Paradigm Shift in Protocol Design

– May end up with suboptimal performance or failures due to lack of context in the design

Design general purpose protocols

Evaluate using models

(random mobility, traffic, …)

Modify to improve performance and failures for specific context

Analyze, model deployment context

Design ‘application class’-specific parameterized protocols

Utilize insights from context analysis to fine-tune protocol parameters

Used to:

Propose to:

Problem Statement• How to gain insight into deployment context?• How to utilize insight to design future services?

Approach• Extensive trace-based analysis to identify dominant

trends & characteristics• Analyze user behavioral patterns

– Individual user behavior and mobility

– Collective user behavior: grouping, encounters

• Integrate findings in modeling and protocol design– I. User mobility modeling – II. Behavioral grouping

– III. Information dissemination in mobile societies, profile-cast

The TRACE framework

TraceAnalyze

Employ(Modeling & Protocol Design)

Characterize(Cluster)

Represent

ntt

n

xx

xx

,1,

,11,1

MobiLib

Vision: Community-wide Wireless/Mobility Library

• Library of– Measurements from Universities, vehicular networks

– Realistic models of behavior (mobility, traffic, friendship, encounters)

– Benchmarks for simulation and evaluation

– Tools for trace data mining

• Use insights to design future context-aware protocols? • http://nile.cise.ufl.edu/MobiLib

Trace

Libraries of Wireless Traces

• Multi-campus (community-wide) traces:– MobiLib (USC (04-06), now @ UFL)

• nile.cise.ufl.edu/MobiLib• 15+ Traces from: USC, Dartmouth, MIT, UCSD, UCSB, UNC,

UMass, GATech, Cambridge, UFL, …• Tools for mobility modeling (IMPORTANT, TVC), data mining

– CRAWDAD (Dartmouth)• Types of traces:

– University Campus (mainly WLANs)– Conference AP and encounter traces– Municipal (off-campus) wireless– Bus & vehicular wireless networks– Others … (on going)

Trace

Wireless Networks and Mobility Measurements

• In our case studies we use WLAN traces– From University campuses & corporate networks

(4 universities, 1 corporate network)– The largest data sets about wireless network users

available to date (# users / lengths)– No bias: not “special-purpose”, data from all users

in the network

• We also analyze – Vehicular movement trace (Cab-spotting)– Human encounter trace (at Infocom Conf)

Trace

Case study I – Individual mobilityT races

Ind iv idua luser m ob ility

O bserva tion

A pp lica tion

U ser g roupsin the

popu la tion

E ncoun terpa tte rns in

the ne tw ork

M obilitym odel

P ro file -castp ro toco l

S m allW orld -based

m essaged issem ination

M icroscop icbehav io r

M acroscop icbehav io r

Case Study I: Goal

• To understand the mobility/usage pattern of individual wireless network users

• To observe how environments/user type/trace-collection techniques impact the observations

• To propose a realistic mobility model based on empirical observations– That is mathematically tractable– That is capable of characterizing multiple classes

of mobility scenarios

IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis*

* W. Hsu, A. Helmy, “IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis”, two papers at IEEE Wireless Networks Measurements (WiNMee), April 2006

- 4 major campuses – 30 day traces studied from 2+ years of traces- Total users > 12,000 users - Total Access Points > 1,300

Trace source

Trace duration

User type

Environment Collection method

Analyzed part

MIT 7/20/02 – 8/17/02

Generic 3 corporate buildings

Polling Whole trace

Dartmouth 4/01/01 – 6/30/04

Generic

w/ subgroup

University campus

Event-based July ’03

April ’04

UCSD 9/22/02 – 12/8/02

PDA only University campus

Polling 09/22/02- 10/21/02

USC 4/20/05 – 3/31/06

Generic University campus

Event-based

(Bldg)

04/20/05-05/19/05

• Understand changes of user association behavior w.r.t.– Time - Environment - Device type - Trace collection method

Metrics for Individual Mobility Analysis

• What kind of spatial preference do users exhibit?– The percentile of time spent at the most frequently

visited locations

• What kind of temporal repetition do users exhibit?– The probability of re-appearance

• How often are the nodes present?– Percentage of “online” time

Represent

ntt

n

xx

xx

,1,

,11,1

•Individual users access only a very small portion of APs in the network.

•On average a user spends more than 95% of time at its top 5 most visited APs.

•Long-term mobility is highly skewed in terms of time associated with each AP.

•Users exhibit “on”/”off” behavior that needs to be modeled.

Observations: Visited Access Points (APs)

Pro

b.(c

over

age

> x

)

Fra

ctio

n of

onl

ine

tim

e as

soci

ated

wit

h th

e A

P

CCDF of coverage of users[percentage of visited APs]

Average fraction of time a MN associates with APs

•Clear repetitive patterns of association in wireless network users.

•Typically, user association patterns show the strongest repetitive pattern at time gap of one day/one week.

Repetitive Behavior

Mobility Characteristics from WLANs• Simple existing models

are very differentfrom the characteristicsin WLAN

Characterize

Pro

b.(o

nlin

e ti

me

frac

tion

> x

)

On/off activity pattern

Skewed location preference

Periodic re-appearance

Mobility Models• Mobility models are of crucial importance for the

evaluation of wireless mobile networks [IMP03]• Requirements for mobility models

– Realism (detailed behavior from traces)

– Parameterized, tunable behavior

– Mathematical tractability

• Related work on mobility modeling– Random models (Random walk/waypoint): inadequate for

human mobility

– Improved synthetic models (pathway model, RPGM, WWP, FWY, MH) – more realistic, difficult to analyze

– Trace-based model (T/T++): trace-specific, not general

• Skewed location visiting preference– Create “communities” to be the preferred area of movement– Each node can have its own community

• Node moves with two different

epoch types – Local or roaming– Each epoch is a random-direction,

straight-line movement – Local epochs in the community– Roaming epochs around the

whole simulation area

L

25%

75%

Employ

Time-variant Community (TVC) Model (W. Hsu, Thyro, K. Psounis, A. Helmy, “Modeling Time-variant User Mobility in Wireless Mobile

Networks”, IEEE INFOCOM, 2007, Trans. on Networking 2009)

Tiered Time-variant Community (TVC) Model• Periodical re-appearance

– Create structure in time – Periods– Node moves with different parameters in periods

to capture time-dependent mobility– Repetitive structure

• Finer granularity in space & time– Multi-tier communities– Multiple time periods

Time

TP1 TP2 TP3 TP1 TP2 TP3

Repetitive time period structure

Time period 1 (TP1) Time period 2 (TP2) Time period 3 (TP3)

C om m 43

C om m 13

C om m 23

C om m 33

C om m 12

C om m 22

C om m 11

C om m 21

C om m 31

Employ

Using the TVC Model – Reproducing Mobility Characteristics

• (STEP1) Identify the popular locations; assign communities

• (STEP2) Assignparameters to the communities according to stats

• (STEP3) Add user on-off patterns (e.g., in WLAN, users are usually off when moving)

Using the TVC Model – Reproducing Mobility Characteristics

• WLAN trace (example: MIT trace)

1 .E -0 6

1 .E -0 5

1 .E -0 4

1 .E -0 3

1 .E -0 2

1 .E -0 1

1 .E + 0 0

1 11 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1AP sorted by to tal am ou n t of tim e associated w ith it

M IT

M odel-sim plified

M odel-com plex

Ave

rage

frac

tion

of o

nlin

e tim

eas

soci

ated

with

the

AP

T im e g ap (d ay s)

Prob

.(Nod

e re

-app

ear a

t the

sam

eA

P af

ter t

he ti

me

gap)

0

0 .0 5

0 .1

0 .1 5

0 .2

0 .2 5

0 .3

0 2 4 6 8

M IT

M odel-sim plified

M odel-com plex

Skewed location visiting preference Periodic re-appearance

* Model-simplified: single community per node. Model-complex: multiple communities** Similar matches achieved for USC and Dartmouth traces

Using the TVC Model – Reproducing Mobility Characteristics

• Vehicular trace (Cab-spotting)

Ave

rage

frac

tion

of o

nlin

e tim

eas

soci

ated

with

the l

ocat

ion

1 .E -0 6

1 .E -0 5

1 .E -0 4

1 .E -0 3

1 .E -0 2

1 .E -0 1

1 .E + 0 0

1 11 2 1 3 1 4 1 5 1 6 1 71 8 1 9 1Loca tion sorted b y tota l am oun t of tim e associa ted w ith it

M ode lV eh icle -trace

0

0 .0 5

0 .1

0 .1 5

0 .2

0 .2 5

0 .3

0 2 4 6 8

V eh icle-trace

M odel

T im e gap (days)

Prob

.(Nod

e re

-app

ear a

t the

sam

elo

catio

n af

ter t

he ti

me

gap)

Using the TVC Model – Reproducing Mobility Characteristics

• Human encounter trace at a conference

0 .0001

0 .00 1

0 .01

0 .1

1

1 10 10 0 10 00 1000 0 1000 00M eetin g d u ra tio n (s)

C am b rid g e-IN F O C O M -trace

M o d el

Prob

(Mee

ting

dura

tion

> X

)

0 .0 0 0 0 1

0 .0 0 0 1

0 .0 0 1

0 .0 1

0 .1

1

10 100 1000 10000 100000 100000 0In te r-m eeting tim e (s)

16 hou rs

C am bridg e-IN F O C O M -trace

M odel

Prob

(Inte

r-mee

ting

time

> X

)

Inter-meeting time Encounter duration

time

A encounters B

Encounter duration

Inter-meeting time

T races

Ind iv idua luser m ob ility

O bserva tion

A pp lica tion

U ser g roupsin the

popu la tion

E ncoun terpa tte rns in

the ne tw ork

M obilitym odel

P ro file -castp ro toco l

S m allW orld -based

m essaged issem ination

M icroscop icbehav io r

M acroscop icbehav io r

Case study II – Groups in WLAN

Case Study II: Goal

• Identify similar users (in terms of long run mobility preferences) from the diverse WLAN user population– Understand the constituents of the population

– Identify potential groups for group-aware service

• In this case study we classify users based on their mobility trends (or location-visiting preferences)– We consider semester-long USC trace (spring 2006,

94days) and quarter-long Dartmouth trace (spring 2004, 61 days)

Representation of User Association Patterns

• We choose to represent summary of user association in each day by a single vector– a = {aj : fraction of online time user i spends at APj on day d}

• Summarize the long-run mobility in an “association matrix”

Represent

ntt

n

xx

xx

,1,

,11,1

-Office, 10AM -12PM-Library, 3PM – 4PM-Class, 6PM – 8PM

Association vector: (library, office, class) =(0.2, 0.4, 0.4)

ntt

ji

n

xx

x

x

xxx

,1,

,

1,2

,12,11,1

E ach row rep resen ts anassocia tion vecto r fo r a tim e slo t

E ach co lum n rep resen ts thepopu larity fo r a loca tion across tim e

A n en try rep resen ts thepercen tage o f on line tim e du ring

tim e slo t i at loca tion j

Eigen-behavior

• Eigen-behaviors: The vectors that describe the maximum remaining power in the association matrix (obtained through Singular Value Decompostion)

with quantifiable importance• Eigen-behavior Distance calculates similarity of users by

weighted inner products of eigen-behaviors.–

• Assoc. patterns can be re-constructed with low rank & error• Benefits: Reduced computation and noise

ji

jiji vuwwVUSim,

),(

2 )(max arg ,max arg1

1

'

111

kuuXuXuuXu

k

iii

uk

u

)(

1

22 XRank

i ikkw

Similarity-based User Classification• With the distance between users U and V

defined as 1-Sim(U,V), we use hierarchical clustering to find similar user groups.

0

0 .2

0 .4

0 .6

0 .8

1

0 0 .2 0 .4 0 .6 0 .8 1

In ter-g roupIn tra-g roupS eries3S eries4

D istan ce b e tw een u sers

CDF

A M V D

E ig en -b eh av io rd is tan ce

0

0 .2

0 .4

0 .6

0 .8

1

0 0 .2 0 .4 0 .6 0 .8 1

In ter-g roupIn tra-g roupS eries3S eries4

D istance be tw een users

CDF

A M V D E igen -behav io rd istance

USCDartmouth

*AMVD = Average Minimum Vector Distance

Validation of User Groups

• Significance of the groups – users in the same group are indeed much more similar to each other than randomly formed groups (0.93 v.s. 0.46 for USC, 0.91 v.s. 0.42 for Dartmouth)

• Uniqueness of the groups – the most important group eigen-behavior is important for its own group but not other groups

Significance score of top eigen-behavior for

USC Dartmouth

Its own group 0.779 0.727

Other groups 0.005 0.004

User Groups in WLAN - Observations• Identified hundreds of distinct groups of similar users• Skewed group size distribution – the largest 10 groups account

for more than 30% of population on campus. Power-law distributed group sizes.

• Most groups can be described by a list of locations with a clear ordering of importance

• We also observe groups visiting multiple locations with similar importance – taking the most important location for each user is not sufficient

U ser g roup size rank

Gro

up si

ze

1

1 0

1 0 0

1 0 0 0

1 1 0 1 0 0 1 0 0 0

D artm ou th5 4 0 *x^-0 .6 7U SC5 0 0 *x^-0 .7 5

T races

Ind iv idua luser m ob ility

O bserva tion

A pp lica tion

U ser g roupsin the

popu la tion

E ncoun terpa tte rns in

the ne tw ork

M obilitym odel

P ro file -castp ro toco l

S m allW orld -based

m essaged issem ination

M icroscop icbehav io r

M acroscop icbehav io r

Case study III – Encounter Patterns

Case Study III: Goal

• Understand inter-node encounter patterns from a global perspective – How do we represent encounter patterns?– How do the encounter patterns influence network

connectivity and communication protocols?

• Encounter definition:– In WLAN: When two mobile nodes access the same

AP at the same time they have an ‘encounter’– In DTN: When two mobile nodes move within

communication range they have an ‘encounter’

0.0001

0.001

0.01

0.1

1

0 0.2 0.4 0.6 0.8 1Fraction of user population (x)

Dart-03

Dart-04

USC

MITUCSD

Cambridge

Pro

b. (

uniq

ue e

ncou

nter

fra

ctio

n >

x)

Pro

b. (

tota

l enc

ount

er e

vent

s >

x)

CCDF of unique encounter count CCDF of total encounter count

•In all the traces, the MNs encounter a small fraction of the user population.

• A user encounters 1.8%-6% on average of the user population (except UCSD)

•The number of total encounters for the users follows a BiPareto distribution.

Observations: Encounters

Encounter-Relationship (ER) graph

• Draw a link to connect a pair of nodes if they ever encounter with each other … Analyze the graph properties?

Group of good friends…

Cliques with random links to join them Represent

ntt

n

xx

xx

,1,

,11,1

Av. Path Length

Clustering Coefficient (CC)

Small Worlds of Encounters

Nor

mal

ized

CC

an

d P

L

• The encounter graph is a Small World graph (high CC, low PL)

• Even for short time period (1 day) its metrics (CC, PL) almost saturate

• Encounter graph: nodes as vertices and edges link all vertices that encounter

Small World

Random graph

Regular graph

Background: Delay Tolerant Networks (DTN)

• DTNs are mobile networks with sparse, intermittent nodal connectivity

• Encounter events provide the communication opportunities among nodes

• Messages are stored and moved across the network with nodal mobility

A B

C

Information Diffusion in DTNs via Encounters

• Epidemic routing (spatio-temporal broadcast) achieves almost complete delivery

Unr

each

able

rat

io

(Fig: USC)

Robust to selfish nodes (up to ~40%)

Trace duration = 15 days

Robust to the removal of short encounters

•Top-ranked friends form cliques and low-ranked friends are key to provide random links (short cuts) to reduce the degree of separation in encounter graph.

Encounter-graphs using Friends• Distribution for friendship index FI is exponential for all the traces

• Friendship between MNs is highly asymmetric

• Among all node pairs: < 5% with FI > 0.01, and <1% with FI > 0.4

Profile-castW. Hsu, D. Dutta, A. Helmy, ACM Mobicom 2007

• Sending messages to others with similar behavior, without knowing their identity– Announcements to users with specific behavior V– Interest-based ads, similarity resource discovery

• Assuming DTN-like environment

A

B

E

C

Is B similar to V?Is E similar to V? D?

Is C/D similar to V?

Profile-cast Use Cases

• Mobility-based profile-cast– Targeting group of users who move in a particular

pattern (lost-and-found, context-aware messages, moviegoers)

– Approach: use “similarity metric” between users

• Mobility-independent profile-cast– Targeting people with a certain characteristics

independent of mobility (classic music lovers)– Approach: use “Small World” encounter patterns

Mobility-based Profile-cast

Mobility space

S

DD Scoped message

spread in the mobility space

S

D

N

N

N

N

Forward??

– Singular value decomposition provides a summary of the matrix (A few eigen-behavior vectors are sufficient, e.g. for 99% of users at most 7 vectors describe 90% of power in the association matrix)

ntt

ji

n

xx

x

x

xxx

,1,

,

1,2

,12,11,1

Profile-cast Operation

• Profiling user mobility– The mobility of a node

is represented by an association matrix

S N

N

NN

1. profiling

Each row represents an association vector for a time slot

An entry represents the percentage of online time during time slot i at location j

Sum. vectors

Profile-cast Operation

2. Forwarding decision

S N

N

NN

1. profiling• Determining user similarity

– S sends Eigen behaviors for the virtual profile to N

– N evaluated the similarity by weighted inner products of Eigen-behaviors

– Message forwarded if Sim(U,V) is high (the goal is to deliver messages to nodes with similar profile)

– Privacy conserving: N and S do not send information about their own behavior

ji

jiji vuwwVUSim,

),(

Profile-cast Evaluation

• Epidemic: Near perfect delivery ratio, low delay, high overhead

• Centralized: Near perfect delivery ratio, low overhead, a bit extra delay

• Decentral: provides tradeoff between delivery & overhead

• Random: poor delivery ratio

* Results presented as the ratio to epidemic routing

Epidemic

Decentral

Decentral

Decentral

Random

Random

Random

Random

- Decentralized I-cast achieves:> 50% reduction in overhead of Epidemic>30% increase in delivery of Random

Evaluation - Result

0 0.2 0.4 0.6 0.8 1 1.2 1.4

FloodingCentralized

Similarity 0.7Similarity 0.6Similarity 0.5

RTx m=1 TTL=inf.RTx m=3 TTL=inf.RTx m=6 TTL=inf.RTx m=9 TTL=inf.RTx m=inf. TTL=1RTx m=inf. TTL=5

3.13

2.56

2.06

1.80

2.11

1.47

Success Rate Delay Overhead

more overhead

92%45%

• Centralized: Excellent successrate with only 3% overhead.• Similarity-based: (1) 61% success rate at low overhead, 92% success rate at 45% overhead (2) A flexible success rate – overhead tradeoff• RTx with infinite TTL: Much more overhead undersimilar success rate• Short RTx with many copies: Good success rate/overhead, but delay is still long

S

Mobility Profile-cast (intra-group)Goal

S

Flooding

S

Flood-sim

S

Single long random walk

S

Multiple short random walks

Mobility Profile-cast (inter-group)

S

T.P.

S

T.P.

Goal Flooding

S

T.P.

Gradient-ascend

S

T.P.

Single long random walk

S

T.P.

Multiple short random walks

S

T.P.

Flooding_sim

Performance Comparison

0

0.2

0.4

0.6

0.8

1

1.2

Flooding Flooding_sim Gradient_acsend Few long RW Many short RW

sim<0.00010.0001<=sim<0.0010.001<=sim<0.010.01<=sim<0.10.1<=sim

Success rate - Large groupsGradient ascend helpsto overcome the difficult case – when the source is far from T.P.

Few long RW is better when S is far from T.P. but many short RW is betterwhen S is close to T.P.

Performance Comparison

0

500000

1000000

1500000

2000000

2500000

3000000

Flooding Flooding_sim Gradient_acsend Few long RW Many short RW

sim<0.00010.0001<=sim<0.0010.001<=sim<0.010.01<=sim<0.10.1<=sim

Delay - Large groups

Gradient ascend helpsto overcome the difficult case – when the source is far from T.P.

Few long RW is better when S is close toT.P. but many short RW is betterwhen S is close to T.P.

Gradient ascend has some extra delay compared with flooding

Profile-cast Initial Results

• Adjustable overhead/delivery rate tradeoff– 61% delivery rate of flooding with 3% overhead– 92% delivery rate with 45% overhead

• Better than single random walk in terms of delay, delivery rate

• Multiple short random walks also work well in this case

Mobility Independent Profile-cast

S

SS

S S

Goal Flooding SmallWorld-based

Single long random walk Multiple short random walks

Future Work

• Sending to a mobility profile specified by the sender– Gradient ascend followed by similarity

comparison (in the mobility space)

• Mobility independent profile-cast– The encounter pattern provides a network in

which most nodes are reachable– We don’t want to flood – How to leverage the

Small World encounter pattern to reach the “neighborhood” of most nodes efficiently?

Future Work– One-copy-per-clique in the “mobility space”

– We expect this to work because similarity in mobility leads to frequent encounters

S S

S

In terest sp ace M ob ility space P hysica l space

- D ifferen t legends rep resen t nodesw ith d iffe ren t m ob ility trends-W hite nodes d eno te the ta rge trec ip ien ts

0

0 .1

0 .2

0 .3

0 .4

0 .5

0 .6

0 .7

0 0 .2 0 .4 0 .6 0 .8 1U ser pa ir s im ila rity

Enco

unte

r Rat

io

Future Directions (Applications)

• Detect abnormal user behavior & access patterns based on previous profiles

• Behavior aware push/caching services (targeted ads, events of interest, announcements)

• Caching based on behavioral prediction

• Can/should we extend this paradigm to include social aspects (trust, friendship, …)?

• Privacy issues and mobile k-anonymity

Markov O(2) Predictor Accuracy VoIP User Prediction Accuracy

-VoIP users are highly mobile and exhibit dramatic difference in behavior than WLAN users-Prediction accuracy drops from ave ~62% for WLAN users to below 25% for VoIP users

On Mobility & Predictability of VoIP & WLAN UsersJ. Kim, Y. Du, M. Chen, A. Helmy, Crawdad 2007

Work in-progress

Motivates-Revisiting mobility modeling-Revisiting mobility prediction

visitorsvisitorsMales Females

University Campus

FraternitySorority

tracestraces

Gender-based feature analysis in Campus-wide WLANsU. Kumar, N. Yadav, A. Helmy, Mobicom 2007, Crawdad 2007

- Able to classify users by gender using knowledge of campus map-Users exhibit distinct on-line behavior, preference of device and mobility based on gender-On-going Work

-How much more can we know? -What is the “information-privacy trade-off”?

Real world group experiments (structural health monitoring)

sensor

sensorsensor

sensor

sensor

sensor

sensor sensor

sensor

sensor

sensorsensor

sensor

sensor

Instructor

WLAN/adhoc

WLAN/adhoc

sensor-adhoc

sensor-adhoc

sensor-adhoc

Multi-party conferenceTele-collaboration tools

WLAN/adhoc

Embedded sensor network

The Next Generation (Boundless) ClassroomStudents

-Integration of wired Internet, WLANs, Adhoc Mobile and Sensor Networks-Will this paradigm provide better learning experience for the students?

Challenges

Emerging Wireless & Multimedia Technologies

Protocols,Applications,

Services

Human Behavior

Mobility, Load

Dynamics

Future Directions: Technology-Human Interaction

The Next Generation Classroom

Human Behavior

Mobility, Load

Dynamics

Protocols,Applications,

Services

Emerging Wireless & Multimedia Technologies

Human Computer Interaction (HCI) & User Interface

Educational/Learning

Experience

Education

Psycology

CognitiveSciences

Mobility Models

Traffic Models

Protocol Design

Context-awareNetworking

Engineering

Application Development

Service Provisioning

Multi-Disciplinary Research

MeasurementsHow to Evaluate?

How to Capture?

How to Design?

Social Sciences

sensor

sensorsensor

sensor

sensor

sensor

sensorsensor

sensor

sensor

sensor

sensorsensor

sensor

sensor

sensor

sensorsensor

sensor

sensor

Disaster Relief (Self-Configuring) Networks

On-going and Future Directions Utilizing mobility

– Controlled mobility scenarios• DakNet, Message Ferries, Info Station

– Mobility-Assisted protocols• Mobility-assisted information diffusion:

EASE, FRESH, DTN, $100 laptop

– Context-aware Networking• Mobility-aware protocols: self-configuring,

mobility-adaptive protocols

• Socially-aware protocols: security, trust, friendship, associations, small worlds

– On-going Projects• Next Generation (Boundless) Classroom

• Disaster Relief Self-configuring Survivable Networks

Thank you!

Ahmed Helmy helmy@ufl.eduURL: www.cise.ufl.edu/~helmy

MobiLib: nile.cise.ufl.edu/MobiLib