1. TSF: Trajectory-based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery in Vehicular Networks Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He, and David Du Department of Computer Science and Engineering June 23, 2010 ICDCS 2010

2. Intelligent Transportation Systems (ITS) 2  ITS provides the transport safety and efficiency through the computing and communications among transport systems.

3. Vehicle Trajectory 3  Vehicle follows the route provided by GPS-based navigation systems for efficient driving. GPS-based Navigation Vehicle Trajectory  Vehicle moves along its trajectory with bounded speed.

4. Road Network Layout 4 Road MapRoad Network Graph  Road network layout can be represented as road map.  This road map can be reduced to the road network graph.

5. Vehicular Traffic Statistics 5 Road Map Road Segment Vehicle Density  Vehicular traffic statistics can be measured per road segment.  Vehicle density can be measured by vehicle inter-arrival time.

6. Motivation We design Data Forwarding for Vehicular Networks based on these four characteristics of road networks:  Vehicle Trajectory  Road Network Layout  Vehicular Traffic Statistics Data Forwarding for Vehicular Networks  In this paper, we investigate the Data Forwarding for Infrastructure-to-Vehicle Data Delivery. 6

7. Problem Definition 7 Good Rendezvous Point !

8. Challenge in Reverse Data Forwarding 8 Target Missing! Inaccurate Delay Estimation The destination vehicle moves along its trajectory.

9. 9 Data Delivery by VADD from AP to Target Point Expected DelayActual DelayError 489 sec413 sec16% Expected STDActual STDError 10 sec139 sec1277% Difficult to deliver packets with these errors!

10. Packet Forwarding based on Stationary Nodes 10  Assume each intersection has a stationary node for packet buffering. 1. Source Routing to Target Stationary Node 2. Source Routing to Destination Vehicle

11. Target Point Selection 11 Miss! Hit!  Target point with a minimum delay and a high delivery probability. Minimum Delay Target Point

12. Design Challenges 12 How to model Packet Delay and Vehicle Delay?  Modeling of Packet Delay Distribution and Vehicle Delay Distribution as Gamma Distributions How to select an Optimal Target Point?  Optimal Target Point Selection Algorithm using the Distributions of Packet and Vehicle Delays

13. Link Delay Model 13 Case 1: Immediate Forward Case 2: Wait and Forward

14. Link Delay Model 14 Case 1: Immediate Forward Case 2: Wait and Forward Let d be the link delay for a road segment. 1. Expectation of link delay 2. Variance of link delay Case 1 Case 2

15. Link Delay Distribution Link Delay is modeled as Gamma Distribution: 15 Where

16. End-to-End Packet Delay Model 16

17. Vehicle Delay Model 17

18. Delay Distributions for intersection i Optimization Optimal Target Point Selection 18

19. Performance Evaluation Simulation Setting  Road Network: 5.1miles x 5.6 miles (49 intersections)  Communication Range: 200 meters (656 feet) Performance Metrics  Average delivery delay  Packet Delivery ratio Baselines compared with TSF  Random Trajectory Point (RTP)  Last Trajectory Point (LTP) 19

20. CDF Comparison for Delivery Delay 20

21. Impact of Vehicle Density 21  For TSF, as the more vehicles exist, 1.The shorter delivery delay is obtained and. 2.The higher delivery ratio is obtained.

22. Impact of Delivery Probability Threshold 22  For TSF, as the threshold α increases, 1.The delivery delay increases and. 2.The delivery ratio increases.

23. Conclusion This paper designs a trajectory-based statistical data forwarding tailored for vehicular networks,  Considering road network characteristics: Vehicle Trajectory Road Network Layout Vehicular Traffic Statistics As future work, we will continue to investigate vehicle trajectory for vehicular networking:  Data Forwarding, Data Dissemination, and Vehicle Detouring Protocol. 23