Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He, and David Du

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

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

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

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

5. Vehicular Traffic Statistics Road Map Road Segment • Vehicular traffic statistics can be measured per road segment. Vehicle Density 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.

7. Problem Definition Good RendezvousPoint !

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

9. Data Delivery by VADD from AP to Target Point Difficult to deliver packets with these errors!

10. Packet Forwarding based onStationary Nodes • 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 • Target point with a minimum delay and a high delivery probability. Hit! Hit! Target Point Hit! Minimum Delay Miss! Miss! Miss!

12. Design Challenges • 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 Case 1:Immediate Forward Case 2:Wait andForward

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

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

16. End-to-End Packet Delay Model

17. Vehicle Delay Model

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

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)

20. CDF Comparison for Delivery Delay

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

22. Impact of Delivery Probability Threshold • For TSF, as the threshold α increases, • The delivery delay increases and. • 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.