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
 Present LOUVRE Histogram-based density
estimation approach
 Report GeoDTN+Nav new results
2
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 3
Recovery/Perimeter Mode
 Face traversal by
right-hand rule
 Face change
z
y
x
D
D
F4
C
F2
A
Walking sequence:
F1 -> F2 -> F3 -> F4
F1
S
F3
I2
I3
E
I1
B
4
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
5
Outline
 Overview of geographic routing
 Summary of previous work
 Present LOUVRE Histogram-based density
estimation approach
 Report GeoDTN+Nav new results
6
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
Perimeter forwarding using greedy
forwarding
Opportunistic routing toward
the target
Packet skipping a junction node if not
changing direction
7
GeoCross[3]
 Motivation: Empty intersection -> routing loop ->
low packet delivery
Routing loop!!
8
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,[R5R6],
R7, F, R8,
B, R2,
UR:
continue
[R2R1], R1, Sexisting loop
9
Packet reaches destination
LOUVRE[4]
D
 Recovery mode often expensive;
backtracking takes too many steps
?
 Use P2P density information to
S
guide packet routing
 LOUVRE: end-to-end routing solution that
eliminates recovery forwarding completely
Road 1
3
3
3
0
3
0
s
Density
> Thresh = 3
3
2
s
0
0
5
5
3
3
Overlay
routes
s
10
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
11
Outline
 Overview of geographic routing
 Summary of previous work
 Present LOUVRE Histogram-based density
estimation approach
 Report GeoDTN+Nav new results
12
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
13
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


SegSize

 Road is connected if Ni   RadioRange 
1
1 2
2 1 1? 2 0 0 0
A
Segment 1
0

0
1
B
2
C
Segment 2
1

Segment center
0
D
Segment 3
Segment 4
14
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
15
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
16
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
1.4
Connectivity accuracy %
1.2
1
0.8
P2P
0.6
Histogram
0.4
0.2
0
18
22
26
30
# of Cars
34
38
17
Broadcast Overhead between
P2P and Histogram
 P2P has scalability issue as it needs to keep
track of unique cars
800
700
Overhead in Bytes
600
500
400
P2P
Histogram
300
200
100
18
0
100
150
200
Nodes
250
300
Outline
 Overview of geographic routing
 Summary of previous work
 Present LOUVRE Histogram-based density
estimation approach
 Report GeoDTN+Nav new results
19
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
20
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)
21
Navigation System Helps!
 Harvest neighbors’ dest/path information
 Assumption:
 Every vehicle has a navigation system
 Is it true?
 Relaxed Assumption
 “Pseudo/Virtual” navigation system
22
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)
23
VNI Example
w/ Navigation
VNI : (Path, 55%)
Bus
VNI : (Path, 100%)
Food Mart
w/o
Navigation
VNI : (?, 0%)
Taxi
VNI : (Dest, 100%)
24
GeoDTN+Nav Modes
 Introduce third forwarding mode in georouting
 DTN recovery mode
 Complement conventional two-mode georouting
 Three routing modes
 Greedy
 Perimeter
 DTN
25
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
26
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!!
27
P(h)
 Suspect network
connectivity by “traversed
hop counts”
 RED-like probability
function
 hmin
 hmax
28
Q(Ni)
 Calculate Ni’s “Delivery
Quality”
 Navigation information
 Confidence
D2
D1
D3
29
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)
30
Example: Perimeter to DTN
 Let
 α = β = 0.5, γ = 0
 Sthresh = 0.5
Q(N1) = 0.1
D(N1) = 0.8
S(N1) = 0.25
Q(N1) = 0.2
D(N1) = 0.3
S(N1) = 0.35
Q(N2) = 0.7
D(N2) = 0.8
S(N2) = 0.60
P(9) = 0.5
Q(B) = 0.5
D(B) = 1
S(B) = 0.50
Q(N3) = 0.6
D(N3) = 0.9
S(N3) = 0.55
Q(N2) = 0
D(N2) = 0.2
S(N2) = 0.25
P(8) = 0.4
Q(A) = 0.4
D(A) = 0.2
S(A) = 0.4
Q(N3) = 0.6
D(N3) = 0.5
S(N3) = 0.5
31
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(B) = 0.6
X
B
S(J) = 0.3
J
S(B) = 0.5
C
S(C) = 0.3
A
S(A) = 0.5
Y
S(X) = 0.4
D
K
S(K) = 0.4
32
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
33
PDR
1.2
Packet Delivery Ratio
1
0.8
0.6
GeoDTN+Nav
0.4
RandDTN
0.2
GPCR
 GeoDTN+Nav
maintains high PDR
because packets are
carried mostly by
Bus nodes
 GeoDTN+Nav beats
RandDTN
0
5
10
15
20
25
30
Number of Bus Nodes
35
40
34
Latency
300
Latency (s)
250
200
150
100
50
0
5
10
15
20
25
30
Number of Bus Nodes
 GeoDTN+Nav
latency lower than
RandDTN because
GeoDTN+Nav
of its hybrid nature
RandDTN
 GPCR latency is low
GPCR
=> packets are
delivered when
network is connected
35
40
35
Hop Count
16
GeoDTN+Nav
Number of Hops
14
RandDTN
12
10
8
6
4
2
 GeoDTN+Nav
higher hop count
than RandDTN
 Trading high count
for PDR and low
latency
0
5
10
15
20
25
30
Number of Bus Nodes
35
40
36
GeoDTN+Nav Forwarding Diversity
0.3
Packet Delivery Ratio
0.25
0.2
0.15
GeoDTN+Nav
0.1
Optimal
0.05
0
0
0.2
0.4
0.6
0.8
Percentage of Bus Nodes
1
 % 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
37
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)
38
Publication
1. "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.
39