Used to:
Design general purpose protocols
Evaluate using models
(random mobility, traffic, …)
Deployment context: Modify to improve performance and failures for specific context
– May end up with suboptimal performance or failures due to lack
Propose to:
Analyze, model deployment context
Design ‘application class’-specific parameterized protocols
Utilize insights from context analysis to fine-tune protocol parameters

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

MobiLib
T race x
1 , 1
 x t , 1


 x
1 , n
 x t , n
R epresent
A nalyze
C haracterize
( C luster)
E mploy
(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
T race

• Multi-campus (community-wide) traces:
– MobiLib (USC (04-06), now @ UFL)
• nile.cise.ufl.edu/MobiLib
• 25+ 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)
T race

• 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)
T race

T ra c e s
O b s e rv a tio n
In d iv id u a l u s e r m o b ility
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
A p p lic a tio n
M o b ility m o d e l
M ic ro s c o p ic b e h a v io r
P ro file -c a s t p ro to c o l
S m a llW o rld b a s e d m e s s a g e 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

• 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
MIT
Trace duration
7/20/02 –
8/17/02
Dartmouth 4/01/01 –
6/30/04
User type
Generic
Generic w/ subgroup
Environment Collection method
3 corporate buildings
University campus
Analyzed part
Polling Whole trace
Event-based July ’03
April ’04
UCSD
USC
9/22/02 –
12/8/02
4/20/05 –
3/31/06
PDA only
Generic
University campus
University campus
Polling
Event-based
(Bldg)
• Understand changes of user association behavior w.r.t.
– Time - Environment - Device type - Trace collection method
09/22/02-
10/21/02
04/20/05-
05/19/05
* 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

• 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 x
1 , 1
 x t , 1


 x
1 , n
 x t , n
R epresent

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.

• Simple existing models are very different from the characteristics in WLAN
Skewed location preference
On/off activity pattern
Periodic re-appearance
C haracterize

• 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, random direction, smoothed RWP, etc.): inadequate for human mobility
– Improved synthetic models (pathway model, RPGM, WWP,
FWY, MH) – more realistic, difficult to analyze, do not capture repetitive behavior
– 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

25%
75%
E mploy

TVC
• 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
Repetitive time period structure
• Finer granularity in space & time
Time period 1 (TP1)
TP1 TP2 TP3
Time period 2 (TP2)
– Multi-tier communities
C o m m
1
1
C o m m
1
2
C o m m
2
1 C o m m
2
2
– Multiple time periods
Time period 3 (TP3)
C o m m
1
3
Time
C o m m
2
3
C o m m
3
3
C o m m
4
3
C o m m
3
1
E mploy

• (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, in Cab spotting: vehicles ‘on’ when moving)

TVC
• WLAN trace (example: MIT trace)
1 .E + 0 0
A P so rted b y to tal am o u n t o f tim e asso ciated w ith it
1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1
1 .E -0 1
1 .E -0 2
1 .E -0 3
1 .E -0 4
M o d el-sim p lified
1 .E -0 5
1 .E -0 6
M o d el-co m p lex
M IT
CCDF
Skewed location visiting preference
0 .3
0 .2 5
0 .2
0 .1 5
0 .1
0 .0 5
0
0
M IT
M o d el-sim p lified
M o d el-co m p lex
2 4
T im e g a p (d a y s)
6
Periodic re-appearance
8
* Model-simplified: single community per node. Model-complex: multiple communities
** Similar matches achieved for USC and Dartmouth traces

• Vehicular trace (Cab-spotting)
L oca tion sorte d b y tota l a m ou n t of tim e a ss ocia te d w ith it
1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1
1 .E + 0 0
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
0 .2 5
0 .2
0 .1 5
0 .1
0 .0 5
0
0
M o d el
V eh icle-trace
2 4
T im e g a p (d a y s)
6 8

TVC
• Human encounter trace at a conference
1 0 0
In te r-m e e tin g tim e (s )
1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0
1 1 0
M e e tin g d u ra tio n (s )
1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0
1
1 0
1
M o d e l
0 .1
0 .1
M o d e l
0 .0 1
C a m b rid g e -IN F O C O M -tra c e
0 .0 1
0 .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
1 6 h o u rs
0 .0 0 0 1 0 .0 0 0 0 1
Inter-meeting time A encounters B Encounter duration time
Encounter duration
Inter-meeting time

T r a c e s
O b s e r v a tio n
I n d iv id u a l u s e r m o b ility
U s e r g r o u p s in th e p o p u la tio n
E n c o u n te r p a tte r n s in th e n e tw o r k
A p p lic a tio n
M o b ility m o d e l
M ic r o s c o p ic b e h a v io r
P r o f ile - c a s t p r o to c o l
S m a llW o r ld b a s e d m e s s a g e d is s e m in a tio n
M a c r o s c o p ic b e h a v io r

• 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’

1
0 0.2
Fraction of user population (x)
0.4
0.6
0.8
1
Cambridge
0.1
UCSD
MIT
0.01
USC
Dart-04
0.001
0.0001
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.

• Draw a link to connect a pair of nodes if they ever encounter with each other …
Analyze the graph properties?
Group of good friends… x
1 , 1
 x t , 1


 x
1 , n
 x t , n
Cliques with random links to join them R epresent

Bkgnd:Graphs , Path Length and Clustering
Regular Graph
- High path length
- High clustering
0.8
0.6
1
Small World Graph:
Low path length, High clustering Random Graph
- Low path length,
- Low clustering
0.4
0.2
Clustering
Path Length
0
0.0001
0.001
0.01
0.1
1 probability of re-wiring (p)
In Small Worlds , a few short cuts contract the diameter (i.e., path length) of a regular graph to resemble diameter of a random graph without affecting the graph structure (i.e., clustering)
[H03]

• Encounter graph: nodes as vertices and edges link all vertices that encounter
Regular graph Clustering
Coefficient (CC)
Av. Path Length
Small World
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

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

• Epidemic routing (spatio-temporal broadcast) achieves almost complete delivery
Trace duration = 15 days
(Fig: USC)
Robust to the removal of short encounters Robust to selfish nodes (up to ~40%)

• 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.

T r a c e s
O b s e r v a tio n
I n d iv id u a l u s e r m o b ility
U s e r g r o u p s in th e p o p u la tio n
E n c o u n te r p a tte r n s in th e n e tw o r k
A p p lic a tio n
M o b ility m o d e l
M ic r o s c o p ic b e h a v io r
P r o f ile - c a s t p r o to c o l
S m a llW o r ld b a s e d m e s s a g e d is s e m in a tio n
M a c r o s c o p ic b e h a v io r

• 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)

• We choose to represent summary of user association in each day by a single vector
– a = { a j
: fraction of online time user i spends at AP j on day d }
-Office, 10AM -12PM
Association vector:
-Library, 3PM – 4PM
(library, office, class) =(0.2, 0.4, 0.4)
-Class, 6PM – 8PM
• Sum long-run mobility in “association matrix”
E ach ro w rep resen ts an asso ciatio n v ecto r fo r a tim e slo t
A n en try rep resen ts th e p ercen tag e o f o n lin e tim e d u rin g tim e slo t i at lo catio n j







 x x x


1
2 t
, 1
, 1
, 1 x
1


, 2 x
 i

E ach co lu m n rep resen ts th e
, j


 p o p u larity fo r a lo catio n acro ss tim e x x
1 , n t



, n







 x
1 , 1
 x t , 1


 x
1 , n
 x t , n
R epresent

Each row represents the percentage of time spent at each location for a day
Dorm
An entry represents the percentage of online time during time day i at location j








0 .
5 x x


2 , 1 t , 1
0 .
4


 x i , j




0 .
1 x


 t , n








Each column corresponds to a location
Office
Illustration of the association matrix to describe a given user’s location visiting preference .

• Eigen-behaviors: The vectors that describe the maximum remaining power in the association matrix M (obtained through Singular Value Decompostion, SVD )
-
Get Eigen-vectors:
-
Get relative importance:
- Get Eigen-values:
• Eigen-behavior Distance calculate similarity of users by weighted inner products of Eigen-behaviors.
–
Sim ( U , V )
  w i w j u i
 v j
 i , j • Assoc. patterns can be re-constructed with low rank & error
• For over 99% of users, < 7 vectors capture > 90% of M ’s power

1
0 .8
• With the distance between users U and V defined as 1-Sim(U,V), we use hierarchical clustering to find similar user groups.
USC In te r-g ro u p
In tra -g ro u p
S e rie s 3
S e rie s 4
1
In te r-g ro u p
In tra -g ro u p
S e rie s 3
S e rie s 4
Dartmouth
0 .8
0 .6
E ig e n -b e h a v io r d is ta n c e
0 .6
0 .4
0 .4
A M V D E ig e n -b e h a v io r d is ta n c e
0 .2
A M V D
0 .2
0
0
0 0 .2
0 .4
0 .6
0 .8
1
D is ta n c e b e tw e e n u s e rs
0
* AMVD = Average Minimum Vector Distance
0 .2
0 .4
0 .6
0 .8
D is ta n c e b e tw e e n u s e rs
1

• 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
Its own group
Other groups
USC
0.779
0.005
Dartmouth
0.727
0.004

• 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
1 0 0 0
1 0 0
D a rtm o u th
5 4 0 * x ^ -0 .6 7
U S C
5 0 0 * x ^ -0 .7 5
1 0
1
1 1 0 1 0 0
U s e r g ro u p s iz e ra n k
1 0 0 0

W. Hsu, D. Dutta, A. Helmy, ACM Mobicom 2007, WCNC 2008, Trans. Networking To appear
• 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
Is E similar to V ?
E
Is B similar to V ?
A
B
C
?
D
Is C/D similar to V ?

• Mobility-based profile-cast (Target mode)
– 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 (Dissemination mode)
– Targeting people with a certain characteristics independent of mobility (classic music lovers)
– Approach: use “Small World” encounter patterns

Mobility space
D
S
D
Scoped message spread in the mobility space
S
N
Forward??
N
N
N
D

N
S N
1. profiling
N matrix (A few eigen-behavior
N
99% of users at most 7 vectors describe 90% of power in the association matrix)
Each row represents an






 


 













 x x

 t
1
2 ,
, 1
, 1
1
1


, 2

Sum. vectors
 x i , j
  



  x x
1 , n


 t , n







 online time during time slot i at location j

N
1. profiling
N
S N
2. Forwarding decision
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 )
 
 i , j w i w j u i
 v 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

Goal Epidemic Group-spread
S S S
Single long random walk Multiple short random walks
S S

Goal Epidemic Group-spread
T.P.
S
T.P.
S
T.P.
S
Gradient-ascend Single long random walk Multiple short random walks
T.P.
S
T.P.
S
T.P.
S

* Results presented as the ratio to epidemic routing
Over 96% delivery ratio – Over 98% reduction in overhead w.r.t. Epidemic
- RW < 45% delivery
- Strikes a near optimal balance between delivery, overhead and delay
- Other variants (e.g., multi-copy, simulated annealing) under investigation

– N -copy-per-clique in the “mobility space”
S
S
- 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
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
– Challenge: From mobility to interest and other classifications

Future of Modeling For Mobile Networks Design
• Realistic spatio-temporal mobility models are necessary but not sufficient
• Need traffic, interest, class-based, trust (and other?) models
• Various population scale (indiv, pair-wise, group), granularity (cell tower, bldg, AP)
• How to enrich the models with social context?

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

3500
Gender-based feature analysis in Campus-wide WLANs
U. Kumar, N. Yadav, A. Helmy, Mobicom 2007, Crawdad 2007
35
3000
2500
M…
30
25
2000
1500
1000
500
0 visitors
University Campus
20
15
10
5
0
Ma le traces traces
Manufacturer
Area
- 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”
?

• 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

The Next Generation (Boundless) Classroom
Students sensor sensor sensor sensor sensor sensor-adhoc
Embedded sensor network
WLAN/adhoc
WLAN/adhoc sensor sensor sensor sensor sensor sensor-adhoc
Multi-party conference
Tele-collaboration tools
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)

Emerging Wireless &
Multimedia Technologies
Protocols,
Applications,
Services
Human
Behavior
Mobility,
Load
Dynamics

Multi-Disciplinary Research
Engineering
Human Computer Interaction (HCI)
& User Interface
Social Sciences
Cognitive
Sciences
Education
Psycology
Application Development
Service Provisioning
How to Capture?
Emerging Wireless &
Multimedia Technologies
Protocol Design
Measurements
Mobility Models
Protocols,
Applications,
Services
Educational/
Learning
Experience
How to Evaluate?
Human
Behavior
Context-aware
Networking
Traffic Models
How to Design?
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

• The Network Simulator (NS-2) (USC/ISI, UCB, Xerox Parc)
[wireless extensions CMU/Rice]
– www.isi.edu/nsnam
• The GloMoSim Simulator (UCLA)/QualNet (Commercial)
• The IMPORTANT Mobility Tool (USC/UF)
– nile.cise.ufl.edu/important
• Time Variant Community (
TVC ) (UF/USC)
– nile.cise.ufl.edu/~helmy (click on TVC model)
• The Obstacle Mobility simulator (UCSB)
– moment.cs.ucsb.edu/mobility
• The CORSIM Simulator
• OPNET (commercial)

• Includes:
– Mobility generator tools for FWY, MH, RPGM, RWP,
RWK (future release), City Section (Rel. Sp 05)
– Acts as a pre-processing phase for simulations, currently supports NS-2 formats (can extend to other formats)
– Analysis tools for mobility metrics (link duration, path duration) and protocol performance
– (throughput, overhead, age gradient tree chars)
– Acts as post-processing phase of simulations
– nile.cise.ufl.edu/important

Manhattan
IMPORTANT
Freeway
Group RWP

CORSIM (Corridor Traffic Simulator)
• Simulates vehicles on highways/streets
• Micro-level traffic simulator
– Simulates intersections, traffic lights, turns, etc.
– Simulates various types of cars (trucks, regular)
– Used mainly in transportation literature (and recently for vehicular networks)
– Does not incorporate communication or protocols
– Developed through FHWA (federal highway administration) http://ops.fhwa.dot.gov
– Need to buy license

Ahmed Helmy helmy@ufl.edu
URL: www.cise.ufl.edu/~helmy
MobiLib: nile.cise.ufl.edu/MobiLib

Goal Flooding SmallWorld-based
S S S
Single long random walk Multiple short random walks
S S

Ahmed Helmy helmy@ufl.edu
URL: www.cise.ufl.edu/~helmy
MobiLib: nile.cise.ufl.edu/MobiLib

– N -copy-per-clique in the “mobility space”
S
S
- 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
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
0 .5
0 .4
0 .3
0 .2
0 .1
0
0 0 .2
0 .4
0 .6
U s e r p a ir s im ila rity
0 .8
1