Topic: Vehicular Networks

1. Topic: Vehicular Networks Team 6 R99922041 陳彥璋 R99922083 梁逸安 R99945051 洪晧瑜

2. CARS: Context-Aware Rate Selection for Vehicular Networks P. Shankar , T. Nadeem , J. Rosca , L. Iftode , Proc. IEEE ICNP , Oct. 2008 , pp. 1 – 12 Speaker: 陳彥璋

3. Outline • Introduction • Key challenges • Context Aware Rate Selection • Performance • Conclusion

4. Rate Selection • IEEE 802.11 allows multiple transmission rate at the physical layer (PHY).

5. Link Quality High link quality. Low link quality due to long distance.

6. Outline • Introduction • Key challenges • Context Aware Rate Selection • Performance • Conclusion

7. Key challenges • Rapid variations of the link quality. • Mobility at vehicular speed.

8. Key challenges • Few or no packets transmitted in estimation window during infrequent and burstytransmission. • No past history to estimate link quality.

9. Key challenges • Distinguish losses due to environment from hidden-station induced collision. • Loss due to hidden station: rate↓ transmission time↑ contention↑

10. Outline • Introduction • Key challenges • Context Aware Rate Selection • Performance • Conclusion

11. Architecture Positions and speeds of itself and its neighbors. Past transmission history.

12. Algorithm for all rate do Context information Transmission rate Packet length Past frame transmission statistics input input Ec Eh α (1-α) Packet error rate αis assigned based on the vehicle speed. Throughput end for Find the rate that maximize the throughput.

13. Algorithm Ec Empirical model. Measurements from extensive outdoor vehicular experiments. Use multivariate linear regression as the learning approach. Eh Exponentially weighted moving average (EWMA) of past frame transmission statistics. http://en.wikipedia.org/wiki/File:Exponential_moving_average_weights_N%3D15.png

14. Outline • Introduction • Key challenges • Context Aware Rate Selection • Performance • Conclusion

15. Experimental Result

16. Simulation Result

17. Outline • Introduction • Key challenges • Context Aware Rate Selection • Performance • Conclusion

18. Conclusion • Context Aware Rate Selection • Use context information to perform fast rate adaption in vehicular network.

19. Connectivity-Aware Routing (CAR) in Vehicular Ad Hoc Networks Valery Naumov & Thomas R. Gross ETH Zurich, Switzerland IEEEINFOCOM 2007 speaker:梁逸安

20. Outline • Introduction • Related Works (GPSR) • Connection-Aware Routing (CAR) • Simulation • Conclusion

21. Introduction • Vehicular ad hoc networks (VANETs) using 802.11-based WLAN technology have recently received considerable attention in many projects • Several geographic routing (GR) protocols use an idealized mechanism such that for every originated data packet the true position of the destination is known

22. Introduction • Another problem is that, all of the GR protocols do not take into account if a path between source and destination is populated. • This paper presents a novel position-based routing scheme called Connectivity-Aware Routing (CAR) to address these kind of problems

23. Outline • Introduction • Related Works (GPSR) • Connection-Aware Routing (CAR) • Simulation • Conclusion

24. Greedy Perimeter Stateless Routing

25. Greedy Perimeter Stateless Routing • Perimeter Mode

26. Greedy Perimeter Stateless Routing

27. Outline • Introduction • Related Works (GPSR) • Connection-Aware Routing (CAR) • Simulation • Conclusion

28. Connection-Aware Routing (CAR) • The CAR protocol consists of four main parts: • (1) destination location and path discovery • (2) data packet forwarding along the found path • (3) path maintenance with the help of guards • (4) error recovery

29. Destination location discovery • A source broadcast a path discovery (PD) • Each node forwarding the PD updates some entries of PD packets • If two velocity vectors’angle > 18°, anchor is set.

30. Greedy forwarding over the anchored path • A neighbor that is closer to the next anchor point is chosen (greedy) , instead of destination.

31. Path maintenance • If an end node (source or destination) changes position or direction, standing guard will be activated to maintain the path.

32. Path maintenance • If end node changes direction against the direction of communication, traveling guard will be activated. • A traveling guard runs as end node’s old direction and speed, and reroute the packets to the destination.

33. Path maintenance

34. Routing error recovery • The reason for routing error • A temporary gap between vehicles • (1) Timeout algorithm • When a node detects a gap – buffer the packets • (2) Walk-around error recovery • When Timeout algorithm fail , do location discovery • Whether the location discovery is successful, the result will be reported to the source node.

35. Outline • Introduction • Related Works (GPSR) • Connection-Aware Routing (CAR) • Simulation • Conclusion

36. Simulation • Scenarios • City • Highway • Traffic density • Low – less than 15 vehicles/km • Medium – 30-40 vehicles/km • High – more then 50 vehicles/km

37. Simulation-Packet Delivery Ratio

38. Simulation-Average data packet delay

39. Simulation-Routing overhead

40. Outline • Introduction • Related Works (GPSR) • Connection-Aware Routing (CAR) • Simulation • Conclusion

41. Conclusion • Address the populated problem about paths. • Path discovery & Anchor points • Path maintenance with guards • Error recovery • Higher performance and lower routing overhead than GPSR

42. Delay-bounded Routing in Vehicular Ad-hoc Networks AntoniosSkordylis, NikiTrigoni Oxford University Computing Laboratory ACM International Symposium on Mobile Ad hoc Networking and Computing, 2008 Speaker: R99945051 洪晧瑜

43. Outline • Introduction • VANETs • Delay-bounded Routing • Objective and Model • Algorithm • D-Greedy • D-MinCost • Evaluation and result • Conclusion

44. Introduction • VANETs • vehicles equipped with wireless transceivers that will enable them to communicate with each other form a special class of wirelessnetwork • Delay-bounded Routing • timely and bandwidth efficient data dissemination from vehicles to an access point, given statistical information about road traffic • tradeoff : timely data delivery v.s. low bandwidth utilization

45. Outline • Introduction • VANETs • Delay-bounded Routing • Objective and Model • Algorithm • D-Greedy • D-MinCost • Evaluation and result • Conclusion

46. Objective • Objective • carry-and-forward algorithms leverage knowledge of traffic statistics in anurban setting • enable timely delivery of messages from vehiclesto APs • minimizing wirelesstransmissions/optimizing bandwidth utilization

47. Model • Urban scenario • Vehicles (mobile nodes ): • geographical position(GPS receiver ) • digital map(G(V,E)):historical traffic statistics • u: average speed, • d: average vehicle density per road segment • communication range: 250m • APs(stationaryaccess points ): • infrastructure nodes whose absolute location in known to all vehicles • Message informations: • tg : message generation time • λ : time-to-live value, message delay threshold

48. Outline • Introduction • VANETs • Delay-bounded Routing • Objective and Model • Algorithm • D-Greedy • D-MinCost • Evaluation and result • Conclusion

49. Algorithm • Forwarding a message • minimize the number of transmissions • within the message-specific delay threshold • Alternate between two forwarding strategies: • Multihop Forwarding • Data Muling • Algorithms • D-Greedy • D-MinCost

50. D-Greedy • Delay-bounded Greedy Forwarding • No knowledge of global traffic conditions • Available location information, ex. Node speed • Best path: shortest path