Performance Evaluation of Vehicular DTN Routing under Realistic Mobility Models

1. Performance Evaluation of Vehicular DTN Routingunder Realistic Mobility Models Pei’en LUO

2. Abstract • In performance studies of vehicular ad hoc networks (VANETs), the underlying mobility model plays an important role. Since conventional mobile ad hoc network (MANET) routing protocols do not work efficiently in vehicular environments due to the rapid topology changes, the Delay-Tolerant Network (DTN) model is often applied. • In this paper, we construct a new mobility model, the Shanghai Urban Vehicular Network (SUVnet) model by using the GPS data from more than 4,000 taxis we have collected, and then investigate the performance of two kinds of DTN routing, the non-geographic pure epidemic routing and our newly-proposed geographic DTN routing, the Distance-Aware Epidemic Routing (DAER). • We use the popular random waypoint mobility model and a more complex microscopic traffic simulator generated model for performance comparison. With the two considered DTN routing protocols, conventional mobility models tend to give higher performance results than SUVnet model, the presumably more realistic mobility model.

3. Contents • Issue with mobility models • The SUMO project • The SUVNet • DTN Routing • Performance simulation • Conclusion

4. Issue with mobility models • Most previous works generate traces by probabilistic methods: RWP, RD…speed of mobile nodes is chosen randomly and constantly • A vehicle does NOT move freely in a specified area • Vehicles are NOT always moving at a constant speed • Traffic rules also limit the movements of vehicles

5. Issue with mobility models • More complex mobility models are developed • Limit nodes' movements in defined grids • Car-to-car interaction • Several tools & projects: BonnMotion tool, GEMM tool, MONARCH project

6. The SUMO project • The Simulation of Urban MObility (SUMO) is an open source, microscopic, space-continuous traffic simulator designed to handle large road networks. • The car microscopic movement model in SUMO is a car following model and includes a stochastic traffic assignment modeled by a probabilistic route choice according to driver models.

7. The SUMO project • Generate traces from SUMO: • Build our road network description with the city map data • Describe explicit vehicle routes • Perform the traffic simulation and dump the mobility output trace

8. The SUVNet We try to obtain a trace data set from the live real GPS data received

9. The SUVNet -Methodology • Data collection • ~4,000 taxis • 20-40 sec interval • Coordinate, Timestamp, Direction, ID

10. The SUVNet -Methodology • Map-matching • Locating GPS data onto a road network map • Choose the road segment with least distance to a taxi and must comply with the headway direction • Error may occur

11. The SUVNet -Methodology • Route-finding • Find the route between two data points • Choose the suitable (possible) route • Reasonable accuracy: 92%

12. DTN Routing • Conventional Epidemic Routing • a source node tries to transfer its bundles to as many neighbors as possible in order to increase the probability of a successful delivery. • Distance-Aware Epidemic Routing considers… • The order of bundles to be forwarded • The number of duplications • The buffer replacement policy

13. DTN Routing

14. Performance simulation • Simulation Setup • We examine the performance of two DTN routing, pure epidemic and DAER, in three mobility models: random waypoint, SUVnet and SUMO-generated model. • Since we find there are about 600 vehicles that stay in the inner-loop area for 1,200 seconds, so we generate the same number nodes' traces for all three models. • The average speed of nodes in random waypoint trace is 25km/h, similar to those in SUVnet. • Network traffic is generated as follows: for light load, 100 bundles are generated and each node has 16MB buffer; for heavy load, 500 bundles are generated and each node has 2MB buffer, bundle size is 256kB. • All bundles are sent at the beginning. The transmission rate is 1Mbps and communication range is set to 150m for all cases. Simulation endures 1,200 seconds and is done with our own built DTN simulator.

15. Performance simulation • Light Load Scenario

16. Performance simulation • High Load Scenario

17. Performance simulation • Difference is not significant for pure epidemic routing. Its delivery ratio is quite close for different mobility models. • DAER performs much better in random waypoint model than the other two. • Random waypoint model tends to give lowest average bundle delay and number of hops. Choice of mobility models will play a significant role in simulation based study for DTN routing.

18. Conclusion • We study the performance of DTN epidemic routing under three mobility models, the random waypoint, SUVnet and SUMO-generated models. • To construct a more realistic model, the SUVnet, we collected the GPS data from more than 4,000 taxis in Shanghai and generated their traces. • Our evaluation shows the performance of DTN routing in VANET depends highly on the underlying mobility models. • We argue from our results, that even by means of a complex map-based microscopic traffic simulator, care should be taken as the results obtained with these models might not be as close to reality as expected.

19. Resources • SUMO project: • http://sumo.sourceforge.net/ • Shanghai digital map: • http://itis.grid.sjtu.edu.cn/Run.htm • Shanghai taxi trace data: • http://itis.grid.sjtu.edu.cn/wiki/download.htm