Paradigm Shift in Protocol Design
Used to:
Design general purpose protocols
Evaluate using models
(random mobility, traffic, …)
Modify to improve performance and
failures for specific context
– May end up with suboptimal performance or failures due to lack
of context in the design
Propose to:
Analyze, model deployment context
Design ‘application class’-specific
parameterized protocols
Utilize insights from context analysis
to fine-tune protocol parameters
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
x1,1  x1,n
MobiLib



xt ,1  xt ,n
Trace
Characterize
(Cluster)
Represent
Analyze
Employ
(Modeling & Protocol Design)
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 mobility
T ra c e s
O b se rv a tio n
A p p lic a tio n
In d iv id u a l
u se r m o b ility
M o b ility
m odel
M ic ro sc o p ic
b e h a v io r
U se r g ro u p s
in th e
p o p u la tio n
E n c o u n te r
p a tte rn s in
th e n e tw o rk
P ro file -c a st
p ro to c o l
S m a llW o rld b a se d
m e ssa g e
d isse m in a tio n
M a c ro sc o p ic
b e h a v io r
Case Study I: Goal
• To understand the mobility/usage pattern of
individual wireless network users
• To observe how environments/user type/tracecollection 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*
- 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
USC
4/20/05 –
3/31/06
Generic
University
campus
Event-based 04/20/0505/19/05
(Bldg)
09/22/0210/21/02
• Understand changes of user association behavior w.r.t.
– Time - Environment - Device type - Trace collection method
* 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
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
x1,1  x1,n
 

xt ,1  xt ,n
Represent
Fraction of online time
associated with the AP
Prob.(coverage > x)
Observations: Visited Access Points (APs)
CCDF of coverage of users
[percentage of visited APs]
Average fraction of time a MN associates with APs
•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.
Repetitive Behavior
•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.
Mobility Characteristics from WLANs
Prob.(online time fraction > x)
• Simple existing models
are very different
from the characteristics
in WLAN
On/off activity pattern
Skewed location
preference
Periodic re-appearance
Characterize
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
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)
• 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

75%
25%
Employ
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
TP1
TP2 TP3
TP1
TP2 TP3
Repetitive time period structure
• Finer granularity in space & time
Time period 1 (TP1)
– Multi-tier communities
– Multiple time periods
C o m m 11
Time period 2 (TP2)
Time
Time period 3 (TP3)
C o m m 13
C o m m 21
C o m m 22 C o m m 1
C o m m 23
2
C o m m 33
C o m m 31
C o m m 43
Employ
Using the TVC Model – Reproducing
Mobility Characteristics
• (STEP1) Identify the popular locations; assign
communities
• (STEP2) Assign
parameters 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)
A P sorted by total am ou n t of tim e associated w ith it
11 21 31 41 51 61 71 81 91
1 .E + 0 0
1 .E -0 1
1 .E -0 2
M odel-sim plified
1 .E -0 3
1 .E -0 4
1 .E -0 5
M IT
M odel-com plex
1 .E -0 6
0 .3
Prob.(Node re-appear at the same
AP after the time gap)
Average fraction of online time
associated with the AP
1
0 .2 5
0 .2
M odel-sim plified
M IT
0 .1 5
0 .1
0 .0 5
M odel-com plex
0
0
Skewed location visiting preference
2
4
T im e g ap (d ay s)
6
Periodic re-appearance
* Model-simplified: single community per node. Model-complex: multiple communities
** Similar matches achieved for USC and Dartmouth traces
8
Using the TVC Model – Reproducing
Mobility Characteristics
• Vehicular trace (Cab-spotting)
L oca tion sorte d b y tota l a m ou n t of tim e a ssocia te d w ith it
1
11 21 31 41 51 61 71 81 91
1 .E -0 1
1 .E -0 2
1 .E -0 3
1 .E -0 4
1 .E -0 5
1 .E -0 6
M od e l
V e h icle -tra ce
0 .3
Prob.(Node re-appear at the same
location after the time gap)
Average fraction of online time
associated with the location
1 .E + 0 0
0 .2 5
M odel
0 .2
0 .1 5
V eh icle-trace
0 .1
0 .0 5
0
0
2
4
T im e g ap (d ay s)
6
8
Using the TVC Model – Reproducing
Mobility Characteristics
• Human encounter trace at a conference
10
100
In te r-m e e tin g tim e (s)
1000
10000
100000
M e e tin g d u ra tio n (s)
1
1000000
0 .1
10
100
1000
10000
1
M odel
0 .0 1
C a m b rid g e -IN F O C O M -tra c e
0 .0 0 1
0 .0 0 0 1
Prob(Meeting duration > X)
Prob(Inter-meeting time > X)
1
0 .1
M odel
0 .0 1
C a m b rid g e -IN F O C O M -tra c e
0 .0 0 1
1 6 h o u rs
0 .0 0 0 1
0 .0 0 0 0 1
Inter-meeting time
A encounters B
Encounter Inter-meeting
duration
time
Encounter duration
time
100000
Case study II – Groups in WLAN
T ra c e s
O b s e rv a tio n
A p p lic a tio n
In d iv id u a l
u s e r m o b ility
M o b ility
m odel
M ic ro s c o p ic
b e h a v io r
U s e r g ro u p s
in th e
p o p u la tio n
E n c o u n te r
p a tte rn s in
th e n e tw o rk
P ro file -c a s t
p ro to c o l
S m a llW o rld based
m essage
d is s e m in a tio n
M a c ro s c o p ic
b e h a v io r
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}
-Office, 10AM -12PM
-Library, 3PM – 4PM
-Class, 6PM – 8PM
Association vector:
(library, office, class) =(0.2, 0.4, 0.4)
• Summarize the long-run mobility in an
E ach row represents an
“association matrix” association vector for a tim e slot
 x 1 ,1 x 1 , 2
x
A n entry represents the
 2 ,1 
percentage of online tim e during  
tim e slot i at location j

 
 x t ,1 



xi, j



E ach colum n represents the
popularity for a location across tim e
x1 , n 
 
 

 
x t , n 
x1,1  x1,n
 

xt ,1  xt ,n
Represent
Eigen-behavior
• Eigen-behaviors: The vectors that describe the maximum
remaining power in the association matrix (obtained through
Singular Value Decompostion)
k 1
u1  arg max X  u , uk  arg max ( X   Xui ui' )  u k  2
u 1
u 1
i 1
wk   i 1
 i2
with quantifiable importance
• Eigen-behavior Distance calculates similarity of users by
weighted inner products of eigen-behaviors.
– Sim(U ,V )   wi w j ui  v j
i , j
• Assoc. patterns can be re-constructed with low rank & error
• Benefits: Reduced computation and noise
2
k
Rank( X )
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.
In te r-g ro u p
In tra -g ro u p
S e rie s3
S e rie s4
1
USC
1
0 .6
0 .4
A M V D E ig e n -b e h a v io r
d ista n c e
0 .4
AMVD
0 .2
Dartmouth
0 .8
E ig e n -b e h a v io r
d ista n c e
CDF
CDF
0 .8
0 .6
In te r-g ro u p
In tra -g ro u p
S e rie s3
S e rie s4
0 .2
0
0
0
0 .2 0 .4 0 .6 0 .8
D ista n c e b e tw e e n u se rs
1
0
*AMVD = Average Minimum Vector Distance
0 .2
0 .4
0 .6
0 .8
D ista n c e b e tw e e n u se rs
1
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
Group size
• 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
1000
D a rtm o u th
5 4 0 * x ^ -0 .6 7
USC
5 0 0 * x ^ -0 .7 5
100
10
1
1
10
100
U se r g ro u p siz e ra n k
1000
Case study III – Encounter Patterns
T ra c e s
O b s e rv a tio n
A p p lic a tio n
In d iv id u a l
u s e r m o b ility
M o b ility
m odel
M ic ro s c o p ic
b e h a v io r
U s e r g ro u p s
in th e
p o p u la tio n
E n c o u n te r
p a tte rn s in
th e n e tw o rk
P ro file -c a s t
p ro to c o l
S m a llW o rld based
m essage
d is s e m in a tio n
M a c ro s c o p ic
b e h a v io r
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
Fraction of user population (x)
0.4
0.6
0.2
0.8
1
1
Cambridge
0.1
UCSD
MIT
USC
0.01
Dart-04
0.001
0.0001
Prob. (total encounter events > x)
Prob. (unique encounter fraction > x)
Observations: Encounters
Dart-03
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.
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…
x1,1  x1,n
 

xt ,1  xt ,n
Cliques with random links to join them
Represent
Small Worlds of Encounters
Regular graph
Normalized CC and PL
• Encounter graph: nodes as vertices and edges link all vertices that encounter
Clustering
Coefficient (CC)
Small World
Av. Path Length
Random graph
• 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
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
Trace duration = 15 days
Unreachable ratio
(Fig: USC)
Robust to the removal of short encounters
Robust to selfish nodes (up to ~40%)
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
•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.
Profile-cast
W. 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
B
Is E similar to V?
E
Is B similar to V?
C
?
D
Is C/D similar to V?
A
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
N
SN
Forward??
S
D
D
Scoped message
spread in the mobility space
N
N
D
Profile-cast Operation
1. profiling
N
N
S N
– Singular
value
decomposition
• Profiling
user
mobility
provides
summary
ofnode
the
– Theamobility
of a
matrixis(Arepresented
few eigen-behavior
by an
vectors
are sufficient,
e.g. for
association
matrix
99% of users at most 7 vectors
describe 90% of power in the
x
  x 
association matrix)
x
1 ,1
N
Each row represents an
association vector for
time
 slot
 a

    







   

entry represents

x
 2 ,1
 

 
x
 t ,1
1, 2
1, n


x ,j
 
Sum. i vectors

    
  x t , n 
 

An
the percentage of
online time during time slot i at location j

Profile-cast Operation
1. profiling
N
N
S N
• Determining user similarity
– S sends Eigen behaviors for
the virtual profile to N
– N evaluated the similarity by
weighted inner products of
Eigen-behaviors
Sim(U ,V )   wi w j ui  v j
2. Forwarding decision
N
i , j
– 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
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
Epidemic
Decentral
Decentral
Decentral
Random
Random
Random
Random
- Decentralized I-cast achieves:
> 50% reduction in overhead of Epidemic
>30% increase in delivery of Random
* Results presented as the ratio to epidemic routing
Evaluation - Result
Success Rate
Delay
Overhead
Flooding
Centralized
Similarity 0.7
Similarity 0.6
Similarity 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=1
RTx m=inf. TTL=5
0
0.2
0.4
• Centralized: Excellent success
92% rate with only 3% overhead.
3.13 • Similarity-based:
45%
(1) 61% success rate at low
2.56 overhead, 92% success rate
more overhead
2.06 at 45% overhead
(2) A flexible success rate –
1.80 overhead tradeoff
2.11 • RTx with infinite TTL: Much
1.47 more overhead undersimilar
success rate
• Short RTx with many copies:
0.6 0.8 1 1.2 1.4 Good success rate/overhead,
but delay is still long
Mobility Profile-cast (intra-group)
Goal
Flooding
S
Flood-sim
S
Single long random walk
S
S
Multiple short random walks
S
Mobility Profile-cast (inter-group)
Goal
Flooding
S
T.P.
S
T.P.
Gradient-ascend
S
T.P.
Single long random walk
S
T.P.
Flooding_sim
Multiple short random walks
S
T.P.
S
T.P.
Performance Comparison
1.2
1
Gradient ascend helps
Success rate - Large groups to overcome the difficult case –
when the source is far from T.P.
0.8
sim<0.0001
0.0001<=sim<0.001
0.001<=sim<0.01
0.01<=sim<0.1
0.1<=sim
Few long RW is better
when S is far from T.P.
but many short RW is better
when S is close to T.P.
0.6
0.4
0.2
0
Flooding
Flooding_sim
Gradient_acsend
Few long RW
Many short RW
Performance Comparison
Gradient ascend helps
to overcome the difficult case –
when the source is far from T.P.
3000000
Delay - Large groups
Few long RW is better
when S is close toT.P.
but many short RW is better
when S is close to T.P.
sim<0.0001
0.0001<=sim<0.001
0.001<=sim<0.01
0.01<=sim<0.1
0.1<=sim
2500000
2000000
Gradient ascend
1500000 has some extra
delay compared
1000000 with flooding
500000
0
Flooding
Flooding_sim
Gradient_acsend
Few long RW
Many short RW
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
Goal
Flooding
S
SmallWorld-based
S
Single long random walk
S
S
Multiple short random walks
S
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”
- D iffe re n t le g e n d s re p re se n t n o d e s
w ith d iffe re n t m o b ility tre n d s
-W h ite n o d e s d e n o te th e ta rg e t
re c ip ie n ts
S
S
S
In te re st sp a c e
M o b ility sp a c e
P h y sic a l sp a c e
– We expect this to work because similarity in
mobility leads to frequent encounters
0 .7
0 .6
Encounter Ratio
0 .5
0 .4
0 .3
0 .2
0 .1
0
0
0 .2
0 .4
0 .6
U se r p a ir sim ila rity
0 .8
1
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
On Mobility & Predictability of VoIP & WLAN Users
J. Kim, Y. Du, M. Chen, A. Helmy, Crawdad 2007
Work in-progress
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
Motivates
-Revisiting mobility modeling
-Revisiting mobility prediction
Gender-based feature analysis in Campus-wide WLANs
U. Kumar, N. Yadav, A. Helmy, Mobicom 2007, Crawdad 2007
3500
Percentage Users
35
3000
M…
20
2000
15
1500
visitors
1000
10
500
0
25
University Campus
traces
traces
Area
5
0
Intel
Apple
Gem…
Ente…
Links…
ASKE…
D-Link
Ager…
Netg…
Average Duration (sec)
2500
Ma
le
30
Manufacturer
- 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”?
The Next Generation (Boundless) Classroom
Students
sensor
sensor
sensor
sensor
sensor
sensor-adhoc
Embedded sensor network
WLAN/adhoc
WLAN/adhoc
sensor
sensor
sensor
Multi-party conference
Tele-collaboration tools
sensor
sensor
sensor-adhoc
Instructor
WLAN/adhoc
Challenges
sensor
sensor
sensor
sensor
sensor-adhoc
-Integration of wired Internet, WLANs, Adhoc
Mobile and Sensor Networks
-Will this paradigm provide better learning
experience for the students?
Real world group experiments (structural health monitoring)
Future Directions: TechnologyHuman Interaction
The Next Generation Classroom
Emerging Wireless &
Multimedia Technologies
Protocols,
Applications,
Services
Human
Behavior
Mobility,
Load
Dynamics
Engineering
Multi-Disciplinary Research
Human Computer Interaction (HCI)
& User Interface
Social Sciences
Cognitive
Sciences
Education
Psycology
Application Development
Service Provisioning
Emerging Wireless &
Multimedia Technologies
How to Capture?
Protocols,
Applications,
Services
Human
Behavior
Educational/
Learning
Experience
Protocol Design
How to Evaluate?
Measurements
Mobility Models
Context-aware
Networking
How to Design?
Traffic Models
Mobility,
Load
Dynamics
Disaster Relief (Self-Configuring) Networks
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
sensor
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.edu
URL: www.cise.ufl.edu/~helmy
MobiLib: nile.cise.ufl.edu/MobiLib