message ferrying and other short stories: assisted data...
TRANSCRIPT
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Mostafa H
. Ammar
Mostafa H
. Ammar
Message
Ferrying and
Other Short
Message
Ferrying and
Other Short
Stories:
Stories:
Mob
ility
Mob
ility--Assisted D
ata D
elivery in Wireless
Assisted D
ata D
elivery in Wireless
Netw
orks
Netw
orks
Mostafa H
. Ammar
Mostafa H
. Ammar
College of Computing
Georgia Institute of Technology
Atlanta, GA
Message Ferry Project Group Members: Ellen Zegura, Wenrui Zhao,
Hyewon Jun, Jeonghwa Yang, Yang Chen, Shashi Merugu, Vincent Borrel, Ahmed Mansy,
Jon Olson, Mukarram Bin Tariq, Meng Guo
U. M
ass Collaborators: Brian Levine, Mark Corner
Funding: NSF, DARPA, Cisco
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Outline
�Intermittently-Connected Networks
�Message Ferrying
�The Space of Wireless and Mobile
�The Space of Wireless and Mobile
Networks
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The “Traditional” MANET Wireless
Paradigm
�The Network is “Connected”
�There exists a (possibly multi-hop) path
from any source to any destination
The path exists for a long-enough period
of
�The path exists for a long-enough period
of
time to allow meaningful communication
�If the path is disrupted it can be repaired
in short order
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A Brief History of Wireless Nets
�Wireless networks are as old as the Internetitself
�DARPA PRnet
�Initial motivation for some protocol functions (e.g., IP-layer
fragmentation)
�PRnet -> SURANet -> Mobile Ad-hoc Net (MANET)
�PRnet -> SURANet -> Mobile Ad-hoc Net (MANET)
�Latest MANET wave coincided with 802.11 activities
�Most wireless today is base-station oriented (not mobile, nor
ad-hoc)
�My conclusion:attempt to emulate wired net model
for MANET has led to failure to achieve wide
deployment
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The Rise of Intermittently-
Connected Networks
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Intermittently-Connected
Wireless Networks
�Disconnected
�By Necessity
�By Design (e.g. for power considerations)
By Design (e.g. for power considerations)
�Mobile
�With enough mobility to allow for some
connectivity over time
�Data paths may not exist at any one point
in time but do exist over time
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Mob
ility-Assisted D
ata D
elivery:
A New Communication Paradigm
�Mobility used for connectivity
�New
Forwarding Pa
radigm
Store
Carry for a w
hile
Carry for a w
hile
forw
ard
�Special nodes: Transport entities that are
not sources or destinations
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Also Known As
�Delay-Tolerant Networks
�Disruption-Tolerant Networks
�Sparse Networks
�Sparse Networks
�Opportunistic Networks
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Data Applications
�Nicely suitable for Message-Switching
�Delay tolerance… but can work at
multiple time scale
multiple time scale
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Epidemic Routing
�Vahdat and Becker
�Utilize physical motion of devices to
transport data
�Store-carry-forwardparadigm
�Store-carry-forwardparadigm
�Nodes buffer and carry data when disconnected
�Nodes exchange data when met
�data is replicated throughout the network
�Robust to disconnections
�Scalability and resource usage problems
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Epidemic Routing
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Epidemic Routing
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Epidemic Routing
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Epidemic Routing
me
ssa
ge
is
de
live
red
�
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The Trouble with ER
�Potentially high-failure rate
�Message duplication consumes nodal
resources
resources
�Some mobility patterns can cause
disconnection
�Can be improved with contact
probability information -Levine et al
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Other “Original” Systems
�ZebraNet and SWIM
�Data MULE and Smart-Tags
�Vehicle-to-Vehicle Communication
�Vehicle-to-Vehicle Communication
�Message Ferrying
�DakNet
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SWIM
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Vehicles on Highways Networks
Source
Destination
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Vehicles on Highways Networks
Source
Destination
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Vehicles on Highways Networks
Source
Destination
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Roadside-to-Roadside Relaying
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DakNet
(Pentland, Fletcher, and Hasson)
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Message Ferrying (MF) @ GT
�Zhao and Ammar
�Exploit non-randomnessin device
movement to deliver data
�A set of nodes called ferriesresponsible
�A set of nodes called ferriesresponsible
for carrying data for all nodes in the
network
�Store-carry-forward paradigm to
accommodate disconnections
�Ferries act as a moving communication
infrastructure for the network
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Message Ferrying System
(cont.)
S
MF
S
M
D
MF M
M
D
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Putting It All Together
�Common Features:
�Intermittent Connectivity
�Store, carry and forward
�Other dimensions where they may differ
�Other dimensions where they may differ
�Special Nodes?
�Source/Destination Mobile?
�Potential for controlling mobility for data
transport purposes?
�Data Communication Pattern
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More on Message Ferrying
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MF V
ariations
�Ferry Mobility
�Task-oriented, e.g., bus movement
�Messaging-oriented, e.g., robot movement
�Regular Node Mobility
�Regular Node Mobility
�Stationary
�Mobile: task-oriented or messaging-oriented
�Number of ferries and level of coordination
�Level of regular node coordination
�Ferry designation
�Switching roles as ferry or regular node
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Target Environments
�Needed for networks where
�Sparse network with no node contacts
�Not enough node contacts
Not enough node contacts
�Also usable in other networks
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A Taste of Message Ferrying
�Ferry Route Design Problem
�Single Ferry
�Multiple Ferries
Multiple Ferries
�MF with Mobile Nodes
�MF in MANETs!!
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A Taste of Message Ferrying
�Ferry Route D
esign Problem
Ferry Route D
esign Problem
��Single Ferry
Single Ferry
�Multiple Ferries
Multiple Ferries
�MF with Mobile Nodes
�MF in MANETs!!
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Ferry Route Design
Stationary N
odes
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Route Design -Stationary Nodes
�Ferry route problem
�Given node locationsand expected traffic
between nodes, find ferry routesuch that
ferry visits all nodes, meets traffic
ferry visits all nodes, meets traffic
requirements and minimizes average
message delay
�Solution
�A generalization of Traveling Salesman
Problem
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Numerical Results
�Experiment settings
�nnodes in 4km x 4km area
�A single ferry moving at speed 20m/s
�Node distributions
�Node distributions
�Random uniform node distribution (UN)
�Random clustered node distribution (CN)
�Traffic models
�Uniform traffic
�Non-uniform traffic
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Impact of Network Size
�MF provides reasonable performance
�For 40 nodes, each node can send at 10Kbps with
1070s delay.
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A Taste Message Ferrying
�Ferry Route D
esign Problem
Ferry Route D
esign Problem
�Single Ferry
��Multiple Ferries
Multiple Ferries
Multiple Ferries
Multiple Ferries
�MF with Mobile Nodes
�MF in MANETs!!
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Multiple Ferry Route Design
�Why multiple ferries?
�Data transport capacity
�Fault tolerance
�Multiple ferries introduce new problems
�Multiple ferries introduce new problems
�Which ferry serves which node?
�Interaction between ferries
�Tradeoff between number of ferries and
performance improvement
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Multiple Ferry Route Design
Problem
�Networks with nstationary nodes and m
ferries
�Ferries move at a constant speed and follow
periodic routes
periodic routes
�Bandwidth requirements are known
�e.g., node A sends to node B at 10kbps
�Problem: find
optimal ferry routes such
find
optimal ferry routes such
that bandwidth requiremen
ts are m
et and
that bandwidth requiremen
ts are m
et and
averag
e delay
is minimized
averag
e delay
is minimized
�NP-hard problem
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Algorithms
�Single Route Algorithm (SIRA)
�All ferries follow the same route
�No interaction between ferries
�Multiple Route Algorithm (MURA)
�Ferries can follow different routes
�Ferries can follow different routes
�No interaction between ferries
�Node Relaying Algorithm (NRA)
�Nodes relay data between ferries
�Ferry Relaying Algorithm (FRA)
�Data exchange between ferries
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Algorithms
�Single Route Algorithm (SIRA)
�All ferries follow the same route
�No interaction between ferries
�Multiple Route Algorithm (MURA)
�Ferries can follow different routes
�Ferries can follow different routes
�No interaction between ferries
�Node Relaying Algorithm (NRA)
�Nodes relay data between ferries
�Ferry Relaying Algorithm (FRA)
�Data exchange between ferries
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Algorithms
�Single Route Algorithm (SIRA)
�All ferries follow the same route
�No interaction between ferries
�Multiple Route Algorithm (MURA)
�Ferries can follow different routes
�Ferries can follow different routes
�No interaction between ferries
�Node Relaying Algorithm (NRA)
�Nodes relay data between ferries
�Ferry Relaying Algorithm (FRA)
�Data exchange between ferries
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Algorithms
�Single Route Algorithm (SIRA)
�All ferries follow the same route
�No interaction between ferries
�Multiple Route Algorithm (MURA)
�Ferries can follow different routes
�Ferries can follow different routes
�No interaction between ferries
�Node Relaying Algorithm (NRA)
�Nodes relay data between ferries
�Ferry Relaying Algorithm (FRA)
�Data exchange between ferries
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Simulation Results
�Settings
�5km x 5km area, 40 nodes, ferry speed:
10m/s
�Radio range: 100m, data rate: 10mbps
�Radio range: 100m, data rate: 10mbps
�Nodes send to random destinations
�Comparison of algorithms
�All algorithms achieve similar delay when
number of ferries is small or traffic load is
high
�MURA performs best
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Impact of Number of Ferries
10
10
0
Message Delivery Delay (hour)b
w=
10
Kb
ps
bw
=5
6K
bp
sb
w=
10
0K
bp
sb
w=
20
0K
bp
sb
w=
32
0K
bp
s
�The use of more ferries reduces message delay
and improves total transport capacity
�Scalability can be achieved by adding more ferries
0.1 1
0 2
4 6
8 1
0 1
2 1
4 1
6
Message Delivery Delay (hour)
Nu
mb
er
of
Fe
rrie
s
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A Taste Message Ferrying
�Ferry Route Design Problem
�Single Ferry
�Multiple Ferries
Multiple Ferries
��MF w
ith M
obile N
odes
MF w
ith M
obile N
odes
�MF in MANETs!!
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MF for N
etw
orks w
ith M
obile
MF for N
etw
orks w
ith M
obile
Nod
es
Nod
es
�Nodes are mobile and limited in
resources, e.g., buffer, energy
�Single ferryis used
�Single ferryis used
�Not limited in buffer or energy
�Data communication in messages
�Application layer data unit
�Message timeout
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Four Approaches
�Non-Proactive ( = Messaging-Specific)
mobility
�Ferrying without Epidemic Routing
�Ferrying with Epidemic Routing
�Ferrying with Epidemic Routing
�Proactive Routing Schemes
�Node-Initiated MF
�Nodes move to meet ferry
�Ferry-Initiated MF
�Ferry moves to meet nodes
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Four Approaches
�Non-Proactive ( = Messaging-Specific)
mobility
�Ferrying without Epidemic Routing
�Ferrying with Epidemic Routing
�Ferrying with Epidemic Routing
�Proactive Routing Schemes
�Node-Initiated MF
�Nodes move to meet ferry
�Ferry-Initiated MF
�Ferry moves to meet nodes
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Node-Initiated Message Ferrying
Meet
the
ferry?
OK
Working
If no, keep working
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Node-Initiated Message Ferrying
Go to Ferry
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Node-Initiated Message Ferrying
Send/Recv
Go to Work
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Node-Initiated Message Ferrying
Go to Work
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Node Trajectory Control
�Whether node should move to meet the ferry
�Goal: minimize message drops and reduce
proactive movement
�Go to ferry if
�Go to ferry if
�Work-time percentage> threshold
�and
�Estimated message drop percentage> threshold
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Simulations
�Ns simulations using 802.11 MAC and
default energy model
�40 nodes in 5km x 5km area
�25 random (source, destination) pairs
�25 random (source, destination) pairs
�Node mobility
�random-waypoint with max speed 5m/s
�Message timeout: 8000 sec
�Single ferry with speed 15m/s
�Rectangle ferry route
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Performance Metrics
�Message delivery rate
�Message Delay
�Number of delivered messages per unit
�Number of delivered messages per unit
energy
�Only count transmission energy in regular
nodes
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Message Delivery Rate
0.6
0.7
0.8
0.9 1
Message delivery rate
FIMF
NIMF
0
0.1
0.2
0.3
0.4
0.5
0.6 1
00
20
0 3
00
40
0 5
00
60
0 7
00
80
0
Message delivery rate
No
de
bu
ffe
r s
ize
(m
es
sa
ge
s)
Ep
ide
mic
Ro
uti
ng
Ep
ide
mic
Ro
uti
ng
(w
/ fe
rry)
NIM
FF
IMF
-NN
FIM
F-T
A
F w/ER
ER
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Message Delay
25
00
30
00
35
00
40
00
Message delay (sec)
FIMF
NIMF
0
50
0
10
00
15
00
20
00
25
00 1
00
20
0 3
00
40
0 5
00
60
0 7
00
80
0
Message delay (sec)
No
de
bu
ffe
r s
ize
(m
es
sa
ge
s)
Ep
ide
mic
Ro
uti
ng
Ep
ide
mic
Ro
uti
ng
(w
/ fe
rry)
NIM
FF
IMF
-NN
FIM
F-T
A
F w/ER
ER
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A Taste Message Ferrying
�Ferry Route Design Problem
�Single Ferry
�Multiple Ferries
Multiple Ferries
�MF with Mobile Nodes
��MF in MANETs!!
MF in MANETs!!
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MF as a Power-Management
Device in MANETs
�Introducing a MF in a well-connected
MANET can help organize power-
management activities
�Nodes can sleep when MF is out of
range.
�Can this improve on delay/power
tradeoff
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Power Management in MF
Ferry
Out
InOut
Ferry location in terms of the radio range of a
node
70
Node Search
Dorman
tContact
�The network model in MF
�A single ferry and multiple nodes
�Location and time: known by each node, e.g., using
GPS
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Sleeping Time Estimation
�How long to sleep in the dormant mode?
�Based on the predicted location of the
ferry
Movement scenarios
71
�Movement scenarios
Sta
tionary
nodes
Mobile
nodes
Str
ictly s
chedule
d1
3
Loosely
schedule
d2
4
Node
Ferry
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Performance Evaluation
�Ns-2 simulation with 802.11 MAC protocol
�50 nodes in 2000m x 500m and a ferry
�Capacity of node buffer: 700 messages
�Dynamic source routing (DSR) with a
75
�Dynamic source routing (DSR) with a
synchronous wake-up mechanism
�DSR-x: wake-up interval of x seconds
�DSR:CAM: continuous aware mechanism
�How to sleep (i.e., disabling or turning off):
decided based on the expected sleep time
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Impact of Traffic Load on
Stationary Nodes
76
�DSR with large wake-up intervals and MF:
Low energy consumption and high delivery delay
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A Fundamental Question
�What is the relationship of MF nets
with MANETs and with other DTNs?
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Und
erstand
ing theWireless and
Mob
ile
Netw
ork Spa
ce[CHANTS 07]
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Background
�Different Types of Wireless and Mobile
(WAM) Networks
�MANETS -> MANET Routing (AODV,DSR,…)
�DTNs (opportunistic,…) -> DTN routing (flooding,
MaxProp, Prophet, …)
MaxProp, Prophet, …)
�Sparse, Disconnected Nets -> Message Ferries,
Data Mules, Throwboxes, …
�Questions:
�What’s the relationship among these classes?
�How can one tell to which of these classes a
particular network belongs?
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Some Terminology
�A Space Path: A multi-hop path where
all the links are active at the same time
�A Space/Time Path: A multi-hop path
�A Space/Time Path: A multi-hop path
that exists over time
�NOTE: S path is a special case of S/T
path
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Space/Space-Time Paths
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The Wireless and Mobile (WAM) Space
(my panel presentation at WDTN 2005)
Mobility”
High
Spa
ce/T
ime Paths
Nod
e D
ensity
“Mobility High
Low
Spa
ce Paths
Low
No (Spa
ce/T
ime)
Paths
Hybrid Environments
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Mapping Routing Solutions to Space
Mobility”
High
Space/Time Paths
No
Path
Solu’ns
Nod
e D
ensity
“Mobility High
Low
Low
Space Paths
No (Space/Time)
Paths
Hybrid Environments
Space-Path Solutions
S/T
Path S
olutions
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Classifying WAMs
�Goal:
Provide a framework for WAM
classification that provides guidance
classification that provides guidance
about the deployment of routing
approaches
�Starting point: Theory of Evolving
Graphs
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Evolving Graph (EG)*
*A. F
err
eira. B
uild
ing a
refe
rence c
om
bin
ato
rial m
odel fo
r
MA
NE
TS
. IE
EE
Netw
ork
, 18(5
):24–29, S
et
2004.
�Is a graph with edges that turn “on”
and “off” over time
�Made up of a sequence of subgraphs
�Made up of a sequence of subgraphs
�Two variations: of EGs
�Finite-Duration
�Infinite Duration (our definition)
�Journey = Space-Time Path:
�Min-hop, foremost, shortest
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WAMs as Evolving Graphs
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Our Classification –
The idealized version
For Infinite-duration graphs
�Ideal Space-Path Networks (SPNs):
An infinite evolving graph where all
subgraphs are connected
subgraphs are connected
�Ideal Unassisted DTNs (U-DTN):
For all t and for all (i, j) there is a
journey from i to j after t
�Ideal Assistance-needed DTN (A-DTN):
Everything else
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SPNs, U-DTNs, A-DTNs
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Mapping Routing Families WAM
Classes
DTN ROUTING
MANET R
OUTING
SPA
RSE N
ET R
OUTING
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An “Practical” Classification
�Intuition: Boundary between classes is
not “crisp”.
�Additional Considerations:
�Additional Considerations:
�Finite-duration EGs
�Practical routing considerations, e.g., it
takes time for a MANET routing protocol
to discover routes.
�Many ways to do this –See CHANTS07
for one approach
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Classifying Networks From
Network Traces
�Goal
Network and Protocol
Parameters: η, δ, γ
Network Classifier
Network and Protocol
Parameters: η, δ, γ
Mobility Model or
Trace
Network
Classification
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Network Classification
�Objective: Percentage of time spent in
each network class.
X% SPN, Y% U-DTN, Z% A-DTN
X% SPN, Y% U-DTN, Z% A-DTN
�Informally:
�SPN: Network connected for long enough
period and with high link resilience
�U-DTN: Long enough in U-DTN class and
path-less time is < γ
�A-DTN otherwise
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Illustrative Examples
�RWP and RW mobility models
�2km x 2km, 250m radio range, 3hrs
�Pedestrian (avg 1.5m/s) and vehicular
�Pedestrian (avg 1.5m/s) and vehicular
(15m/s) speeds
�Goal: to illustrate classification
outcome from our approach
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Effect of Speed
RWP –Pedestrian Speed
RWP –Vehicular Speed
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Joint Density/Speed: RWP
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Remaining Issues
�Transformations between classes
�Partial classification (e.g., per S-D pair)
�More on practical parametric
�More on practical parametric
classification –direct analysis of
routing
�Experience using classification
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Concluding Remarks
�FINALLY! A realisticmobile wireless network
paradigm
�Within this new paradigm:
�Everything looks familiar but this is a truly
�Everything looks familiar but this is a truly
differentenvironment
�Techniques developed have wide applicability
�Fertile Groundfor both networking problems and
novel application paradigms
�Essential to understand entire WAM
space
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Concluding Remarks
�Mobility can be your friend
�Now the rest of the ICEBERG is visible
�MANETs are just a special case
�MANETs are just a special case
�So many networks so little time
�Can be solved by unified treatment of
entire WAM space
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Questions?