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Geographic Routing in Vehicular Ad Hoc Networks (VANETS)

Geographic Routing in Vehicular Ad Hoc Networks (VANETS). Kevin C. Lee Computer Science Department University of California, Los Angeles Chair – Professor Mario Gerla. Outline. Overview of geographic routing Summary of previous work

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Geographic Routing in Vehicular Ad Hoc Networks (VANETS)

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  1. Geographic Routing in Vehicular Ad Hoc Networks (VANETS) Kevin C. Lee Computer Science Department University of California, Los Angeles Chair – Professor Mario Gerla

  2. Outline • Overview of geographic routing • Summary of previous work • Present LOUVRE Histogram-based density estimation approach • Report GeoDTN+Nav new results

  3. Greedy Mode • Nodes learn 1-hop neighbors’ positions from beaconing • A node forwards packets to its neighbor closest to D • Greedy traversal not always possible! x is a local maximum to D; w and y are further from D

  4. Recovery/Perimeter Mode z y • Face traversal by right-hand rule • Face change x D D F4 C F3 I3 F2 A E I2 F1 Walking sequence: F1 -> F2 -> F3 -> F4 I1 B S

  5. Planarization • Face traversal requires planar graph: cross edges result in routing loops • GG and RNG planarization algorithms • Their disadvantages • Planarization overhead • High hop count • Unit disk assumption, GPS accuracy, etc

  6. Outline • Overview of geographic routing • Summary of previous work • Present LOUVRE Histogram-based density estimation approach • Report GeoDTN+Nav new results

  7. TO-GO[1, 2] • Eliminate planarization overhead – Roads naturally formed a “planar” graph • Improve routing efficiency – Packets stop @ the junction only when necessary (aka junction lookahead) • Improve packet delivery – Opportunistic forwarding whenever possible Opportunistic routing toward the target Perimeter forwarding using greedy forwarding Packet skipping a junction node if not changing direction

  8. GeoCross[3] • Motivation: Empty intersection -> routing loop -> low packet delivery Routing loop!!

  9. GeoCross Basic Operations S, R1, [R1R2], R2, B, R3, C, R4, D, R5, [R5R6], R6, E, R7, F, R8, B => No cross link, continue forwarding Can’t forward b/c UR: [R5R6] S, R1, [R1R2], R2, B, R3, C, R4, D, R5, [R5R6], R6, E, R7, F, R8, B, R2, [R2R1], R1, S UR: [R5R6], continue existing loop Packet reaches destination

  10. LOUVRE[4] D ? • Recovery mode often expensive; backtracking takes too many steps • Use P2P density information to guide packet routing • LOUVRE: end-to-end routing solution that eliminates recovery forwarding completely Road 1 S 3 3 3 0 3 0 0 s s s 5 0 5 Density > Thresh = 3 3 Overlay routes 3 2 3

  11. Limitations & Previous Work • TO-GO: • No planarizaton overhead by taking roads that naturally formed a planar graph • Improve efficiency by junction-lookahead • Opportunistic forwarding to improve packet delivery • GeoCross: Takes care of loop-inducing cross links • LOUVRE: Peer-to-peer density estimation to avoid dead ends and backtracking

  12. Outline • Overview of geographic routing • Summary of previous work • Present LOUVRE Histogram-based density estimation approach • Report GeoDTN+Nav new results

  13. Drawback of the LOURVRE’S P2P Density Estimation Scheme • Not scalable • The memory overhead increases with the number of nodes • Not accurate • Density does not correlate well with connectivity when it is not uniform NOT CONNECTED

  14. Histogram-Based Density Discovery Algorithm[5] • Break up the roads into segments • Nodes within a segment keep track of unique # of cars they have seen in P2P fashion • Nodes receive broadcast beacons to update segment densities in the other segments • Road is connected if Segment center A B C D Segment 1 Segment 3 Segment 2 Segment 4

  15. Advantages of Histogram-Based Approach • Scalable • E.g. 1500-meter road, 250-meter segment length • Only need 6 integers for 6 segments (1500/250) • P2P can only store 6 cars, not enough • More accurate • Each segment size is smaller than the road length • Connectivity correlates better with segment density than road density NOT CONNECTED

  16. Evaluation • Connectivity accuracy between P2P and histogram-based approach • Road Percentage Connectivity (RPC) vs. Connectivity Accuracy (CA) • If road is connected, CA = RPC • If road is not, CA = 1 – RPC • Broadcast overhead between P2P and histogram-based approach • 1,000 realistic mobility traces

  17. Connectivity Accuracy between P2P and Histogram • P2P underperforms when density is low • This is due to the clustering behavior at two ends of a road

  18. Broadcast Overhead between P2P and Histogram • P2P has scalability issue as it needs to keep track of unique cars

  19. Outline • Overview of geographic routing • Summary of previous work • Present LOUVRE Histogram-based density estimation approach • Report GeoDTN+Nav new results

  20. GeoDTN+Nav Motivation [6,7] • Current geographic routing protocols assume connected networks • Connectivity not always guaranteed • Intermittent connectivity possible: • Low vehicle density • Obstacles • Temporal evolving traffic pattern

  21. Which Node? • Basic idea: Exploit mobility to help deliver packets across disconnected networks • The problem now is which node to choose? • Blind random choice: • Might not help • Nodes may move even farther away from the destination • Informed choice: • Better decision • HOW? – WHAT IF we know more about nodes (such as their destination or path information)

  22. Navigation System Helps! • Harvest neighbors’ dest/path information • Assumption: • Every vehicle has a navigation system • Is it true? • Relaxed Assumption • “Pseudo/Virtual” navigation system

  23. Virtual Navigation Interface • A lightweight wrapper interface interacts with data sources • Provide two unified information: • Nav Info • Destination • Path • Direction • Confidence • 0% (Unreliable) ~ 100% (Reliable)

  24. VNI Example Bus VNI : (Path, 100%) w/ Navigation VNI : (Path, 55%) Taxi VNI : (Dest, 100%) w/o Navigation VNI : (?, 0%)

  25. GeoDTN+Nav Modes • Introduce third forwarding mode in geo-routing • DTN recovery mode • Complement conventional two-mode geo-routing • Three routing modes • Greedy • Perimeter • DTN

  26. DTN Mode • In recovery mode • Current node C • Neighbors Ni (i=1~n) • Hops h • Compute a “switch score” for each neighbor with • Scoring function S • Switch threshold Sthresh • RULE: • If S(C) > Sthresh and there exists Ni, such that S(Ni) > Sthresh and S(Ni) > S(Nj), i ≠ j for all j • Switch to DTN mode • Forward the packet to Ni

  27. Scoring Function • S(Ni) = αP(h) + βQ(Ni) + γDir(Ni) where α + β + γ = 1 • S(Ni): “Switch score” of Ni • P(h): (0 ~ 1) Partition probability • Q(Ni): (0 ~ 1) Quality of the “mule” • Dir(Ni): (0 ~ 1) Direction of the “mule” towards the dest • P(h) ↑ S(Ni) ↑ • If the network is highly suspected to be disconnected, it would be better to switch to DTN • Q(Ni) ↑ S(Ni) ↑ • If there is a neighbor which has higher guarantee of delivery of packets to the destination, Q(Ni) would increase S(Ni) • Dir(Ni) ↑ S(Ni) ↑ • If the neighbor is heading toward the destination, Dir(Ni) would increase S(Ni) • Q(Ni) and Dir(Ni) functions depend largely on info from VNI!!

  28. P(h) • Suspect network connectivity by “traversed hop counts” • RED-like probability function • hmin • hmax

  29. Q(Ni) • Calculate Ni’s “Delivery Quality” • Navigation information • Confidence D2 D1 D3

  30. Dir(Ni) • Determine Ni’s “routability”: Can Ni carry the packets? • Ni’s direction wrt destination • Current node’s direction wrt destination Dir(N2) > Dir(N1)

  31. Example: Perimeter to DTN Q(N2) = 0D(N2) = 0.2 S(N2) = 0.25 Q(N1) = 0.1D(N1) = 0.8 S(N1) = 0.25 • Let • α = β = 0.5, γ = 0 • Sthresh = 0.5 P(8) = 0.4Q(A) = 0.4D(A) = 0.2S(A) = 0.4 Q(N1) = 0.2D(N1) = 0.3 S(N1) = 0.35 P(9) = 0.5Q(B) = 0.5D(B) = 1S(B) = 0.50 Q(N2) = 0.7D(N2) = 0.8 S(N2) = 0.60 Q(N3) = 0.6D(N3) = 0.5 S(N3) = 0.5 Q(N3) = 0.6D(N3) = 0.9 S(N3) = 0.55

  32. Example: DTN to Greedy • Switch to greedy only if neighbor score is lower AND it’s closer than the node that first entered into DTN S(X) = 0.2 S(J) = 0.3 S(B) = 0.6 X B J S(B) = 0.5 C S(C) = 0.3 D A A Y K S(A) = 0.5 S(X) = 0.4 S(K) = 0.4

  33. GeoDTN+Nav Evaluation • Topology: 1500m by 4000m Oakland map from TIGER database • Mobility: • VanetMobisim (100 cars) • 50 buses and taxis for mules • Routing protocols: GPCR, RandDTN • Metrics: PDR, hop count, latency

  34. PDR • GeoDTN+Nav maintains high PDR because packets are carried mostly by Bus nodes • GeoDTN+Nav beats RandDTN

  35. Latency • GeoDTN+Nav latency lower than RandDTN because of its hybrid nature • GPCR latency is low => packets are delivered when network is connected

  36. Hop Count • GeoDTN+Nav higher hop count than RandDTN • Trading high count for PDR and low latency

  37. GeoDTN+Nav Forwarding Diversity • % of Bus nodes and taxi nodes as mules • As the number of bus node increases, PDR increases => bus has better packet delivery • GeoDTN+Nav able to use both types of vehicles provided by VNI

  38. Conclusion • Geographic routing is feasible in VANETs • Yet it is inefficient in a VANET environment • We identified problems of geographic routing in VANETs and propose solutions: • Planarization overhead, routing inefficiency, and signal interference (TO-GO) • Routing loops caused by empty junction nodes (GeoCross) • Expensive recovery (LOUVRE) • Intermittent connectivity (GeoDTN+Nav)

  39. Publication • "Enhanced Perimeter Routing for Geographic Forwarding Protocols in Urban Vehicular Scenarios,“ Kevin C. Lee, Jerome Haerri, Uichin Lee, Mario Gerla, Autonet'07, Washington, D.C., November, 2007. • "TO-GO: TOpology-assist Geo-Oppertunistic Routing in Urban Vehicular Grids," Kevin C. Lee, Uichin Lee, Mario Gerla, WONS 2009 , Snowbird, Utah, February, 2009. • "GeoCross: A Geographic Routing Protocol in the Presence of Loops in Urban Scenarios," Kevin C. Lee, Pei-Chun Cheng, Mario Gerla, Ad Hoc Networks: January, 2010. • "LOUVRE: Landmark Overlays for Urban Vehicular Routing Environments," Kevin C. Lee, Michael Le, Jerome Haerri, Mario Gerla, WiVeC 2008, Calgary, Canada, September, 2008. • "Histogram-Based Density Discovery in Establishing Road Connectivity," Kevin C. Lee, Jiajie Zhu, Jih-Chung Fan, Mario Gerla, VNC, Tokyo, Japan, October, 2009. • "GeoDTN+Nav: A Hybrid Geographic and DTN Routing with Navigation Assistance in Urban Vehicular Networ," Pei-Chun Cheng, Jui-Ting Weng, Lung-Chih Tung, Kevin C. Lee, Mario Gerla, Jerome Haerri, MobiQuitous/ISVCS 2008, Trinity College Dublin, Ireland, July, 2008. • "GeoDTN+Nav: Geographic DTN Routing with Navigator Prediction for Urban Vehicular Environments," Pei-Chun Cheng, Kevin C. Lee, Mario Gerla, Jérôme Härri, Mobile Networks and Applications: Volume 15, Issue 1 (2010), Page 61.

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