Road-Based Routing in Vehicular Networks

PhD Dissertation Defense

by Josiane NzouontaAdvisor: Cristian Borcea

2

Today: Smart Vehicles Geographical Positioning System (GPS) Digital maps or navigation system On-Board Diagnostic (OBD) systems DVD player

3

Tomorrow: Vehicular Networks

Applications Accident alerts/prevention Dynamic route planning Entertainment

Roadside infrastructure

Internet

CellularCellular

Vehicle-to-vehicle

Roadside infrastructure

Communications Cellular network Vehicle to roadside Vehicle to vehicle

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Vehicular Ad Hoc Networks (VANET)

My focus in this research Benefits

Scalability Low-cost High bandwidth

Challenges Security High mobility

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VANET Characteristics High node mobility

Constrained nodes movements

Obstacles-heavy deployment fields, especially in cities

Large network size

Can applications based on multi-hop communications work in

such environment?

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Problem Statement How to design efficient routing and forwarding

protocols in VANET?

Do existing MANET routing protocols work well in VANET?

If not, can we take advantage of VANET characteristics to

obtain better performance?

Are current forwarding protocols enough or can they be

optimized for VANET characteristics?

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Contributions Road-Based using Vehicular Traffic (RBVT) routing

Use real-time vehicular traffic and road topology for routing decisions

Geographical forwarding on road segments

VANET distributed next-hop self-election Eliminate overhead associated with periodic “hello” messages in

geographical forwarding

Effect of queuing discipline on VANET applications LIFO-Frontdrop reduces end-to-end delay compared with FIFO-

Taildrop

RBVT path predictions Analytical models to estimate expected duration of RBVT paths

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Outline Motivation RBVT routing Forwarding optimizations

Distributed next-hop election Effect of queuing disciplines on VANET performance RBVT path predictions

Conclusions

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Node Centric Routing Shortcomings in VANET Examples of node-centric

MANET routing protocols AODV, DSR, OLSR

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

S

SN1 D

N1

D

a) At time t

b) At time t+Δt

N2

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Geographical Routing Shortcoming in VANET Examples of MANET

geographical routing protocols GPSR, GOAFR

Advantage over node-centric Less overhead, high

scalability Subject to (virtual) dead-

end problem

S

DDead end road

N1

N2

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RBVT Routing Main Ideas 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 Greater path stability Lesser sensitivity to vehicles

movements

I2I1 I3

I6 I8E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

Path in header: I8-I5-I4-I7-I6-I1

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RBVT Protocols RBVT-R: reactive path creation

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 Distribute topology information to all nodes No upfront cost for given communication pair Suitable for multiple concurrent transfers

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RBVT-R Route Discovery Source broadcasts route discovery

(RD) packet

RD packet is rebroadcast using

improved flooding

Intersections traversed are stored

in RD header

I2I1 I3

I6 I8E

carIntersection j

I7

I4 I5

Ij

D

S

AB

C

Source

Destination

N1

Re-broadcast from B

Re-broadcast from N1

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RBVT-R Route Reply Destination unicasts route

reply (RR) packet back to the source

Route stored in RR header RR follows route stored in

the RD packet

I2I1 I3

I6 I8E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

Path in reply packet header

I8

I4

I6

I5

I7

I1

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RBVT-R Forwarding

Data packet follows path in header

Geographical forwarding is used between intersections

I2I1 I3

I6 I8E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

Path in data header

I8

I4

I6

I5

I7

I1

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RBVT-R Route Maintenance Dynamically update routing

path Add/remove road intersections

to follow end points When path breaks

Route error packet sent to source

Source pauses transmissions New RD generated after a

couple of retries

I2I1 I3

I6 I8E

carIntersection j

I7

I4 I5

Ij

D

S

AB

C

Source

Destination

N1

Re-broadcast from B

Re-broadcast from N1

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RBVT-P Topology Discovery Unicast connectivity packets

(CP) to record connectivity graph Node independent topology leads

to reduced overhead Lesser flooding than in MANET

proactive protocols Network traversal using

modified depth first search Intersections gradually added to

traversal stack Status of intersections stored in

CP Reachable/unreachable

I2I1 I3

I6 I8E

carIntersection j

I7

I4 I5

Ij

A

B

C

CP generator

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3

4

5

6

7 8

9

nn-1

i Step i

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RBVT-P Route Dissemination & Computation CP content is disseminated in

network at end of traversal Each node

Updates local connectivity view Computes shortest path to other

road segments

Reachability Intersection j

I2: I1, Iv2

I4: I7, I5, Iv3

I6: I1, I7

I5: I4, I8, Iv4

I7: I6, I4

I1: I2, I6,

Iv1

Iv3

Iv2

RU content

I1 I2 I3

I4 I5

I6 I7 I8

Iv4

Ij

Iv1

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RBVT-P Forwarding and Maintenance

RBVT-P performs loose source routing Path stored in every data packet header

Intermediate node may update path in data packet header with newer information

In case of broken path, revert to greedy geographical routing

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RBVT Evaluation Perform simulations to compare against existing

protocols Comparison protocols:

AODV (MANET reactive) GPSR (MANET geographical) OLSR (MANET proactive) GSR (VANET)

Metrics Average delivery ratio Average end-to-end delay Routing overhead

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Simulation Setup 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 Range [0dB, 16dB]

Shadowing propagation model

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Simulation Setup (cont’d)

Data rate 11Mbps

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Average Delivery Ratio

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

150 nodes

0

10

20

30

40

50

60

70

80

90

100

0.5 1 1.499 2 3.003 4 4.505 5

Packet sending rate (Pkt/s)

Ave

rag

e d

eliv

ery

rati

o (

%)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

250 nodes

0

10

20

30

40

50

60

70

80

90

100

0.5 1 1.499 2 3.003 4 4.505 5

Packet sending rate (Pkt/s)

Ave

rag

e d

eliv

ery

rati

o (

%)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

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Average End-to-end Delay

RBVT-P performs best, consistently below 1sec in the simulations

RBVT-R delay decreases as the density increases (less broken paths)

250 nodes

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0.5 1 1.499 2 3.003 4 4.505 5

Packet sending rate (Pkt/s)

En

d-t

o-e

nd

del

ay (

Sec

on

ds)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

150 nodes

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0.5 1 1.499 2 3.003 4 4.505 5

Packet sending rate (Pkt/s)

En

d-t

o-e

nd

del

ay (

Sec

on

ds)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

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Outline Motivation RBVT routing Forwarding optimizations

Distributed next-hop election Effect of queuing disciplines on VANET performance RBVT path predictions

Conclusions

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The Problem with “hello” Packets “hello” packets used to advertise node positions in

geographical forwarding “hello” packets need to be generated frequently in

VANET High mobility leads to stalled neighbor node positions Presence of obstacles leads to incorrect neighbor presence

assumptions Problems in high density VANET

Increased overhead Decreased delivery ratio

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Distributed Next-hop Self-election Slight modification of

IEEE 802.11 RTS/CTS Backward compatible

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

ns

n4

n1

n2

n3

n5

n6

DRTS

CTS

(a) RTS Broadcast and Waiting Time Computation

(b) CTS Broadcast

(NULL) (0.115ms)

(0.201ms)(0.0995ms)r

rns

n4

n1

n2

n3

n5

n6

D

ns

n4

n1

n2

n3

n5

n6

DData

(c) Data Frame

r

ACKrns

n4

n1

n2

n3

n5

n6

D

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Waiting function Function takes 3 parameters

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 Value of α determines weight of corresponding parameter

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Waiting Function Results

Using multi-criteria function to select next hops leads to significantly lower packet loss and overhead in VANET

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Evaluation of Self-election Performance 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

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Delivery Ratio & Delay

Distributed next-hop self election Increases delivery ratio Decreases end-to-end delay

RBVT-R with source selection using “hello” packets vs. self-election

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Outline Motivation RBVT routing Forwarding optimizations

Distributed next-hop election Effect of queuing disciplines on VANET performance RBVT path predictions

Conclusions

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Effect of Current Queue Discipline on Delay

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

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Improving Delay through Queuing Discipline Why improve?

Delay sensitive but loss tolerant applications important in VANET/MANET

Applications: video streaming near an accident; search and rescue operations

Analyze four queuing disciplines FIFO-Taildrop (FIFO-TD) FIFO-Frontdrop (FIFO-FD) LIFO-Taildrop (LIFO-TD) LIFO-Frontdrop (LIFO-FD)

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Probabilities of service and failure

Probabilities of service/failure given that packet arrives with system in state k

And for all disciplines

Single Queue Analysis

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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

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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

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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

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Average End-to-end Delay

Static ad hoc network scenario Comparable performance for low traffic LIFO disciplines have best and worst performance in

high traffic

UDP sending rate 5 Packet/seconds

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

5 10 20 30 50 70 90 100

Buffer size

En

d-t

o-e

nd

del

ay (

seco

nd

s)

FIFO-FD

FIFO-TD

LIFO-FD

LIFO-TD

UDP sending rate 20 Packet/seconds

0

2

4

6

8

10

12

14

16

18

20

5 10 20 30 50 70 90 100

Buffer size

En

d-t

o-e

nd

del

ay (

Sec

on

ds)

FIFO-FD

FIFO-TD

LIFO-FD

LIFOTD

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Average Jitter

Static ad hoc network scenario Low traffic: less than 40ms jitter for all 4

FIFO has lowest jitter

High traffic: LIFO-FD maintains less than 1sec jitter with buffer size increase

UDP sending rate 5 Packet/second

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

5 10 20 30 50 70 90 100

Buffer size

Ave

rag

e ji

tter

(S

eco

nd

s)

FIFO-FD

FIFO-TD

LIFO-FD

LIFO-TD

UDP sending rate 10 Packet/second

0

1

2

3

4

5

6

5 10 20 30 50 70 90 100

Buffer size

Ave

rag

e ji

tter

(S

eco

nd

s)

FIFO-FD

FIFO-TD

LIFO-FD

LIFO-TD

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Delay & Throughput in VANET

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

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Outline Motivation RBVT routing Forwarding optimizations

Distributed next-hop election Effect of queuing disciplines on VANET performance RBVT path predictions

Conclusions

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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

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Cellular Automata (CA) Traffic Model

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

(a) At time t

(b) At time t+1

car 2, v2=1car 1, v1=2

car 3, v3=1

car 1, v1=2

car 2, v2=2

car 3, v3=1

gap1 = 4 cells gap2 = 1 cell gap3 ≥ 3 cells

Lc = 7.5m

gap1 = 3 cells gap2 = 1 cell gap3 ≥ 2 cells

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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

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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

Algorithm to output non-transient states Directly obtaining non-transient states needed

0 2Time t

1 0Time t-1

0 3Time t

2 0Time t-1

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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

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Transition Matrix Generic transition probability from state of aggregated

Markov chain 2 1Road section 1 0

1 2 3 4 5 6 7 8 9 10Cell number

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

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Probabilistic Measures Stationary distribution π Connected states S1

Disconnected states S2 S1US2 =S S1∩S2 =Ø Expected duration of connectivity

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Probabilistic Measures (cont’d) Expected duration of disconnection

Probability of connection duration

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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

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Evaluation Method and Setup Simulation to validate

model Simulate CA freeway

model and SUMO Large ring layout

Connectivity of shaded area is analyzed

Complete ring affects shaded area

DTMC-CA considers shaded area only

Area of observationwith k cells

Total number of cells = 320 cells

SourceDestination

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Expected Connectivity Duration

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

50 vehicles 75 vehicles

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Expected Disconnectivity Duration

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

50 vehicles35 vehicles

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Probability Connectivity Duration

Longer uninterrupted connectivity less likely Larger k leads to smaller probabilities of connectivity

duration

k = 8 cells k = 10 cells

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Incorporating Path Estimates in RBVT Road-side sensors or historical data

Road segment densities and entry speeds probabilities

Improving route selection Duplicate routes received at the destination

Enhancing route maintenance of RBVT-R How long should the source wait when a route breaks

Determining RBVT-P CP generation interval Period between CP generation based on connectivity duration

Reducing overhead network traffic Likelihood of success of 100MB transmission (delay or divide in smaller

chunks)

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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

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

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59

Thank you!

Acknowledgments: This research was supported by the NSF

grants CNS-0520033 and CNS-0834585

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Impact of Number of 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

0

10

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50

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80

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1 5 10 15 20

Number of concurrent flows

Ave

rag

e d

eliv

ery

rati

o (

%)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

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Node Selection Using Waiting Function

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Overhead

Routing packets exchanged for each received data packet

Removing “hello” packets essentially eliminates most overhead

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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

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TCP Throughput and Fairness

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

6 flows, 5MB each10 flows, 2MB each

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DTMC-CA: Effect of Removing Invalid States

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SUMO Results

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Different Traffic Models - Different Results

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Connectivity Window Model Provide analytical model

independent of the traffic model

Uses the concept of connectivity window

Count vehicles in each window