Trace-driven Context-aware Mobile NetworksTowards Mobile Social Networks
Ahmed HelmyComputer and Information Science and Engineering (CISE) Dept
Mobile Networking Lab (NOMADS group)University of Florida
[email protected]://nile.cise.ufl.edu/MobiLib
Birds-Eye View: Mobile & Networking LabArchitecture & Protocol Design Methodology & Tools
Test Synthesis (STRESS)
Mobility Modeling(IMPORTANT, TVC)
Protocol Block Analysis (BRICS)
Query Resolution in Wireless Networks (ACQUIRE & Contacts)
Robust Geographic Wireless Services (Geo-Routing, Geocast, Rendezvous)
Gradient Routing (RUGGED)
Worms, Traceback in Mobile Networks
Behavioral Analysis in Wireless Networks
(IMPACT & MobiLib)
Multicast-based Mobility (M&M)
Mobility-Assisted Protocols (MAID)
Socially-Aware Networks
Network Simulator (NS-2)
Protocol Independent Multicast (PIM)
• Future network devices are mobile & ‘personal’– Very tight coupling between devices & humans
• Network performance significantly affected by (and affects) users behavior: – Movement, grouping, on-line activity, trust,
cooperation…
• How do users behave in mobile societies?• What kinds of protocols/networks survive &
perform well in highly mobile societies?
Introduction & Problem Scope
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
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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
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•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)
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
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-Office, 10AM -12PM-Library, 3PM – 4PM-Class, 6PM – 8PM
Association vector: (library, office, class) =(0.2, 0.4, 0.4)
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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
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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
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,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, 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)
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x
x
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,
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,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
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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
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
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 [email protected]: www.cise.ufl.edu/~helmy
MobiLib: nile.cise.ufl.edu/MobiLib
Emerging Wireless Communication
• Opportunities
• Challenges– Dynamic network structure– Decentralized service paradigm– Tight coupling between the devices and
individuals
Outline
Complete caseDetailed behavior analysis
Future Work
T races
Ind iv idualuser 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
Trace Sets
• Available information from WLAN traces– MAC addresses of the devices as identifiers– Location/Time of users (our main focus)
2197745 172.16.8.244_11009 44332230200 172.16.8.244_11009 133202257917 172.16.8.244_11009 6432285119 172.16.8.244_11009 10172297134 172.16.8.244_11009 71532304287 172.16.8.244_11023 6744
Node: e0_12_29_fc_ba_8c
AssociationStart time Location_ID Duration
Trace
Summary (Case Study I)
• We observe some omni-present mobility characteristics from WLANs.
• These characteristics are not captured by existing synthetic mobility models (i.e., hence the models are not realistic)
• We propose the Time-variant Community (TVC) model, which is realistic, theoretically tractable, and flexible
Theoretical Tractability
• For the TVC model, we can derive– Nodal spatial distribution – the demographic profile of
the mobility model– Average node degree – important for cluster
maintenance and geographic routing– Hitting time/ Meeting time – important for routing
performance analysis
• With low error when the communication range is small compared to the community sizes (communication disk < 25% of community)
Theoretical Tractability
Ave
rage
Nod
e D
egre
e
C om m unica tion R ange (K )
0
0 .5
1
1 .5
2
2 .5
3
0 1 0 2 0 3 0 4 0 5 0
M od el6 -s imM od el6 -th eoryM od el7 -s imM od el7 -th eoryM od el3 -s imM od el3 -th eory
Hitt
ing
time
(s)
0
5 0 0 0
1 0 0 0 0
1 5 0 0 0
2 0 0 0 0
2 5 0 0 0
3 0 0 0 0
3 5 0 0 0
4 0 0 0 0
4 5 0 0 0
5 0 0 0 0
1 0 2 0 3 0 4 0 5 0 6 0 7 0
M od el1 -s imM od el1 -th eoryM od el2 -s imM od el2 -th eoryM od el6 (m u lti_ tier)-s imM od el6 (m u lti_ tier)-th eory
C om m unica tion R ange (K )
Mee
ting
Tim
e (s)
C om m unica tion R ange (K )
0
3000
6000
9000
12000
15000
10 20 30 40 50 60 70
M od el5 (tiered _ com m )-s imM od el5 (tiered _ com m )-th eoryM od el7 (m u lti_ com m )-s imM od el7 (m u lti_ com m )-th eoryM od el3 -s imM od el3 -th eory
X
Y
Prob
(nod
e app
ears
at (X
,Y))
0 200 400 600 8000
200400
600800
0
0.02
0.04
0.06
0.08
0.1
Theory Derivation – Hitting Time
• Hitting time – the time for a node to move into the communication range of a randomly chosen target coordinate, starting from the stationary distribution
(hit)
Theory Derivation – Hitting Time
1. Weighted average conditioned on the relative location of the ‘target’
2. Calculate the unit-time hitting probability for each scenario
3. Calculate hitting probability for the whole time period
4. Calculate the conditional hitting time
comm
ii commcommHTHT )in target Pr()in target (
2_edge)(Communityd))(Ave_speeComm_range(2 movet
h PP
Application II: Trace-based Mobility Modeling
• Skewed location preference– Nodes spend 95% of time
at top 5 preferred locations.– Heavily visited “preferred
spots”
• Repetitive behavior– Nodes show up repeatedly at
the same location after integer multiples of days.
– Periodical “daily/weekly schedules”
Similarity-based User Classification: Association-based Representation*
• For a given day d, user assoc. vector is defined by n-element vector– a = {aj : fraction of online time user i spends at APj on day d}– ‘n’ is the number of APs– Use zero vector for off-line users
• Vector elements quantify relative attraction of AP to user• User Association Consistency
– User i is ‘consistent’ if daily assoc. vectors can be grouped into few clusters– Use clustering with Manhattan distance measure
naaaa 21
(AP2:library, 1:30PM-2:30PM)
(AP1:office, 10AM-12PM) (AP3: class, 6PM-8PM)
Association vector: (AP1, AP2, AP3) =(0.2, 0.4, 0.4)
n
iii babaD
1
),(
* W. Hsu, D. Dutta, A. Helmy, Mobicom 2007
Summarizing user associations
• Association matrix: concatenate user association vectors for all days into a matrix.
• To summarize, perform SVD and store the top-k eigen values/vectors.
• What value of k we have to use for a good representation of the matrix?– Captured matrix power =
• How much is the reconstruction error?– Matrix norms ||X-Xk||p/||X||p
where Daily association vector
p
ji
p
jipXX
),(
),(
aluesingular vth -i , i22
d
iii
k
iii
Trace % users # vectors (rank) power
USC (Bldg) 95% 6 90%
Dartmouth (AP)
92% 6 90%
Dartmouth (Bldg)
94% 4 90%Assoc. patterns can be re-constructed with low rank and low error* Matrix reconstruction error < 5%
Summarizing user associations
Clustering Users with Similar Behavior
• Exhaustive comparison of assoc. vectors: – Find average of |ajd - aid| over all days d for all i,j pairs– Drawback: O(nd2) for each pair
• Compare similarity of eigen-vectors obtained from SVD• Use weighted inner products of eigen vectors U, V
– ,– – wui = proportion of power of SV– D(U,V) = 1 - Sim(U,V)– Corr > 91% with exhaustive
Can achieve very good clustering efficiently using distributed computation
A handful of eigen-vectors can capture most of the behavior power
Encounter Events
• Derived from simultaneous associations to the same locations
• How many other nodes does a node encounter with?
Prob
. (un
ique
enc
ount
er f
ract
ion
> x
)
0.5On avg. only 2%~7% of population
Encounter-Relationship graph• To our surprise, disconnected pairs of
nodes are low!!
Dis
conn
ecte
d R
atio
(%
)
Summary (Case Study II)
• We use SVD to obtain eigen-behaviors of individual users.
• We use the eigen-behavior distances and hierarchical clustering to classify WLAN users into similar groups.
• This finding is useful for mobility modeling (identifying group sizes and their frequently visited locations), network management, abnormality detection, and group-aware protocol (i.e., profile-cast, our future work)
Summary (Case Study III)
• The distribution of encounters in real WLAN trace is very different from synthetic models
• The encounter-relationship graph displays SmallWorld characteristics
• Despite a low encounter ratio of the whole population, the encounter events lead to a robust, reachable network (with long delay).
T races
Ind iv idualuser 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
Future Work – Profile-cast
Goal
• To send messages to a group of nodes within the general population– The group is defined by the intrinsic behavior
patterns of the nodes (CISE students, library visitors, moviegoers)
– The sender does not know the network identities (addresses) of the destinations
• Different from multi-cast: No join/leave, no group maintenance
Largest number of female users is in social sciences and is much higherthan the male WLAN users in those buildings.Female users are surprisingly high (vs males) in the first 2 samples. WLAN activity was down Feb 07 due to lower enrollment in Spring and potentialchanges in the network.
Females in social, economic, admin and comm/journalism generally have longer session durations than males in those majors.In Engineering, music and chemistry the opposite is true.Session durations are decreasing indicating potential increase in mobility.
Apple consistently more popular in females than malesIntel (PCs) are more popular in males than femalesIncrease in use of Apple and Intel in general, and degradation in other brands
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 comparing with flooding
Mobility Independent Profile-cast
S
SS
S S
Goal Flooding SmallWorld-based
Single long random walk Multiple short random walks