Lecture 10 Overview
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Last Lecture
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Datacenter networking
This Lecture
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Mobile opportunistic networks
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Vehicular ad hoc networks
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Source: lecture note
Next Lecture
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LiFi networks
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Network on chips
Motivations
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Popularity of mobile computing devices
Motivations
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Wireless is everywhere.
Motivations
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Storage is cheap and vast.
Network Connectivity
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Expectation from end users
! Anywhere
! Anytime
! Any device
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Current situations
! Access internet through cellular data network
is still not cheap
! WiFi networks have limited coverage
Mobile Opportunistic Networks
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!
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Assumptions of TCP/IP are violated
! Limited end-to-end connectivity due to
mobility, power saving or unreliable links
Explore the advantages of short range
communication techniques
! Bluetooth
! WiFi/LiFi
“Store and forward” does not work
! Store – carry – forward
How does DTN work?
Traditional Routing
Store and Forward
Data
S
D
How does DTN work?
Data
Store –Carry- Forward
D
Data
S
Data
R
Features of mobile opportunistic networks
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High Latency & Low Data Rate
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Disconnection
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Hours or days
Short Range Contact
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Mobility & low duty-cycle
Long Queuing Time
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Long propagation delay
Asymmetric data rates
Only one-hop communication is guaranteed.
Dynamic Network Topology
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Different types of user behavior will result in dramatically
different network conditions.
Applications
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Vehicular Networks - “Drive-Thru Internet” (2)
Internet
email reply
email reply
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!
!
10
send email
send email
write email
Asynchronous operation: OK for e-mail!
Web caching; Local information; download news
Enough bandwidth even at high speeds!
Applications
Internet to Remote Communities
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Email, cached/asynchronous services
Use: Village bus, postman’s vehicle, passing cars
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!
11
Equip with radio, antenna, and storage
Use: dial-up, satellite, microwave links when available
Applications
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Mobile Social Network Service
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12
Smart phones
Tablets
PDAs
Laptops
…
Applications
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Mobile data offloading
13
Routing Protocol Classification
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Flooding-based protocols
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Forwarding-based protocols
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Forward a copy of each encountered node
Largest delivery rate assuming no storage constraint
Packets never replicate
Less wasteful of network resources
Low delivery rate
Replication-based protocols
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Nodes propagate packet replications
Consume more network resources
High delivery rate
Epidemic Routing
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Give a message copy to every node encountered
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essentially: flooding in a disconnected context
D
F
E
D
B
D
D
A
D
D
C
Epidemic Routing: Message Vectors
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Node A encounters node B
Message Vector of A
Dest ID
Seq. Num.
D
0
G
F
Message Vector of B
Dest ID
Seq. Num.
D
0
1
E
0
0
F
0
F
1
(G,1)
(E,0),(F,1)
16
Epidemic Routing: Message Vectors
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After message exchange
Message Vector of A
Message Vector of B
Dest ID
Seq. Num.
Dest ID
Seq. Num.
D
0
D
0
E
0
E
0
F
0
F
0
F
1
F
1
G
1
G
1
17
Epidemic Routing Performance
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How many transmissions?
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All nodes receive the message
What is the delay?
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Minimum among all possible routing schemes
If NO resource constraints (bandwidth, buffer space)
Controlled Replication
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Limit number of copies to L (small, fixed)
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Transmissions = L!
Spray and Wait (sigcomm 2005)
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Spray: For every message, L message copies are
initially spread to L distinct relays
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Wait: If the destination is not reached in the spraying
phase, each of the L nodes carrying a copy performs
direct transmission.
Two modes
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Normal: one copy per encountered node
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Binary: n/2 copies per encountered node
Spray and Wait (Binary Spraying)
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Use forwarding tokens; SRC starts with L tokens
When L = 1, can only forward to DST
L=1
D
F
E
L=1
L=1
B
L=4
D
Src
L=2
L=2
L=1
D
D
Dst
D
C
Opportunistic Routing = ? Random Routing
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So far all schemes are random: no assumptions about
relays
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!
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all relays equally fast, equally capable, similar mobility
epidemic, random flooding, 2-hop, spray & wait, etc.
Is real life that random and homogeneous???
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!
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Nodes have different capabilities (PDA, sensor, laptop, BS)
Nodes move differently (vehicles vs. pedestrians, 1st year
student vs. PhD)
Nodes have social relations
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!
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Same affiliation => same building, floor
Friends => meet more often than others
Learn and exploit the patterns => better routing
Predicting Future Encounters
Based on past encounter statistics
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!
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Mobility pattern non-random, but unknown
Essentially non-parametric learning\prediction
Per contact vs. end-to-end statistics
Grained Contact Characterisation
ows the pairwise contacts between two active devices randomly c
ta traces: Cambridge [Scott et al. 2006], MIT Reality [Eagle an
Prophet Routing
nd UMassDieselNet
[?]. It can be seen that most contacts occurr
me period of a day. This repeating pattern is useful to predict
! Like Epidemic routing, but maintains a probability of
wever, most existing schemes do not fully take this advantage. In
delivery
for each
node
pair p(i,D)
o nodes a and
b contact,
they
update
their encounter predictability,
s follows:
P (a, b) = Pold (a, b) + (1
Pold (a, b)) ⇤ Pencounter ,
is the encounter predictability before the current cont
is a scaling factor at which the probability increases upon encoun
l positive
valuei copies
to set an
upper to
bound
P (a, b)
[Lindgren et al.
! Node
message
j onlyon
if p(j,D)
> p(i,D)
messages based on this metric is simple but may not be efficient
old (a, b)
ACM Transactions on Internet Technology, Vol. V, No. N, Article A, Publication date:
3R – Fine-grained routing
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Fine-grained encounter pattern
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!
!
!
∆n |, which
ar. |∆n | is
used in the
rder [5]. In
|∆n | in the
Weekday vs. weekend
Each day is divided into multiple time slots
Estimate encounter probability in each time slot
Single-copy message routing
ν
weekday
weekday
weekday
weekday
...
weekend
weekend
...
τ
(08:00,09:00]
(09:00,10:00]
(10:00,11:00]
(12:00,13:00]
...
(08:00,09:00]
(09:00,10:00]
...
n1
0.4
0.1
0
0
...
0
0
...
Table IV
n2
0.2
0
0
0
...
0.8
0
...
n3
0.1
0.6
0.5
0.8
...
0
0.2
...
n4
0
0
0.6
0
...
0
0.7
...
Performance Comparison
!
Cambridge data trace (Scott et al. 2006)
108616
110543
L. W
109345
Fig. 2: Performance with unlimited message buffer size
duce message overhead, the average delivery rate is not dropped too mu
Fig. 2: Performance
with unlimited message buffer size
Performance
Comparison
! Cambridge
data trace
(Scott delivery
et al. 2006)
to reduce
message overhead,
the average
rate is not dropped too much as it
uses fine-grained encounter information for contact prediction.
42350
43512
43844
Fig. 3: Performance with limited message buffer size: 1M bytes/node
Fig. 3 shows the performance of the three schemes where the message buffer is
restricted to 1M bytes/node. The average delivery rates achieved by Epidemic and
PRoPHET are dropped to 0.43 and 0.45, respectively, and the performance of Epi
Model-based Prediction
Office
Cafeteria
50%
5%
Restaurant 2
Restaurant 1
5%
Rest of Area
2%
!
3%
Library
20%
Class
15%
Mobility Profile: Each node visits specific locations
more often
COMPUTER SCIENCE
Introduction to the
Kevin XIAO
Outline
VANET
Broadcast Storm
Broadcast Island
COMPUTER SCIENCE
What is VANET?
Vehicles connected to each others
through an ad hoc formation form a
wireless network called “Vehicular Ad
Hoc Network”.
Vehicular ad hoc networks (VANETs) are
a subgroup of mobile ad hoc networks
(MANETs).
COMPUTER SCIENCE
What is VANET?
VANET is an important component of ITS,
including:
V2V Communications: Vehicle To Vehicle
Communications
V2I Communications: Vehicle To
Infrastructure Communications
COMPUTER SCIENCE
What is VANET?
COMPUTER SCIENCE
What is VANET?
VANET Communication Standards
The VANET communication builds on IEEE 802.11p
WLAN operating on seven reserved channels in the
5.9 GHz frequency band.
COMPUTER SCIENCE
What is VANET?
COMPUTER SCIENCE
Why VANET
Increase the vehicles’ safety
21.8 million vehicle crashes from 1990 to 2015;
36,000 fatalities in 2005 only;
24,000 of these due to collision with other
vehicles/objects.
Cost more than $100 billion per year in
medical health and etc.
COMPUTER SCIENCE
Why VANET
COMPUTER SCIENCE
vb
c
va
vc
Why VANET
a
COMPUTER SCIENCE
Accleration
Overtaking
c
b
d
Why VANET
Increase the driving experience
Online videos in vehicles;
Smart driving;
Vehicle-Human interaction.
COMPUTER SCIENCE
Characteristics of VANET
High mobility of vehicles
Rapidly changing network topology.
Unbounded network size.
Potential support from infrastructure
Real-time, time sensitive data exchange.
Crucial effect of security and privacy.
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VANET Broadcast
COMPUTER SCIENCE
Broadcast Storm
COMPUTER SCIENCE
What is Broadcast Storm?
A broadcast storm occurs when a network system
is overwhelmed by continuous multicast or
broadcast traffic.
When different nodes are sending/broadcasting
data over a network link, and the other network
devices are rebroadcasting the data back to the
network link in response, this eventually causes
the whole network to melt down and lead to the
failure of network communication.
COMPUTER SCIENCE
How the Broadcast Storm occurs?
Poor network management
Poor monitoring of the network;
The use of cheap devices, including hubs,
switches, routers, cables, connectors, etc.;
Improperly maintained network configuration and
inexperienced network engineers.
COMPUTER SCIENCE
How the Broadcast Storm occurs?
Network Topology
The lack of a network diagram design,
which is needed for proper management
and to provide guidelines for all network
traffic routes. This can be done on paper
and with the help of application
software that creates an automated
network diagram.
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Broadcast Island
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Any solution?
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