Transcript josianethesis

Road-Based Routing in
Vehicular Networks
PhD Dissertation Defense
by Josiane Nzouonta
Advisor: Cristian Borcea
Today: Smart Vehicles
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Geographical Positioning System (GPS)
Digital maps or navigation system
On-Board Diagnostic (OBD) systems
DVD player
2
Tomorrow: Vehicular Networks
Internet
Cellular
Cellular
Roadside
infrastructure
Roadside
infrastructure
Vehicle-to-vehicle
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Applications
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Accident alerts/prevention
Dynamic route planning
Entertainment
Communications
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3
Cellular network
Vehicle to roadside
Vehicle to vehicle
Vehicular Ad Hoc Networks (VANET)
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My focus in this research
Benefits
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Challenges
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Scalability
Low-cost
High bandwidth
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4
Security
High mobility
VANET Characteristics
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High node mobility
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Constrained nodes movements
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Obstacles-heavy deployment fields, especially in cities
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Large network size
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Can applications based on multi-hop communications work in
such environment?
5
Problem Statement

How to design efficient routing and forwarding
protocols in VANET?
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Do existing MANET routing protocols work well in VANET?
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If not, can we take advantage of VANET characteristics to
obtain better performance?
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Are current forwarding protocols enough or can they be
optimized for VANET characteristics?
6
Contributions
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Road-Based using Vehicular Traffic (RBVT) routing
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VANET distributed next-hop self-election
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Eliminate overhead associated with periodic “hello” messages in
geographical forwarding
Effect of queuing discipline on VANET applications
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Use real-time vehicular traffic and road topology for routing
decisions
Geographical forwarding on road segments
LIFO-Frontdrop reduces end-to-end delay compared with FIFOTaildrop
RBVT path predictions

Analytical models to estimate expected duration of RBVT paths
7
Outline
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Motivation
RBVT routing
Forwarding optimizations
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Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions
8
Node Centric Routing Shortcomings in VANET
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Examples of node-centric
MANET routing protocols
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AODV, DSR, OLSR
D
N1
S
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Frequent broken paths due to
high mobility
Path break does not correspond
to loss connectivity
Performance highly dependent
on relative speeds of nodes on a
path
9
a) At time t
N1
S
N2
b) At time t+Δt
D
Geographical Routing Shortcoming in VANET
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Examples of MANET
geographical routing
protocols
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Advantage over nodecentric
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GPSR, GOAFR
Less overhead, high
scalability
S
N1
Subject to (virtual) deadend problem
N2
D
Dead end road
10
RBVT Routing Main Ideas
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Use road layouts to compute
paths based on road
intersections
Select only those road
segments with network
connectivity
Use geographical routing to
forward data on road
segments
Advantages


S
Source
I1
I2
I3
I4
I5
I7
I8
A
B
C
Path in header: I8-I5-I4-I7-I6-I1
I6
Greater path stability
Lesser sensitivity to vehicles
movements
E
D
car
Ij
11
Intersection j
Destination
RBVT Protocols
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RBVT-R: reactive path creation
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Up-to-date routing paths between communicating pairs
Path creation cost amortized for large data transfers
Suitable for relatively few concurrent transfers
RBVT-P: proactive path creation
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Distribute topology information to all nodes
No upfront cost for given communication pair
Suitable for multiple concurrent transfers
12
RBVT-R Route Discovery
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Source broadcasts route discovery
(RD) packet
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RD packet is rebroadcast using
I1
improved flooding
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S
A
B
Intersections traversed are stored
Source
N1
Re-broadcast
from N1
I2
I3
I4
I5
I7
I8
Re-broadcast
from B
C
in RD header
I6
E
I
13
car
Intersection
j
j
D
Destination
RBVT-R Route Reply
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Destination unicasts route
reply (RR) packet back to
the source
Route stored in RR header
RR follows route stored in
the RD packet
S
Source
I1
I3
I4
I5
B
C
I6
E
I
14
I2
A
car
j Intersection j
I7
I
Path in reply
packet header
I1
I6
I7
D I4
8
I5
I8
Destination
RBVT-R Forwarding
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Data packet follows path
in header
Geographical
forwarding is used
between intersections
Path in data
header
I1
I6
I7
I4
I5
I8
S
Source
I1
I3
I4
I5
I7
I8
B
C
I6
E
I
15
I2
A
car
j Intersection j
D
Destination
RBVT-R Route Maintenance
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Dynamically update routing
path
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Add/remove road intersections
to follow end points
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I1
A
B
When path breaks
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S
Route error packet sent to
source
Source pauses transmissions
New RD generated after a
couple of retries
16
Source
N1
Re-broadcast
from N1
I2
I3
I4
I5
I7
I8
Re-broadcast
from B
C
I6
E
I
car
Intersection
j
j
D
Destination
RBVT-P Topology Discovery
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Unicast connectivity packets
(CP) to record connectivity
graph
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I1
Node independent topology leads
to reduced overhead
Lesser flooding than in MANET
proactive protocols
I3
I2
A
2
1
B
7
C
3
8
I4
6
I5
9
5
4
I6
Intersections gradually added to
traversal stack
Status of intersections stored in
CP
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n
n-1
Network traversal using
modified depth first search
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CP generator
Reachable/unreachable
17
E
I
car
i Step i
Intersection
j
j
I7
I8
RBVT-P Route Dissemination & Computation
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CP content is disseminated in
network at end of traversal
Iv1
I1
I2
Iv2
I3
Each node
Iv4
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Updates local connectivity view
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Computes shortest path to other
road segments
I1: I2, I6,
Iv1
6: I1, I7
I7: I6, I4
I4: I7, I5,
I5v3: I4, I8,
Iv4
2: I1, Iv2
RU content
Iv3
I6
Reachability
18
I4
I5
I7
I8
Ij Intersection j
RBVT-P Forwarding and Maintenance
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RBVT-P performs loose source routing
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Path stored in every data packet header
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Intermediate node may update path in data packet header
with newer information
In case of broken path, revert to greedy
geographical routing
19
RBVT Evaluation

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Perform simulations to compare against existing
protocols
Comparison protocols:

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AODV (MANET reactive)
GPSR (MANET geographical)
OLSR (MANET proactive)
GSR (VANET)
Metrics
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Average delivery ratio
Average end-to-end delay
Routing overhead
20
Simulation Setup
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Network Simulator NS-2
Map: 1500m x 1500m from
Los Angeles, CA
Digital map from US
Tiger/Line database
SUMO mobility generator
Obstacles modeled using
random selection of signal
attenuation
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Range [0dB, 16dB]
Shadowing propagation
model
21
Simulation Setup (cont’d)
Data rate
11Mbps
22
Average Delivery Ratio
250 nodes
100
100
90
90
80
70
AODV
60
GPSR
Average delivery ratio (%)
Average delivery ratio (%)
150 nodes
RBVT-P
50
OLSR
40
GSR
30
RBVT-R
20
10
1
1.499
2
3.003
4
4.505
5
GPSR
RBVT-P
50
OLSR
40
GSR
30
RBVT-R
20
0.5
Packet sending rate (Pkt/s)
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AODV
60
0
0.5

70
10
0

80
1
1.499
2
3.003
4
4.505
5
Packet sending rate (Pkt/s)
RBVT-R has the best delivery ratio performance
RBVT-P improves in medium/dense networks
The denser the network, the better the performance for
road-based protocols in these simulations
23
Average End-to-end Delay
250 nodes
5
5
4.5
4.5
4
3.5
AODV
3
GPSR
End-to-end delay (Seconds)
End-to-end delay (Seconds)
150 nodes
RBVT-P
2.5
OLSR
2
GSR
RBVT-R
1.5
1
0.5
GPSR
RBVT-P
2.5
OLSR
2
GSR
RBVT-R
1.5
1
0
0.5
1
1.499
2
3.003
4
4.505
5
0.5
Packet sending rate (Pkt/s)

AODV
3
0.5
0

4
3.5
1
1.499
2
3.003
4
4.505
5
Packet sending rate (Pkt/s)
RBVT-P performs best, consistently below 1sec in the
simulations
RBVT-R delay decreases as the density increases (less
broken paths)
24
Outline

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Motivation
RBVT routing
Forwarding optimizations



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Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions
25
The Problem with “hello” Packets
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“hello” packets used to advertise node positions in
geographical forwarding
“hello” packets need to be generated frequently in
VANET
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High mobility leads to stalled neighbor node positions
Presence of obstacles leads to incorrect neighbor presence
assumptions
Problems in high density VANET
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Increased overhead
Decreased delivery ratio
26
Distributed Next-hop Self-election
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Slight modification of
IEEE 802.11 RTS/CTS
Backward compatible

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(0.201ms) n1
(0.0995ms)
r
ns RTS
n2
(NULL)
(0.115ms)
n4
n3
RTS specifies sender and
final target positions
Waiting time is computed by
each receiving node using
prioritization function
Next-hop with shortest
waiting time sends CTS first
Transmission resumes as in
standard IEEE 802.11
27
n5
D
n6
(a) RTS Broadcast and Waiting Time Computation
n1
r
ns CTS
n2
n4
n5
D
n6
n3
(b) CTS Broadcast
r
n4
ns
n1
Data n
2
n5
D
n6
n3
(c) Data Frame
n1
r
ns ACK n
2
n4
n3
n5
n6
D
Waiting function
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Function takes 3 parameters
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Distance sender to next-hop (dSNi)
Distance next-hop to destination (di)
Received power level at next-hop (pi)
Weight parameters α set a-priori
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Value of α determines weight of corresponding parameter
28
Waiting Function Results
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Using multi-criteria function to select next hops leads to
significantly lower packet loss and overhead in VANET
29
Evaluation of Self-election Performance
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Goal: Verify and quantify if/how self-election improves
performance in high congestion scenarios
Metrics
 Average delivery ratio
 Average end-to-end delay
 Routing overhead
Used own mobility generator based on Gipps car-following and
lane-changing models
Simulations parameters same as used for RBVT evaluation
Map used in the no obstacle simulations
30
Delivery Ratio & Delay
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RBVT-R with source selection using “hello” packets vs. selfelection
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Distributed next-hop self election
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Increases delivery ratio
Decreases end-to-end delay
31
Outline



Motivation
RBVT routing
Forwarding optimizations




Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions
32
Effect of Current Queue Discipline on Delay

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Current queuing discipline: FIFO with Taildrop (TD)
Wireless contention increase time packets spend in
queue
Low percentage problem frames have significant impact
on average delay
33
Improving Delay through Queuing Discipline

Why improve?

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
Delay sensitive but loss tolerant applications important in
VANET/MANET
Applications: video streaming near an accident; search and
rescue operations
Analyze four queuing disciplines

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FIFO-Taildrop (FIFO-TD)
FIFO-Frontdrop (FIFO-FD)
LIFO-Taildrop (LIFO-TD)
LIFO-Frontdrop (LIFO-FD)
34
Single Queue Analysis

Probabilities of service and failure

Probabilities of service/failure given that packet arrives with
system in state k

And for all disciplines
35
Single Queue Analytical Results



Low traffic rate ρ = 0.75
Expected waiting times are similar for all 4 disciplines
Variance of waiting times higher for LIFO disciplines
36
Single Queue Analytical Results (cont’d)



High traffic rate ρ = 1.5
LIFO-FD presents low expected waiting times of packets served
Variance of waiting times of served packets is also lowest for
LIFO-FD and highest for LIFO-TD
37
Network Evaluation

Evaluation


Assess performance in ad hoc networks, static and mobile
Metrics: average end-to-end delay, end-to-end jitter,
throughput
Static topology
38
Average End-to-end Delay
UDP sending rate 5 Packet/seconds
UDP sending rate 20 Packet/seconds
0.14
20
18
End-to-end delay (Seconds)
End-to-end delay (seconds)
0.12
0.1
FIFO-FD
0.08
FIFO-TD
LIFO-FD
0.06
LIFO-TD
0.04
0.02
10
20
30
50
70
90
100
FIFO-TD
10
LIFO-FD
8
LIFOTD
6
4
5
Buffer size

FIFO-FD
12
0
5

14
2
0

16
10
20
30
50
70
90
100
Buffer size
Static ad hoc network scenario
Comparable performance for low traffic
LIFO disciplines have best and worst performance in
high traffic
39
Average Jitter
UDP sending rate 5 Packet/second
UDP sending rate 10 Packet/second
0.04
6
5
Average jitter (Seconds)
Average jitter (Seconds)
0.035
0.03
0.025
FIFO-FD
FIFO-TD
0.02
LIFO-FD
LIFO-TD
0.015
0.01
4
FIFO-FD
FIFO-TD
3
LIFO-FD
LIFO-TD
2
1
0.005
0
0
5
10
20
30
50
70
90
100
5
10
Buffer size


30
50
70
90
100
Buffer size
Static ad hoc network scenario
Low traffic: less than 40ms jitter for all 4


20
FIFO has lowest jitter
High traffic: LIFO-FD maintains less than 1sec jitter with buffer
size increase
40
Delay & Throughput in VANET

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
No obstacles map with 250 nodes, RBVT-R
LIFO-FD leads to lower delay (as much as 45%)
Throughput not aversely affected by LIFO-FD
41
Outline



Motivation
RBVT routing
Forwarding optimizations




Distributed next-hop election
Effect of queuing disciplines on VANET performance
RBVT path predictions
Conclusions
42
Characterization of RBVT Paths

Why?





How long is the current route going to last?
Does it make sense to start a route discovery?
Can a 100Mb file be successfully transferred using the current
route?
Is it possible to estimate the duration of a path disconnection?
How to estimate path characteristics (connectivity
duration/probability)?


Simulations are specific to geographical area
Analytical models based on validated traffic models are
preferred
43
Cellular Automata (CA) Traffic Model
Lc = 7.5m
car 1, v1=2
car 3, v3=1
car 2, v2=2
gap1 = 4 cells
gap3 ≥ 3 cells
gap2 = 1 cell
(a) At time t
car 1, v1=2
car 2, v2=1
gap1 = 3 cells
car 3, v3=1
gap2 = 1 cell
gap3 ≥ 2 cells
(b) At time t+1

Update rules at vehicle i




Acceleration: if vi < vmax, vi = vi + 1
Slow down (if needed): if vi > gapi, vi= gapi
Randomization: vi = vi – 1 with probability p
Move car: xi = xi + vi
44
DTMC-CA Model




Discrete-time and discrete space model
Uses CA microscopic traffic model for vehicle
movements
Portion of road between source and destination divided
in k cells of length Lc
Markov chain M = (S, P, s0)



State space S = {s = (c1, c2, …, ck), ci є V, i=(1,…, k)}
Cell values V = {0, 1, 2, …, vmax, ∞}
Interested only in stationary measures
45
State Reduction: Invalid States


As described, |S| = |V|k
Many potential states are transient states


Violate updating rules
Not reachable from any other state in the system
Time t-1
Time t

1
Time t-1
0
0
2
Time t
2
0
0
3
Algorithm to output non-transient states
 Directly obtaining non-transient states needed
46
State Reduction: Lumpability

Markov chain
is lumpable w.r.t
with

Example

Additional 80% decrease in size of space set observed
when lumping the Markov chain
47
Transition Matrix

Generic transition probability from state of aggregated
Markov chain
Road section
Cell number
48
2
1
2
1
3
4
5
1
6
7
0
8
9
10
For cell 2:
2 = 3
2 = 0
For cell 3:
3 = 5
3 = 0
Borders:
0 = 3
10 = 9
For cell 6:
6 = 7
6 = 5
Probabilistic Measures




Stationary distribution π
Connected states S1
Disconnected states S2
S1US2 =S
Expected duration of connectivity
49
S1∩S2 =Ø
Probabilistic Measures (cont’d)

Expected duration of disconnection

Probability of connection duration
50
Extending Basic Model

Bidirectional Traffic



Each lane is divided in k cells, juxtaposed, independent
Markov chain
Moving endpoints and lane change


Speed relative to source speed
Possible cell values
Lc = 7.5m
51
Evaluation Method and Setup



Simulation to validate
model
Simulate CA freeway
model and SUMO
Large ring layout



Total number of cells = 320 cells
Source
Connectivity of shaded
area is analyzed
Complete ring affects
shaded area
DTMC-CA considers
shaded area only
52
Area of observation
with k cells
Destination
Expected Connectivity Duration
75 vehicles
50 vehicles



DTMC-CA match well with simulation results
Increase in transmission range leads to increase in connectivity
duration (as expected)
Stochastic nature of CA model: 11 cells out of 12 cells for
connectivity leads to average of < 80 sec with 0.23 density
53
Expected Disconnectivity Duration
35 vehicles



50 vehicles
DTMC-CA match well with simulation results
Increasing connectivity range decreases expected disconnectivity
duration
Impact of density on expected disconnectivity duration reduced
compared to impact on expected connectivity duration
54
Probability Connectivity Duration
k = 8 cells


k = 10 cells
Longer uninterrupted connectivity less likely
Larger k leads to smaller probabilities of connectivity
duration
55
Incorporating Path Estimates in RBVT

Road-side sensors or historical data


Improving route selection


How long should the source wait when a route breaks
Determining RBVT-P CP generation interval


Duplicate routes received at the destination
Enhancing route maintenance of RBVT-R


Road segment densities and entry speeds probabilities
Period between CP generation based on connectivity duration
Reducing overhead network traffic

Likelihood of success of 100MB transmission (delay or divide in smaller
chunks)
56
Conclusion



Existing MANET routing protocols do not work well in VANET
Better routing and forwarding possible by integrating VANET
characteristics such as road layouts and node mobility
Contributions:
 RBVT routing: Stable traffic-aware road-based paths
 Distributed VANET next-hop self-election: Significant
overhead reduction in geographical forwarding
 Impact of queuing discipline on latency: LIFO-TD improves
performance for delay sensitive applications
 RBVT paths predictions: Analytically compute path estimates,
which can be used to improve data transfer performance
57
Future Work

Adaptive queuing mechanism

Route lifetime prediction independent of the vehicular
traffic model used

Apply knowledge of expected route duration in RBVT

Security issues in RBVT
58
Thank you!
Acknowledgments: This research was supported by the NSF
grants CNS-0520033 and CNS-0834585
59
Impact of Number of Flows
100
90
Average delivery ratio (%)
80
70
AODV
60
GPSR
RBVT-P
50
OLSR
40
GSR
RBVT-R
30
20
10
0
1
5
10
15
20
Number of concurrent flows


The data rate is fixed at 4 packets/second and the network size is
250 nodes
Delivery ratio is stable in the simulations performed
60
Node Selection Using Waiting Function
61
Overhead


Routing packets exchanged for each received data
packet
Removing “hello” packets essentially eliminates most
overhead
62
Single Queue Analysis




Interested in time elapsed from packet arrival to service
Markov chain model X(t) on the state space {−1, 0, · · · ,N}
If packet arrives when state (k), k < N
 State changes to (k + 1)
 New packet goes in position k+1
If a packet arrives while the system is in state (N)
 System remains in this state
 Under Taildrop, the arriving packet is dropped and all the
other packets remain in their old positions
 Under Frontdrop, the packet in position 1 is dropped, other
packets move up one position (j -> j −1), arriving packet goes
to position N
63
TCP Throughput and Fairness
10 flows, 2MB each



6 flows, 5MB each
Static ad hoc network scenario
Transfers complete at comparable times for LIFO-FD
and FIFO-TD
LIFO-FD does not disadvantage any specific flow in
those simulations
64
DTMC-CA: Effect of Removing Invalid States
65
SUMO Results
66
Different Traffic Models - Different Results
67
Connectivity Window Model
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Provide analytical model
independent of the traffic
model
Uses the concept of
connectivity window
Count vehicles in each
window
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