Lecture 6: Vehicular Computing and Networking Cristian Borcea

Department of Computer Science

NJIT

2

GPS & navigation system On-Board Diagnostic (OBD) systems DVD player Satellite communication

Applications Accident alerts/prevention

Real-time re-routing

Entertainment

Roadside infrastructure

Internet

Cellular Cellular

Vehicle-to-vehicle

Roadside infrastructure

Communication Cellular network (3G/4G)

Vehicle to roadside (WiFi)

Vehicle to vehicle (WiFi)

3

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?

4

Introduction

VANET applications: EZCab & TrafficView

RBVT routing in VANET

Real-time re-routing in vehicular networks

5

6

Phase1: Fight with other people for a cab

The nightmare!

Phase2: Call a dispatching center … and wait, and wait

Need a cab

Use mobile ad hoc networks of cabs to book a free cab

Each cab has short-range wireless interface and GPS

Prototype over Smart Messages 7

A

B

C

D

E

F

G

H

PH=0.5

PD=0.5 PE=0.75

PF=0.0 PG=0.5

PB=0.375 PC=0.250 PA=0.187

Discovery Phase

Busy cab

Free cab

Free cab

Busy cab

Discover

8

Booking Phase

A

B

C

D

F

G

H

PH=0.5

PD=0.5 PE=0.75

PF=0.0 PG=0.5

PB=0.375 PC=0.250 PA=0.187

PD=0.5 PE=0.25

PB=0.250 PC=0.250 PA=0.125

E

Busy cab

Free cab 9

PB=0.25 PC=0.25

Updating phase

A

B

C

D

F

G

H

PH=0.5

PD=0.5 PE=0.25

PF=0.0 PG=0.5

PB=0.25 PC=0.75 PA=0.125

E

PC=0.75

PC=0. 50

PA=0.375

PA=0.375 PD=0.5 PE=0.25

Busy cab

Free cab 10

0

1

2

3

4

5

355 305 255 205 155

Number of free cabs from the 410 cabs

Avger

age

num

ber

of

hops

bet

wee

n a

booked

cab

and

its

corr

espondin

g c

lien

t

FloodingOn-demandProactive

Proactive books the closest cabs

Average distance increases as the number of free cabs

decreases

0

1

2

3

4

5

6

7

8

9

10

10 20 50

Number of cab requests per second

Av

ger

age

nu

mb

er o

f h

op

s

bet

wee

n a

bo

ok

ed c

ab a

nd

its

corr

esp

on

din

g c

lien

t

FloodingOn-demandProactive

11

What’s in front of

that bus?

What’s behind the

bend? On rainy days

On foggy days

12

Provides dynamic, real-time view of the traffic ahead

Initial prototype Laptop/PDA running Linux

WiFi & Omni-directional antennas

GPS & Tiger/Line-based digital maps

Road identification software

Second generation prototype adds Touch screen display

3G cards

Possibility to connect to the OBD system

13

Problem: How to disseminate information about

cars in dynamic ad-hoc networks of vehicles?

Solution: broadcast all data in one packet (simple

data propagation model)

Use aggregation to put as much data as possible in one

packet

Aggregate data for vehicles that are close to each other

Perform more aggregation as distance increases

Maintain “acceptable” accuracy loss 14

15

Current Vehicle

Parameters Aggregation ratio: inverse of the number of

records that would be aggregated in one record

Portion value: amount of the remaining space in the broadcast message

3. For every region, merge every two consecutive records closer than merge threshold

1. Calculate region boundaries

2. Calculate merge thresholds

High-density highway scenario

Ratio-based aggregation performs best overall

Visibility Accuracy 16

Introduction

VANET applications: EZCab & TrafficView

RBVT routing in VANET

Real-time re-routing in vehicular networks

17

Examples of node-centric MANET routing protocols

AODV, DSR, OLSR

Frequent broken paths due to high mobility

Path break does not always correspond to connectivity loss

Performance highly dependent on relative speeds of nodes

on a path

S

S N1 D

N1

D

a) At time t

b) At time t+Δt

N2

18

Examples of MANET geographical routing protocols

GPSR, GOAFR

Advantage over node-centric

Less overhead, high scalability

Subject to (virtual) dead-end problem

S

D Dead end road

N1

N2

19

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

I2 I1 I3

I6 I8 E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

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

20

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

21

Source broadcasts route discovery (RD) packet RD packet is rebroadcast using improved flooding

Nodes wait before rebroadcasting packet a period inverse proportional to distance from sender

▪ If overhear another packet transmission, no need to rebroadcast

Traversed intersections stored in RD header

I2 I1 I3

I6 I8 E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

N1

Re-broadcast

from B

Re-broadcast

from N1

22

Destination unicasts route reply (RR) packet back to source Route stored in RR header RR follows route stored in RD packet

I2 I1 I3

I6 I8 E

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

23

Data packet follows path in header

Geographical forwarding is used between intersections

I2 I1 I3

I6 I8 E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

Path in data

header

I8

I4

I6

I5

I7

I1

24

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

I2 I1 I3

I6 I8 E

car

Intersection j

I7

I4 I5

Ij

D

S

A

B

C

Source

Destination

N1

Re-broadcast

from B

Re-broadcast

from N1

25

Unicast connectivity packets (CP) 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

I2 I1 I3

I6 I8 E

car

Intersection j

I7

I4 I5

Ij

A

B

C

CP generator

1 2

3

4

5

6

7 8

9

n n-1

i Step i 26

CP content 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

27

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

28

“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 29

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

D RTS

CTS

(a) RTS Broadcast and Waiting Time Computation

(b) CTS Broadcast

(NULL) (0.115ms)

(0.201ms) (0.0995ms) r

r ns

n4

n1

n2

n3

n5

n6

D

ns

n4

n1

n2

n3

n5

n6

D Data

(c) Data Frame

r

ACK r

ns

n4

n1

n2

n3

n5

n6

D

30

Function takes 3 parameters Distance from sender to next-hop (dSNi)

Distance from next-hop to destination (di)

Received power level at next-hop (pi)

Weight parameters α1,2,3 set a-priori Their values determine weight of corresponding

parameter

31

32

Distributed next-hop self-election Increases delivery ratio

Decreases end-to-end delay

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

33

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

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)

Avera

ge d

eli

very

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)

Avera

ge d

eli

very

rati

o (

%)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

RBVT-P performs best

Consistently below 1sec in these simulations

RBVT-R delay decreases as the density increases

Fewer 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

dela

y (

Seco

nd

s)

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

dela

y (

Seco

nd

s)

AODV

GPSR

RBVT-P

OLSR

GSR

RBVT-R

34

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 35

DTMC-CA derives probabilistic measures based

only on vehicle density for a traffic mobility model

Microscopic Cellular Automaton (CA) freeway traffic

model

DTMC-MFT generalizes the approach used by

DTMC-CA to any vehicular mobility model

Focuses on macroscopic information of vehicles rather

than their microscopic characteristics

Values predicted by models are similar to

simulation results from validated CA traffic model 36

Enhancing route maintenance of RBVT-R

How long should the source wait when a route breaks

Network overhead decreases up to 50%

Delivery ratio and latency remain similar

37

Introduction

VANET applications: EZCab & TrafficView

RBVT routing in VANET

Real-time re-routing in vehicular networks

38

Use global real-time traffic

knowledge to dynamically guide

drivers to alternative routes

Goals: lower travel time for each driver,

avoid congestion

▪ Byproducts: reduce fuel consumption,

pollution

Use smart phones for instantly

deployable solution

39

Re-routing triggered when congestion predicted on certain road segments Congestion predicted using

▪ Segment-specific short term historical data (speed, volume)

▪ Static information (i.e., road capacity and speed limit)

▪ Speed-volume equations

Select of vehicles to be re-routed according to utility function E.g., remaining travel time

Selected vehicles provided with alternative paths that lower current predicted travel time Paths don’t have to be the shortest

Goal: avoid moving congestion from one segment to another 40

Privacy Reduce frequency with which drivers report their position, cloak

destination

Robustness System works with low penetration rate & in presence of drivers who

ignore guidance

Accurate real-time traffic view traffic Adapt number and frequency of reports submitted by smart phones

to balance accurate global traffic view with privacy

Effective real-time guidance Push guidance to drivers fast to allow them enough time to switch on

new route

Scalability Low communication overhead 41

Off-load some computation to vehicles: server distributes global traffic view to vehicles, which make local decisions

Better privacy & scalability Server + MANET: vehicles make collaborative decisions 42

MANET: best privacy protection and quickly predict congestion in small regions

Localized, non-optimal decisions

Peer-to-peer: same privacy benefits as MANET and acquire a global view of the traffic

Difficult to provide fast guidance; significant overhead

EZCab

1. http://cs.njit.edu/~borcea/papers/percom05.pdf

TrafficView

2. http://dl.acm.org/citation.cfm?id=1031487

3. http://cs.njit.edu/~borcea/papers/vtcsp04.pdf

RBVT routing

4. http://cs.njit.edu/~borcea/papers/ieee-tvt08.pdf

5. http://cs.njit.edu/~borcea/papers/acm-tomacs10.pdf

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1. Two decades of mobile computing

2. Infrastructure support for mobility

3. Mobile social computing

4. People-centric sensing

5. Programming mobile ad hoc networks

6. Vehicular computing and networking

7. Privacy and security in mobile computing

Location privacy

Location authentication

Trusted ad hoc networks

44