Tracedriven Contextaware Mobile Networks Towards Mobile Social Networks - PowerPoint PPT Presentation

Loading...

PPT – Tracedriven Contextaware Mobile Networks Towards Mobile Social Networks PowerPoint presentation | free to download - id: 26fdcc-MmJiZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Tracedriven Contextaware Mobile Networks Towards Mobile Social Networks

Description:

... of spatial preference do ... Analyze the graph properties? Group of good friends... Cliques with ... (1 day) its metrics (CC, PL) almost saturate ... – PowerPoint PPT presentation

Number of Views:119
Avg rating:3.0/5.0
Slides: 85
Provided by: Ahmed88
Learn more at: http://www.cise.ufl.edu
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Tracedriven Contextaware Mobile Networks Towards Mobile Social Networks


1
Trace-driven Context-aware Mobile
NetworksTowards Mobile Social Networks
  • Ahmed Helmy
  • Computer and Information Science and Engineering
    (CISE) Dept
  • Mobile Networking Lab (NOMADS group)
  • University of Florida
  • helmy_at_ufl.edu
  • http//nile.cise.ufl.edu/MobiLib

2
Birds-Eye View Mobile Networking Lab
3
Introduction Problem Scope
  • 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?

4
Paradigm Shift in Protocol Design
Used to
  • May end up with suboptimal performance or
    failures due to lack of context in the design

Propose to
5
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

6
The TRACE framework
MobiLib
Employ (Modeling Protocol Design)
7
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

8
Libraries of Wireless Traces
  • Multi-campus (community-wide) traces
  • MobiLib (USC (04-06), now _at_ 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)

9
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)

10
Case study I Individual mobility
11
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

12
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 gt 12,000 users -
Total Access Points gt 1,300
  • 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
13
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

14
Observations Visited Access Points (APs)
Fraction of online time associated with the AP
Prob.(coverage gt x)
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.

15
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.

16
Mobility Characteristics from WLANs
  • Simple existing modelsare very differentfrom
    the characteristicsin WLAN

17
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

18
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)
  • 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

19
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

20
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)

21
Using the TVC Model Reproducing Mobility
Characteristics
  • WLAN trace (example MIT trace)

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
22
Using the TVC Model Reproducing Mobility
Characteristics
  • Vehicular trace (Cab-spotting)

23
Using the TVC Model Reproducing Mobility
Characteristics
  • Human encounter trace at a conference

Inter-meeting time
Encounter duration
A encounters B
time
Encounter duration
Inter-meeting time
24
Case study II Groups in WLAN
25
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)

26
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
  • Office, 10AM -12PM
  • Library, 3PM 4PM-Class, 6PM 8PM

27
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

28
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.

USC
Dartmouth
AMVD Average Minimum Vector Distance
29
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

30
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

31
Case study III Encounter Patterns
32
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

33
Observations Encounters
Prob. (unique encounter fraction gt x)
Prob. (total encounter events gt 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.

34
Encounter-Relationship (ER) graph
  • Draw a link to connect a pair of nodes if they
    ever encounter with each other Analyze the
    graph properties?

35
Small Worlds of Encounters
  • Encounter graph nodes as vertices and edges link
    all vertices that encounter

Clustering Coefficient (CC)
Regular graph
Normalized CC and PL
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

36
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

37
Information Diffusion in DTNs via Encounters
  • Epidemic routing (spatio-temporal broadcast)
    achieves almost complete delivery

Robust to the removal of short encounters
Robust to selfish nodes (up to 40)
38
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 lt 5 with FI gt 0.01, and
    lt1 with FI gt 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.

39
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

C
B
E
D
A
40
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

41
Mobility-based Profile-cast
Scoped message spread in the mobility space
42
Profile-cast Operation
  • 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)
  • Profiling user mobility
  • The mobility of a node is represented by an
    association matrix

43
Profile-cast Operation
  • 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

44
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 gt 50 reduction
in overhead of Epidemic gt30 increase in
delivery of Random
45
Evaluation - Result
  • 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

46
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

47
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 dont want to flood How to leverage the
    Small World encounter pattern to reach the
    neighborhood of most nodes efficiently?

48
Future Work
  • One-copy-per-clique in the mobility space
  • We expect this to work because similarity in
    mobility leads to frequent encounters

49
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

50
On Mobility Predictability of VoIP WLAN
UsersJ. Kim, Y. Du, M. Chen, A. Helmy, Crawdad
2007Work 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

51
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?

52
The Next Generation (Boundless) Classroom
Students
sensor-adhoc
Embedded sensor network
Instructor
sensor-adhoc
Challenges
  • Integration of wired Internet, WLANs, Adhoc
    Mobile and Sensor Networks
  • Will this paradigm provide better learning
    experience for the students?

sensor-adhoc
53
Future Directions Technology-Human
InteractionThe Next Generation Classroom
Protocols, Applications, Services
Human Behavior
Emerging Wireless Multimedia Technologies
Mobility, Load Dynamics
54
Multi-Disciplinary Research
Engineering
Social Sciences
Human Computer Interaction (HCI) User Interface
Education
Cognitive Sciences
Psycology
Application Development
Service Provisioning
How to Capture?
Emerging Wireless Multimedia Technologies
Educational/ Learning Experience
Protocol Design
How to Evaluate?
Context-aware Networking
Measurements
Mobility Models
How to Design?
Traffic Models
55
Disaster Relief (Self-Configuring) Networks
56
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

57
Thank you!
  • Ahmed Helmy helmy_at_ufl.edu
  • URL www.cise.ufl.edu/helmy
  • MobiLib nile.cise.ufl.edu/MobiLib

58
Emerging Wireless Communication
  • Opportunities
  • Challenges
  • Dynamic network structure
  • Decentralized service paradigm
  • Tight coupling between the devices and individuals

59
Outline
60
Trace Sets
  • Available information from WLAN traces
  • MAC addresses of the devices as identifiers
  • Location/Time of users (our main focus)

Node e0_12_29_fc_ba_8c
AssociationStart time
Location_ID
Duration
2197745 172.16.8.244_11009 4433 2230200
172.16.8.244_11009 13320 2257917
172.16.8.244_11009 643 2285119
172.16.8.244_11009 1017 2297134
172.16.8.244_11009 7153 2304287
172.16.8.244_11023 6744
61
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

62
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 lt 25 of community)

63
Theoretical Tractability
64
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)
65
Theory Derivation Hitting Time
  • Weighted average conditioned on the relative
    location of the target
  • Calculate the unit-time hitting probability for
    each scenario
  • Calculate hitting probability for the whole time
    period
  • Calculate the conditional hitting time

66
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

67
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

Association vector (AP1, AP2, AP3) (0.2, 0.4,
0.4)
W. Hsu, D. Dutta, A. Helmy, Mobicom 2007
68
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-Xkp/Xp where

69
Summarizing user associations
Assoc. patterns can be re-constructed with low
rank and low error
Matrix reconstruction error lt 5
70
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 gt 91 with exhaustive

Can achieve very good clustering efficiently
using distributed computation
A handful of eigen-vectors can capture most of
the behavior power
71
Encounter Events
  • Derived from simultaneous associations to the
    same locations
  • How many other nodes does a node encounter with?

Prob. (unique encounter fraction gt x)
0.5
On avg. only 27 of population
72
Encounter-Relationship graph
  • To our surprise, disconnected pairs of nodes are
    low!!

Disconnected Ratio ()
73
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)

74
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).

75
Future Work Profile-cast
76
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

77
Largest number of female users is in social
sciences and is much higher than 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 potential changes
in the network.
78
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.
79
Apple consistently more popular in females than
males Intel (PCs) are more popular in males than
females Increase in use of Apple and Intel in
general, and degradation in other brands
80
Mobility Profile-cast (intra-group)
Goal
81
Mobility Profile-cast (inter-group)
Goal
Flooding
Flooding_sim
82
Performance Comparison
Gradient 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.
83
Performance Comparison
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 helpsto overcome the difficult
case when the source is far from T.P.
Gradient ascend has some extra delay comparing
with flooding
84
Mobility Independent Profile-cast
Goal
Flooding
SmallWorld-based
Single long random walk
Multiple short random walks
About PowerShow.com