a random speed and then move till a point close to the boundary of simulation area in that direction. The intention here is to avoid the center clustering as in case of Random Waypoint Model. A somewhat constant neighbor distribution is intended as the simulation progresses. Another model worth mentioning is the Boundless Simulation Area Mobility model in which the nodes move unobstructed by the simulation boundary. They wrap around and reappear on the other side of the boundary and continue their motion. This results in a torus shaped simulation area as against rectangular boundaries. All of these models mentioned above involve movement of nodes in an open free space. In case of vehicular ad hoc networks this is highly unrealistic. The movement of communicating nodes in this case is along streets and is highly restricted with other characteristics peculiar to vehicular motion.
Through this work, methods to capture the realistic mobility characteristics of vehicles on urban streets are proposed. These characteristics can then be used for carrying out more accurate simulations for VANETS. Two new simple mobility models that account for constrained movement patterns of vehicles on real world urban street maps are introduced. These two models incorporate the Stop Signs, Traffic Lights on the streets and interdependent motion of vehicles on same street. Also these traffic control mechanisms are enforced on real street maps available from US bureau database [2]. The mobility files
Jardosh et al. [5] introduced obstacles in the simulation area to constrain mobility as well as wireless transmission. Their model explores communication on college campuses where nodes tend to move through obstacles, congregate at attraction points or choose destinations decisively. The placement of obstacles is user defined and the paths are computed based on the chosen obstacle placement which may not be totally realistic. This work is specific to vehicular ad-hoc networks and hence it incorporates vehicular motion characteristics on real street maps.
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Worth mentioning here is the contribution of Saha et. al [7] who have modeled mobility for vehicular ad hoc networks on real street maps from TIGER database provided by the US bureau. Their work restricts the mobility of nodes along the streets but
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clustering effect. In turn, increased clustering would lead to higher delivery ratios (packets sent to packets received) when neighboring intersections are within transmission range of the nodes, and to lower delivery ratios when neighboring intersections are beyond each other’s transmission range. This further depends on the size of the city blocks in the maps used. The experiments that are being performed validate this hypothesis.
does not incorporate crucial characteristics of vehicular motion like stop signs, traffic lights at intersections, interdependent vehicular motion on the streets. As a consequence the results of their evaluation are close to the Random Waypoint model which is the open space model. The characteristics mentioned above are incorporated into the two models implemented in this work. Experiments on the maps from the same TIGER database would perform a comprehensive comparison of these models with the Rice University Model as well as the Random Waypoint model.
Following are the results for a simulation on a sample 1200mX1200m topology with block size of 200m X 50m.
A recent work is closely related to this study [4]. This work introduces an urban vehicular mobility model and its performance is analyzed. However the emphasis is not on the effect of node clustering at the intersections and its influence on mobility in the manner it is done here. Also the evaluation parameters chosen are such that the delivery ratios obtained are low and may not be useful in real world VANETS.
3. APPROACH AND UNIQUNESS Two mobility models that incorporate the various mobility intricacies of vehicles on urban streets have been implemented • The Stop Sign Model (SSM) • The Traffic Sign Model (TSM) These models are used as tools to generate the mobility files for the network simulator ns2. They operate on any given map and any number of vehicles to generate the mobility pattern of the vehicles along the streets in that map in format that can be fed to ns2. It is observed that the TSM and SSM models bring out a clustering effect at the intersections which significantly impacts protocol performance. The state-of-the-art models do not capture this effect.
It is believed that this work captures the effect of vehicle clustering on real streets in the context of vehicular mobility and vehicular network performance, using a novel approach. The two models described above serve as a good starting point to capture mobility of vehicles on urban streets for performance evaluation of Vehicular AdHoc Networks
3.1 The Stop Sign Model (SSM)
5. REFERENCES
This model simulates the mobility of vehicles in the presence of stop signs at every intersection. Every vehicle arriving at the intersection would wait for a fixed period of time before proceeding to its destination. This necessarily introduces queuing at the intersections. Also the motion of vehicles on the same street is interdependent. Every vehicle maintains a specific distance from the vehicle in front of it.
[1] The network simulator - ns-2. http://www.isi.edu/nsnam/ns/.
3.2 The Traffic Sign Model (TSM)
[4] David R. Choffnes and Fabin E. Bustamante. An integrated mobility and traffic model for vehicular wireless networks. In VANET ’05: Proceedings of the 2nd ACM international workshop on Vehicular ad hoc networks, 2005.
[2] Tiger - topologically integrated geographic encoding and referencing system http://www.census.gov/geo/www/tiger [3] T. Camp, J. Boleng, and V. Davies. A survey of mobility models for ad hoc network research. volume 2, pages 483– 502, 2002.
This model simulates the mobility of vehicles in presence of traffic lights at every intersection. It is a probabilistic model as a vehicle may or may not stop at a traffic light. A vehicle arriving at any intersections stops with a 50 percent probability. If the vehicle has to wait, the period of wait is chosen randomly from a distribution of 0 to maximum specified. Also if a vehicle waits at an intersection, all successive vehicles arriving at that intersection wait till it moves. This necessarily implies that vehicles queued at any intersection move together after elapse of the chosen wait time. In this model too a specific distance is maintained between two consecutive vehicles.
[5] Amit Jardosh, Elizabeth M. Belding-Royer, Kevin C. Almeroth, and Subhash Suri. Towards realistic mobility models for mobile ad hoc networks. In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking, pages 217–229, New York, NY, USA, 2003. ACM Press. [6] D. Johnson, D. Maltz, and J. Broch. DSR The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks.
4. RESULTS AND EXTENSION
[7] Amit Kumar Saha and David B. Johnson. Modeling mobility for vehicular ad-hoc networks. In VANET ’04: Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks, pages 91–92, New York, NY, USA, 2004. ACM Press
The TSM and SSM models bring out a clustering effect at the intersections which significantly impacts protocol performance. The hypothesis is that increasing the number of nodes or the maximum wait times at intersections would lead to increased
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