geographic routing in vehicular ad hoc networks (vanets) kevin c. lee computer science department...
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Geographic Routing in Vehicular Ad Hoc Networks (VANETS)
Kevin C. LeeComputer Science Department
University of California, Los AngelesChair – Professor Mario Gerla
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Outline Overview of geographic routing Summary of previous work Present LOUVRE Histogram-based density
estimation approach Report GeoDTN+Nav new results
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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
Face traversal by right-hand rule
Face change
Walking sequence: F1 -> F2 -> F3 -> F4
Recovery/Perimeter Mode
x
y z
S
D
F1
F2
F3
F4
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A
B
C
D
E
I1
I2
I3
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
Planarization
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Outline Overview of geographic routing Summary of previous work Present LOUVRE Histogram-based density
estimation approach Report GeoDTN+Nav new results
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TO-GO[1, 2]
Perimeter forwarding using greedy forwarding Packet skipping a junction node if not
changing direction
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
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GeoCross[3]
Routing loop!!
Motivation: Empty intersection -> routing loop -> low packet delivery
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GeoCross Basic OperationsS, R1, [R1R2], R2, B, R3, C, R4, D, R5, [R5R6], R6, E, R7, F, R8, B => No cross link, continue forwarding
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
Can’t forward b/c UR: [R5R6]
Packet reaches destination
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LOUVRE[4] 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
D
S
?Road 1
s sDensity > Thresh = 3 2
3
3 3
5
3
3
00
50
0
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sOverlayroutes
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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
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Outline Overview of geographic routing Summary of previous work Present LOUVRE Histogram-based density
estimation approach Report GeoDTN+Nav new results
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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
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
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1 2 0 01 2 ? 01 2 0 0
1 2 1 01 2 1 0 Segment center
1 2 0 0
1 2 1 0
SegSizeNiRadioRange
Segment 1
Segment 2
Segment 3
Segment 4
A B C D
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
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NOT CONNECTED
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
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Connectivity Accuracy between P2P and Histogram
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P2P underperforms when density is low This is due to the clustering behavior at two
ends of a road
Broadcast Overhead between P2P and Histogram P2P has scalability issue as it needs to keep
track of unique cars
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Outline Overview of geographic routing Summary of previous work Present LOUVRE Histogram-based density
estimation approach Report GeoDTN+Nav new results
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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
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)
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Which Node?
Harvest neighbors’ dest/path information Assumption:
Every vehicle has a navigation system Is it true?
Relaxed Assumption “Pseudo/Virtual” navigation system
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Navigation System Helps!
A lightweight wrapper interface interacts with data sources
Provide two unified information: Nav Info
Destination Path Direction
Confidence 0% (Unreliable) ~ 100% (Reliable)
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Virtual Navigation Interface
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VNI Example
Food Mart
BusVNI : (Path, 100%)
TaxiVNI : (Dest, 100%)
w/ NavigationVNI : (Path, 55%)
w/o Navigation
VNI : (?, 0%)
Introduce third forwarding mode in geo-routing DTN recovery mode Complement conventional two-mode geo-
routing Three routing modes
Greedy Perimeter DTN
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GeoDTN+Nav Modes
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
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DTN Mode
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
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!!
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Scoring Function
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P(h) Suspect network
connectivity by “traversed hop counts”
RED-like probability function hmin
hmax
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Q(Ni) Calculate Ni’s “Delivery
Quality” Navigation information Confidence
D1
D2
D3
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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)
Let α = β = 0.5, γ = 0 Sthresh = 0.5
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Example: Perimeter to DTN
Q(N1) = 0.1D(N1) = 0.8S(N1) = 0.25
P(9) = 0.5Q(B) = 0.5D(B) = 1S(B) = 0.50
Q(N2) = 0D(N2) = 0.2S(N2) = 0.25
P(8) = 0.4Q(A) = 0.4D(A) = 0.2S(A) = 0.4
Q(N3) = 0.6D(N3) = 0.5S(N3) = 0.5
Q(N1) = 0.2D(N1) = 0.3S(N1) = 0.35
Q(N2) = 0.7D(N2) = 0.8S(N2) = 0.60 Q(N3) = 0.6
D(N3) = 0.9S(N3) = 0.55
Switch to greedy only if neighbor score is lower AND it’s closer than the node that first entered into DTN
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Example: DTN to Greedy
A Y
BX
K
J
DC
S(X) = 0.2
S(X) = 0.4
S(B) = 0.6
S(A) = 0.5S(K) = 0.4
S(J) = 0.3
S(C) = 0.3
S(B) = 0.5
A
Topology: 1500m by 4000m Oakland map from TIGER database
Mobility: VanetMobisim (100 cars) 50 buses and taxis for mules
Routing protocols: GPCR, RandDTN
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GeoDTN+Nav Evaluation
Metrics: PDR, hop count, latency
GeoDTN+Nav maintains high PDR because packets are carried mostly by Bus nodes
GeoDTN+Nav beats RandDTN
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PDR
GeoDTN+Nav latency lower than RandDTN because of its hybrid nature
GPCR latency is low => packets are delivered when network is connected
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Latency
GeoDTN+Nav higher hop count than RandDTN
Trading high count for PDR and low latency
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Hop Count
% 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
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GeoDTN+Nav Forwarding Diversity
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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)
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Publication1. "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.
2. "TO-GO: TOpology-assist Geo-Oppertunistic Routing in Urban Vehicular Grids," Kevin C. Lee, Uichin Lee, Mario Gerla, WONS 2009 , Snowbird, Utah, February, 2009.
3. "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.
4. "LOUVRE: Landmark Overlays for Urban Vehicular Routing Environments," Kevin C. Lee, Michael Le, Jerome Haerri, Mario Gerla, WiVeC 2008, Calgary, Canada, September, 2008.
5. "Histogram-Based Density Discovery in Establishing Road Connectivity," Kevin C. Lee, Jiajie Zhu, Jih-Chung Fan, Mario Gerla, VNC, Tokyo, Japan, October, 2009.
6. "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.
7. "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.