Doctoral thesis defense Arezu Moghadam 13 May 2011

1

Application platform, routing protocols and behavior models in mobile

disruption-tolerant networks (DTNs)

Doctoral thesis defense

Arezu Moghadam13 May 2011

D

D

Introduction

2

Communication in mobile DTNs :1 – No knowledge of the routes beyond the immediate hop2 – Mobility3 – Opportunistic

InternetWiFi or 3G

?? D1

D2

D1

D2

DTN: Disruption-Tolerant Networks 3

4

Introduction Applications of mobile DTNs:

Covering regions with no infrastructure, e.g. natural disasters Retrieving data from remote sensor networks Sharing music, news, pictures in the subway or networks of

pedestrians Collaborative ad-hoc environments

Challenges of mobile DTNs Networking and connectivity No application server or end-to-end communication path Different routing requirements and models Performance of the applications and routing algorithms relies on

the mobility behavior of mobile users

5

Problem scope MobileDTNs

MobileDTNs

MobilityMobilityRoutingRouting

A modularapp. platform

Popularity-based and interest-aware

communicationmodels

Markov-basedmobility model

and routing algorithm

Application

6


MobileDTNs

ApplicationsApplications MobilityMobility

Class of disruption-

tolerant

Core functional

requirements

RoutingRouting

A modularApp platform

7

Motivation

8

Problem

?Internet

3G

9

Solution

7DS platform Provides a class of

disruption-tolerant applications

Store-carry-forward communication

Node and service discovery Web, email, file-

synchronization and bulletin-board

Modular platform for application developers

InternetSuman Srinivasan, Arezu Moghadam, Se Gi Hong, Henning G Schulzrinne, "7DS - Node Cooperation and Information Exchange in Mostly Disconnected Networks", IEEE International Conference on Communications (ICC), Jun 2007.

10

Email exchange

Mobile nodes act as mail transport agents (MTA) Email client configuration

SMTP server is set to the 7DS local MTA in the email client Database

TTL, relays identities to avoid loops.

11

File synchronization7DS nodes running file-sync application

(view of the nodes before sync).

Shared folder content:test1.txt=2e6480af642eeba3;1170886792000test2.txt=a66a86c11861cb0e;1170957333000

Shared folder content:test1.txt=2e6480af642eeba3; 1170886792000test3.doc=a6ba76c21861db5e;1170757443000

Shared folder content:test1.txt=2e6480af642eeba3; 1170886792000test4.doc=c78a56b341861cd06;1170867833000

Shared folder content:test1.txt=2e6480af642eeba3; 1170886792000test2.txt=a66a86c11861cb0e;1170957333000test4.doc=c78a56b341861cd06;1170867833000

DiscoveryDiscovery

DiscoveryDiscovery

7DS nodes running file-sync application (view of the nodes after sync).

All shared folders content after sync:test1.txt=2e6480af642eeba3;1170886792000test2.txt=a66a86c11861cb0e;1170957333000test3.doc=a6ba76c21861db5e;1170757443000test4.doc=c78a56b341861cd06;1170867833000

SyncSync

SyncSync

Pull-based: automatic download

12

Bulletin board system Push-based data sharing Data exchange should be

approved by the user Metadata in an XML format

7DS Access Boxat 116th & Broadway

1. User publishes announcements on the bulletin board.

2. Users can search for and read bulletin board announcements.

Users can generate and share content in the spirit of Web 2.0

1

1

2

2

2

13

User interface

EmailBulletinBoard

File Synchronization

Webserver

Proxy server

Cachemanager

Mail Transport

Agent

Multicastengine

Deltacompression

DiscoveryModule

Web query

Supportservices

APIs

Search engine

Datasharing

APPs

BonAHAA thin wrapper

around Apple’s Bonjour

BonAHAA thin wrapper

around Apple’s Bonjour

Emulates a connected communication path in

the absence of Internet

Emulates a connected communication path in

the absence of Internet

Fetches the locally cached

web pages.

Fetches the locally cached

web pages.

Query the local neighbors

Query the local neighbors

Search the internal cache

Search the internal cache

1 - Arezu Moghadam, Suman Srinivasan, Henning Schulzrinne, "7DS - A Modular Platform to Develop Mobile Disruption-tolerant Applications", Second IEEE Conference and Exhibition on Next Generation Mobile Applications, Services, and Technologies (NGMAST 2008) , Sep 2008. 2 - Suman Srinivasan, Arezu Moghadam, Henning Schulzrinne, "BonAHA: Service Discovery Framework for Mobile Ad-Hoc Applications", IEEE Consumer Communications & Networking Conference 2009 (CCNC'09), Jan 2009.

. Implementation of the Rsync algorithm

. A more efficient use of the BW and contact opportunity. Useful when someone has a newer version of the stale file (>>)

> rsync

14


MobileDTNs

ApplicationsApplications MobilityMobilityRoutingRouting



communicationmodels



15


MobileDTNs


Lack of groupcommunication

model

Popularity-based Interest-aware

model

16

Routing Problem Store-carry-forward

Storage constraints Routing objectives:

Minimize delay Maximize throughput

Per-hop routing vs. source routing No end-to-end path

MANET’s routing protocols fail Proactive and reactive

No knowledge of the topology Time varying connectivity graph

Unicast vs. Multicast

))(),(,),(( tdtcvue nn

S

u

x

w

v

D

Each edge is a contact meaningan opportunity to transfer data.

> Routing Models

Problem – lack of group communication model for mobile DTNs? Any cast communication model

Emergencies Traffic congestion notifications Severe weather alerts

Traditional multicast as a group communication model Fails! No knowledge of the topology No infrastructure to track group memberships

Communication with communities of interest Even a harder problem! Market news, sport events Scientific articles Advertisement about particular products

Epidemic routing

17

18

Solution – interest-aware communication model

Our one-to-many communication model with communities of users

Objective: transmitting data to users who are interested in the content

Assumptions No previous knowledge about

the location of the recipients No knowledge about the

mobility behavior of users No previous knowledge about

interests of users Uniform probability of

encounter

S

X

Y

Y

D1

1

1

3

3

3

3

wireless contactdata transfer

Y

a

b

c

d

e

f

g

2D

4

4

D

D

X

X

X

Arezu Moghadam, Henning Schulzrinne, "Interest-aware content distribution protocol for mobile disruption-tolerant networks", 10th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks , Kos, Greece, Jun 2009.

Interest Vector

User profiling for the Web Profiles users based on their downloaded or reviewed

web content, clicked hyperlinks and… Music

The genre of the music user is playing more often Topic and category of the documents user has

downloaded

Monitoring Behavior

Interests – IV

MusicReviewedwebpages

Downloadeddocuments

Restaurantreviews

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cache j

j

jkI

1

cache i

i

ikI

D

D3

correlation(D , ) > jkI

2 jkI

jkI : Interest-vector of node j ?

Solution – interest-aware communication model

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LSA

w1 ... w j ... w J

d1

...

d i

...

d I

D

W

...

...

...

...

Document-Term Matrix

User profiling for the Web Profiles users based on their

downloaded or reviewed web content, clicked hyperlinks and…

Latent Semantic Analysis A low-dimensional topic-based

representation of web documents is obtained

Then low-dimensional representations are clustered to semantic groups

> Web recommender

22

Singular Value Decomposition (SVD)

A U

= x x

m x n m x r r x r r x n

TV

23

k= x x

m x n m x r r x r r x n

TkV

k

k

kA kU

Tkkkk VUA K << r

1~ kkTUDD

kI

Singular Value Decomposition (SVD)

> Sim

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Rock

Soul

Pop

P2PMusic Bulletin Board

Adele

Madonna

Vampire weekend

Miles Davis

Reviews all

JazzJazz

Interest-aware music sharing app.

?

Problem with interest-aware: Greedy!

S

X

Y

Y

D1

1

1

3

3

3

3

wireless contactdata transfer

Y

a

b

c

d

e

f

g

2D

4

4

D

D

X

X

X

Yh

5D

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Solution – PEEP Still interest-aware

Interest vectors; binary Learning interests: feedback from user, # data items of

each category, play times for music files, or LSA Transmit-budget

Amount of data items allowed for transmission at each connection

How to divide the transmit budget?

Popularity Should be estimated

1 2Items of interest? Others?

1 0 0 1 1 1 0

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Popular

Arezu Moghadam, Henning Schulzrinne, "PEEP: Popularity-based and Energy Efficient Protocol for Data Distribution in Mobile DTNs ", CCNC'2011 - Smart Spaces and Personal Area Networks, Las Vegas, USA, Jan 2011.

T1 T2 T3 T4 T5 T6 T7

Popularity estimation

Contact window N History of the users’ interests Average or weighted average

Example: C=6, N=8 Replace the oldest

i iI

NP

1

1 0 1 0 0 1

1 0 0 1 1 1

0 1 0 0 0 0

1 0 0 1 0 0

0 0 1 0 0 0

0 1 0 0 0 0

1 1 0 0 0 0

1 0 1 0 0 0

.62 .37 .37 .25 .12 .25

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T1 T2 T3 T4 T5 T6

Evaluation of PEEP

28> Simulation details

29


MobileDTNs

Applications RoutingRouting MobilityMobility



communicationmodels



30


MobileDTNs


Markov models toModel users’

movement

Markov-basedRouting algorithm

31

Mobility is a crucial factor!

partition

D

S

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Mobility models Mobility models usage

Application provisioning and evaluation of routing protocols performance analysis

QoS in cellular networks Problem: Inadequacy of the current synthetic and trace-based

mobility models Trace-based studies

Precision and granularity Specific population of study

Our empirical analysis based on a new set of traces Calculating patterns of human movement and using it in designing

routing protocols

> Levy

33

Problem with the current models Synthetic models mostly based on RWP

Simplified assumptions about human movement Synthesized or trace-driven models

Cellular networks Handoff predictions for QoS Movement of the node is not important within the cell

Mobile DTNs No cell-tower or AP

Impact of the mobility is higher on data propagation Traces or models extracted for cellular networks are not fine-grained

enough! Traces from a limited number of users from a specific class Traces from APs with not enough granularity

Arezu Moghadam, Tony Jebara, Henning Schulzrinne, “A Markov Routing Algorithm for Mobile DTNs based on Spatio-Temporal Modeling of Human Movement Data ", ACM MSWiM 2011 , Miami Beach, FL, USA, Oct 2011.

Spatial and Temporal Patterns

34

8 AM: Home

9 am:Drop kid @

school

10 AM:Work

12 pm:Café X

1 PM:Work

4 pm:Coffee X 6 PM:

Work

7 pm:Shop Y

8 pm:Home

10 pm:Bar Z

12am~8am Home

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Sense Network’s traces GPS traces of a wide-spectrum of

mobile users Citysense application

Nightlife discovery Friend-finder

Privacy concerns People are owners of their own data

GPS precision of 20 feet compared to 1~20miles cell-tower coverage

Population of 10,000 users

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Data presentation

Sequence of gridsG1, G1, G17, G23,…, GN…

Learning mechanism Ngrams

A subsequence of N items from a sequence

Modeling sequences in NLP, gene sequence analyzing, speech recognition

Goal: most probable future locations

Pattern Likelihood of traversing a given

sequence.

123456789

101112

A B CD E F GH I J K L M NO

Tuples of Grids

5039907665

5038663466

5038414624

5038414623

5060063904

5053345115

. . .

5039907665

1370 10 230 10 0 30 . . .

5038663466

30 130 110 0 0 0 . . .

5038414624

220 110 3420 120 0 60 . . .

5038414623

10 0 50 0 14 0 . . .

5060063904

0 0 12 110 0 0 . . .

5053345115

0 50 0 13 176 343 . . .

.

...

.

...

.

...

.

.. . .. . .

Triple of Grids 5039907665

5038414624

5050607875

5053345115

5038414623

. . .

5039907665

5039907665

1180 110 30 30 10 . . .

5039907665

5038414624

0 230 0 0 0 . . .

5038414624

5038414624

220 2820 10 60 110 . . .

5038414624

5039907665

110 100 0 0 0 . . .

5039907665

5050607875

0 20 10 0 0 . . .

5050607875

5038414624

0 30 133 0 44 . . .

.

...

.

...

.

...

.

.. . .. . .

Ngrams

G1 , G2 , … , Gi , … , Gn Training

Extract bigram and trigram tables.

Testing Calculating the likelihood of

a new observation

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)(log

),...,,,...,,,(log 1321

i

Nii

GP

GGGGGG

Markov chains for users’ movement Set of states

S = {S1, s2, …, sr} Transition matrix

Transitions correspond to consecutive GPS pings

users’ mobility profiles Pattern

States should be positive recurrent

Finite hitting times with prob. 1 Matrix of hitting times

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xxx xxxxx

xx

xx

x xxxxxx xx

xx xx

xxx

x

xx

xxxx xx

xxx

xx

xx

x xxxxxx xx

xx xx

xx

xx

xxxxxx

x

xx

xx

x

xxx

xx

xxx

x xxx

xxxx

xx

xxx

xxx

x

x

xx

x

x

xx

xx

50%

xx

x

25%10%

xxx

xx

xx

grid

s (1

00ft

)

grids (100ft)

ijP

Markov-based routing algorithm

Absorption (hitting) times = number of transitions until

chain arrives at state j starting @ i

Select the relay (r) with less absorption time than source (s).

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1 2 3 4

1.0.7529

.0882.625

.0588

1.0

.3750.1

ijN

ijij NET

nnnn

n

n

ttt

ttt

ttt

T

.

....

.

.

21

22221

11211

sijj

rij TT

Monte Carlo simulation

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1 2 3 4

1.0.7529

.0882.625

.0588

1.0

.3750.1 Mobility

Generator Engine

--------------Sampling from the Markov Chains

MobilityGenerator

Engine--------------Sampling from the Markov Chains

Users’ locations after each transition

Routing Algorithm Emulator

Routing Algorithm Emulator1 2 3 4

1.00.7

0.150.6

0.05

0.20.20.1 5

0.30.2

0.3

0.7

0.3

1 2 3

0.4

.0.3

0.6.625

.3750.1

0.6

Delay = #transitions

Energy = #transmissions

Performance measure

Performance objective Delay Consumed energy

Family of α-epidemics Measure performance

curve:

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SS

RRRR

RR

RR

RRRR

RR

RR

RR

RR

α = 100%α = 70%α = 30%

)(energyfdelay

)(energyfdelay MBMB ?

Evaluation of results

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α = 1

α = 0.1

α = 0.7

α = 0.2

α = 0.3

Random DestinationRandom Destination Popular DestinationPopular Destination

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Conclusion MobileDTNs

MobileDTNs

ApplicationsApplications MobilityMobility

Class of disruption-

tolerant

Core functional

requirements

Simulationsbased onmobility

Synthetic &synthesized

models

RoutingRouting

Classes ofrouting

protocols

Groupcommunication

model

Developed a Modular Platform

(Released on sourceforge)

DevelopedInterest-Aware,PEEP algorithms

Mobile music-sharing system1 – N-Grams to estimate future locations2 – Routing based on Markov Model3 – Best to route to popular locations

Markov-based Mobility-Model

and Routing Algorithm

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Back up slides

47

NewFile

(R0, H0)(R1, H1)

(Checksum, Hash)

(R2, H2)

(R5, H5)(R6, H6)

(R4, H4)

Signatures File(received from client)

(R3, H3)

Look up hash

(pointer i) Copy

Download

matching

non-matching

Difference(deltas file, to be sent back to the

client)

……

……

……

(pointer i+1) Copy(pointer i+2) Copy

Download(pointer i+4) Copy(pointer i+5) Copy

OldFile

(R0, H0)(R1, H1)

(Checksum, Hash)

(R2, H2)

(R5, H5)(R6, H6)

(R4, H4)

Signatures File(to be sent to server)

(R3, H3)

Insert hash…

…

……

Client ServerRsync Algorithm

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Current routing models Single-source single-destination (no knowledge of topology)

Flooding based protocols Epidemic

Probabilistic routing PROPHET [57], RPLM [79], MaxProp [21]

Context or behavior of mobile users HiBOp [18], Profile-cast [42], MobySpace [54]

Multicast Extends the classical model with group memberships to mobile DTNs

No infrastructure No knowledge of the topology (e.g., no multicast routers)

Epidemic based multicast (no knowledge)

49

Current routing models Single-source single-destination (no knowledge of topology)

Flooding based protocols Epidemic

Probabilistic routing PROPHET [57], RPLM [79], MaxProp [21]

Context or behavior of mobile users HiBOp [18], Profile-cast [42], MobySpace [54]

Multicast Extends the classical model with group memberships to mobile

DTNs No infrastructure (e.g., no multicast routers) No knowledge of the topology

Epidemic based multicast (no knowledge)

50

Probabilistic routing criteria

PROPHET Delivery predictability calculation.

Routing with Persistent Link Modeling (RPLM) Monitors link connectivity to calculate its cost. Dijkstra to find a minimum cost path.

MaxProp Assigning a cost value to each destination based on probability. Priority queue younger messages higher chances.

MobySpace MobyPoint each node’s coordinates or mobility pattern. Distance on each axes probability of contacts or presence in a location.

51

Characteristics of the current modelsModel objective

Delivery ratio Delay Message redundancy

Knowledgeof topology

Flooding1-to-1

1-to-many

High Low(the least)

High Buffer congestion

Zero

Knowledge based1-to-1

1-to-many

MF the highest (even higher than

ER)

Moderate Low Provided to the algorithm

Probabilistic1-to-1

Close to ER with tendency in mobility

Close to ER with tendency in

mobility

Moderate Memory(learning from the

past)

Multicast1-to-many

Flooding based is the highest

Flooding based is the lowest

Flooding based is the highest

Required in non-epidemic

52

Interest-aware simulation results The ONE simulator for mobile DTNs Movement generation based on reality-mining’s mobile

traces Compared to epidemic multicast with the same storage

constraints The only model with no knowledge about topology and group

memberships Measured # relevant and irrelevant documents received

by mobile users Increases # received relevant documents by 30% Decreases # received irrelevant documents by 35%

Interest-aware algorithm limits the resource usage in terms of the cache and contact duration

The ONE, reality-mining

53

Web recommender systems Systems for recommending items (e.g. books,

movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

Many on-line stores provide recommendations (e.g. Amazon, CDNow).

Personalization to the individual needs, interests, and preferences of each user.

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E.g. book recommenderRedMars

Juras-sicPark

1984

Ident-ity

Foundation

Differ-enceEngine

Machine Learning

UserProfile

Animalfarm

Neuro-mancer

history

55

Collaborative filtering

Maintains a database of many users’ ratings of a variety of items

For a given user, find other similar users whose ratings strongly correlate with the current user

Recommend items rated highly by these similar users, but not rated by the current user

Almost all existing commercial recommenders use this approach (e.g. Amazon)

56

Collaborative filtering

A 9B 3C: :Z 5

A B C 9: :Z 10

A 5B 3C: : Z 7

A B C 8: : Z

A 6B 4C: :Z

A 10B 4C 8. .Z 1

UserDatabase

ActiveUser

CorrelationMatch

A 9B 3C . .Z 5

A 9B 3C: :Z 5

A 10B 4C 8. .Z 1

ExtractRecommendations

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The ONE Simulator A modular simulation environment for mobile DTNs Routing package

Prophet Epidemic Spray and wait

Internal and external mobility generation RWP Map based Stationary

Internal and external message event generation Reports of connection and message passing

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Snapshot of map-based movement

The ONE Simulator

A modular simulation environment for mobile DTNs Implements routing packages for one-to-one model

Prophet Epidemic Spray and wait

Internal and external mobility generation RWP Map based Stationary

Internal and external message event generation Reports of contacts and message transmission

59

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Interest-aware protocol implementation Interest-aware routing as a new module for the

routing package General categories for documents Each node randomly assigned with some interest

in each category A sub-population is randomly selected to be in the

same community of interest Documents/messages are generated from nodes

outside this community Coverage, pollution and dropped messages

61

Choice of mobility model for interest-aware

Synthetic mobility traces RWP Map-based Community-based

Speed of nodes Residence time Directions More realistic simulation with real-world traces

Reality-mining traces

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Users behavior: Reality Mining Social behavior study;

Users encounters and visited locations

How predictable is people’s lives?

How does information flow?

100 subjects with Nokia symbian series 6600.

Logs AP, GSM base stations and

users encounters, call logs. Goal: learn users behaviors

and social network studies.

63

Reality-mining database

Tables in REALMINE

activityspan

callspan

cellname

cellspan

celltower

coverspan

device

devicespan

person

phonenumber

MySql database Device Devicespan Person person-person contacts device-device contacts

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Relations we usedperson

PK oid

name

password

email

devicespan

PK oid

FK1

FK2

starttime

endtime

person_oid

device_oid

device

PK oid

FK1

macaddr

name

person_oid

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Statistics and simulation set up

Reality-mining subjects: 97 Total number of encountered devices: 20795 44% of contacts with duration 0 15% of total contacts with devices outside the

reality-mining 66% of these contacts just happened once!

40% have been considered in the same community of interests

Fixed number of general categories

Optimization criteria for PEEP Maximize the number of received items of interest

Minimize the delay of data distribution

Not two independent values! The more the distribution the less the delay

has nodes interested = set of nodes interested in

i i

i Sjij

N

t

Min i

iC iN

iS

ii NS

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PEEP implementation in The ONE PEEP routing as a new module for the routing package General categories for documents Each node is assigned some interest in each category based

on Zipf distribution Distribution of the popular items follows Zipf law

No knowledge of the topology Documents/messages are generated uniformly from

different sources Measurements:

Number of received documents of interest over time Number of received documents of interest over contacts Speed of the distribution (slope of the graph)

67

Choice of mobility model for PEEP Synthetic mobility traces

RWP Map-based Community-based

Speed of nodes Residence time Directions The relative performance of the algorithm should be

independent from the choice of the mobility model Our choice: RWP A constant slope verifies this fact

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Evaluation of the results

If storage size is low buffer overflow happens too soon No chance for the items of interest to survive

The most important difference with our previous work Unlimited storage size Limited energy (transmit-budget) Not far from the reality

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Low storage size

EpidemicInterest-aware

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Medium ~ High storage sizes

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Levy flight Human walk follows a Levy flight

distribution A random walk for which step size follows a

power-law distribution: :step size

Rhee et al. GPS traces of 44 users; truncated power-law

Brockmann et al. Bank notes is fat tailed power-law

Gonzalez et al. Cell phone traces of 100,000 users; truncated

power-law

r

* Graph from: D. Brockmann and F. Theis, “Money Circulation, Trackable Items, and the Emergence of Universal Human Mobility Patterns“, IEEE Pervasive Computing, Volume 7, Piscataway, NJ, October 2008.

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Doctoral thesis defense Arezu Moghadam 13 May 2011

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Transcript of Doctoral thesis defense Arezu Moghadam 13 May 2011