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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
43
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