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China Mobile Leader’s Programme Mobile Technology Jon Crowcroft

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Title: China Mobile Leader’s Programme Mobile Technology Jon Crowcroft


1
China Mobile Leaders ProgrammeMobile
TechnologyJon Crowcroft
  • http//www.cl.cam.ac.uk/jac22
  • Jon.crowcroft_at_cl.cam.ac.uk
  • gmail, hotmail
  • 441223763633
  • 447733 231822
  • linkedin, facebook, myspace

2
4 Areas
  • Mobile Social Networks
  • Data Collection
  • Energy
  • Programming

3
1. Online Mobile Social Nets Real Life
4
We meet, we connect, we communicate
  • We meet in real life in the real world
  • We use text messages, phones, IM
  • We make friends on facebook, Second Life
  • How are these related?
  • How do they affect each other?
  • How do they change with new technology?

5
Thank you but you are in the opposite direction!
I have 100M bytes of data, who can carry for me?
Give it to me, I have 1G bytes phone flash.
I can also carry for you!
Dont give to me! I am running out of storage.
Reach an access point.
There is one in my pocket
Internet
Search La Bonheme.mp3 for me
Finally, it arrive
Search La Bonheme.mp3 for me
Search La Bonheme.mp3 for me
6
My facebook friendswheel
7
My email statistics!
8
Cliques and Communities
9
Dunbars Number Trust
  • Dunbars number-150 (for humans)
  • Size of simple communities of humans
  • Reflects ability to cope with group
  • Humans gossip rather than physical grooming
  • Language lets us abstract
  • We can reason up to 5 levels of intentionality
  • (Shakespear does 6 -)
  • T 1 / 3.xN
  • T is trust metric
  • 3.x is a number between 3 and 4
  • N is distance in social net

10
Conjecture on N?
  • N 0 Kin (sex)
  • N 1 friends (beer/drugs)
  • N 2 or more acquaintances (dancing/music/laugh
    ing at same jokes)
  • How does this help in facebook?

11
Conjecture on Online v. Real
  • Were looking at co-lo networks
  • c.f. haggle, cityware - bluetooth etc
  • AND online social networks
  • Friendship graph on orkut,li,facebook
  • AND communication networks
  • Email address book, sms, phonecalls
  • Can use to infer real relationship
  • I.e. type of edge in graph (and value of N)

12
Conjectures on Trust
  • Trust in terms of revelation/disclosure
  • Or carrying data (in ferry net)
  • Or simple automated/default grouping for ACLs
  • Need to do some experiments
  • Figure out how ties are broken
  • Forgetting
  • How new tools/technology affect
  • Size and dynamics of social net

13
EU Social Net Project Questions
  • What net/edge type is more likely to cause an
    edge in another net?
  • Does meeting someone dominate over online or vice
    versa -
  • i.e. how does new tech affect x (size of
    immediate gang) and
  • N (scope of gang/level of intentionality
    reasoning?)?
  • Can you use this to detect dodgy behaviour (spam,
    bullying, etc)?

14
Ongoing studies
  • Data?
  • We have large datasets for single
    edge-type/modality
  • (6M phone call timeloc, 1M social net)
  • But only very small datasets for 2 or 3
    modalities
  • 30 army base people -gt retirement
  • 100 school leavers -gt University
  • Very heavy-lifting
  • Not only lots of data processsing, but worse-
  • Interview eahc user for context
  • Privacy?
  • Correlating (datamining) the different nets is
    massive breach of trust
  • Usefulness?

15
Usefulness?
  • Improve privacy
  • As mentioned, could auto-default Fb settings and
    relate to phone/locn
  • Could also use as interest based filter
  • Fundamental understanding of social groups
  • How society/technology co-evolve
  • Social inclusion and accessibility (!)
  • Epidemiology ()
  • Buzztraq
  • Use currency of local interest to
  • Fetch content

16
Epidemiology
  • Two projects -
  • Emulation (ESRC)
  • Run s/w on smart phone that mimics a disease
  • Has a vector and SIR(!) parameter per person
  • Run on real socieity based on meeting
    duration/proximity/frequency
  • Flubook (Horizon)
  • Panic button (Not well/Feelin better)
  • Uploads list of contacts in last week via free
    SMS
  • Puts anonymized data on google maps
  • Alerts trusted friendship group on facebook

17
SIR
  • Susceptibility, Infectiousness, Recovery
  • Given contact distribution,
  • Can compute progress of epidemic
  • Whether collapse (S, I low, R high)
  • Or go pandemic (S, I high)
  • As with relationship between online and RL
    behaviour for socialising,
  • Flubook might alter contact rate
  • .systematically for subset of population
  • (social or geographic) with high S/I
  • Help prevent/collapse epidemic

18
Thank you
  • Questions?

19
And another thing
  • Virtualising online social self
  • Floating it in the cloud
  • Crypt content, but allow cloud/fb to match
    interests (for advertising)
  • Migrate it to track user (and handset)
  • Performance gain
  • handset can be meagre cpu/memory
  • Latency reduced
  • Synchronisation/persistence assured
  • Dont care if handset lost/stolen -)

20
Snakes (and Ladders) on a Plane
  • Human
  • Node
  • World

21
Threads of your life
  • Human level is activities relationships
  • Nodal level is processing and storage
  • World level is location and context

22
Idea is
  • To allow mobile (compact/portable) representation
    of your activities and relationships (0wned by
    ou)
  • Roam across arbitrary nodes in environment
    (embedded or handset owned by anyone)
  • While recording where you are and context (
    other people)

23
2. Data Collection for Modelling Contact Networks
  • Eiko Yoneki and Jon Crowcroft
  • eiko.yoneki_at_cl.cam.ac.uk
  • Systems Research Group
  • University of Cambridge Computer Laboratory

24
Outline
  • Purposes of Data Collection
  • ? Modelling Human Contact Networks
  • Proximity Data Collection Methodology
  • Issues for Data Collection
  • Examples of Data Analysis
  • Extending to Collect/Correlate Online Data
  • Conclusion

25
Purpose of Data Collection
  • Building communication protocol based on
    proximity
  • EU FP6 Haggle Project
  • Inferring social interaction, opinion dynamics ?
    Apply results to networking and computer systems
  • EU FP7 Socialnets, EU FP7 Recognition
  • Network modelling for epidemiology
  • EPSRC Data Driven Network Modelling for
    Epidemiology
  • Understanding behaviour to infectious disease
    outbreak - social and economic influences
  • ESRC FluPhone Project

25
26
Haggle Pocket Switched Networks
  • Networked distributed database over
    opportunistically connected devices (e.g. Mobile
    phones)

Legacy network (e.g. the Internet)
Ex. Haggle Twitter
EU FP6 Haggle http//www.haggleproject.org
26
27
FluPhone Project
  • Understanding behavioural responses to infectious
    disease outbreaks
  • Extending data collection to general public
  • https//www.fluphone.org

27
28
Purpose of Data Collection
  • Robust data collection from real world
  • Post-facto analysis and modelling yield insight
    into human interactions
  • Data is useful from building communication
    protocol to understanding disease spread

Modelling Contact Networks Empirical Approach
28
29
Proximity Data Collection
  • Sensor board (iMote), mobile phone
  • Proximity detection by Bluetooth, and/or GPS
  • Environmental information (e.g. in train, on road)

AroundYou
iMote
FluPhone
29
30
Proximity Detection by Bluetooth
  • Only 15 of devices Bluetooth on
  • Scanning Interval
  • 2 mins iMote (one week battery life)
  • 5 mins phone (one day battery life)
  • or continuous scanning by station nodes
  • Bluetooth inquiry (e.g. 5.12 seconds) gives gt90
    chance of finding device
  • Complex discovery protocol
  • Two modes discovery and being discovered
  • 510m discover range

Can it produce reliable data (negligible noise)?
30
31
Sensor Board or Phone or ...
  • iMote needs disposable battery
  • Expensive
  • Third world experiment
  • Mobile phone
  • Rechargeable
  • Additional functions (messaging, tracing)
  • Smart phone location assist applications
  • Provide device or software
  • Combine with online information (e.g. Twitter)

31
32
Phone Price vs Functionality
  • lt20 GBP range
  • Single task (no phone call when application is
    running)
  • gt100 GBP
  • GPS capability
  • Multiple tasks run application as a background
    job
  • Challenge to provide software for every operation
    system of mobile phone

32
33
Location Data
  • Location data necessary?
  • Ethic approval gets tougher
  • Use of WiFi Access Points or Cell Towers
  • Use of GPS but not inside of buildings
  • Infer location using various information
  • Online Data (Social Network Services, Google)
  • Us of limited location information Post
    localisation

Scanner Location in Bath
33
34
Target Population
  • Provide devices to limited population or target
    general public
  • For epidemiology study 100 coverage may be
    required
  • Fluphone project participants will be general
    public
  • Or school as mixing centres

?
?
?
?
?
?
?
34
35
Experiment Parameters vs Data Quality
  • Battery life vs Granularity of detection interval
  • Duration of experiments
  • Day, week, month, or year?
  • Data rate
  • Data Storage
  • Contact /GPS data lt50K per device per day (in
    compressed format)
  • Server data storage for receiving data from
    devices
  • Extend storage by larger memory card
  • Collected data using different parameters or
    methods ? aggregated?

35
36
Data Retrieval Methods
  • Retrieving collected data
  • Tracking station
  • Online (3G, SMS)
  • Uploading via Web
  • via memory card
  • Incentive for participating experiments
  • Collection cycle real-time, day, or week?

36
37
Data Transformation for Analysis
  • Transform to discrete version of contact data
  • Deal with noise and missing data
  • Ex. transitivity closure
  • Data analysis requires high performance computer
    and storage
  • Low volume - raw data in compact format
  • Transformation of raw data for analysis increases
    data volume

37
38
Security and Privacy
  • Current method Basic anonymisation of identities
    (MAC address)
  • FluPhone Project use of HTTPS for data
    transmission via 3G
  • Anonymising identities may not be enough?
  • Simple anonymisation does not prevent to be found
    the social graph
  • Ethic approval tough!
  • 40 pages of study protocol document for FluPhone
    project took several months to get approval

38
39
Consent
39
40
Human Connectivity Traces
  • Capture Human Interactions
  • ..thus far not large scale
  • Crawdad DB http//crawdad.cs.dartmouth.edu/
  • Contact 025d04b2b3f 4650000025d0
    5416492246711621549 5416492246711644527
  • Location 0025d0e113da lon -3.384610278596745E12
    5 lat 1.3168305280597862E182 506661995017043176
    3

HAGGLE
40
41
Regularity of Network Activity
  • Size of largest connected nodes shows network
    dynamics

Tuesday
5 Days
41
42
Inter Contact Time of Pair Nodes
  • Power law distribution ( exponential decay)

Time
42
43
Classification of Node Pairs
  • I Community
  • High Frequency - Long Duration
  • II Familiar Stranger
  • High Frequency - Short Duration
  • III Stranger
  • Low Frequency Short Duration
  • IV Friend
  • Low Frequency - High Duration

I
II
Number of Contact
III
IV
Contact Duration
43
44
Betweenness Centrality
  • Frequency of a node that falls on the shortest
    path between two other nodes

MIT
Cambridge
44
45
Uncovering Community
  • Contact trace in form of weighted (multi) graphs
  • Contact Frequency and Duration
  • Use community detection algorithms from complex
    network studies
  • K-clique, Weighted network analysis, Betweenness,
    Modularity, Fiedler Clustering etc.

Fiedler Clustering
45
46
  • Visualisation of Community Dynamics

46
47
Extending Data Collection to OSN
  • Online Social Networks (e.g. Facebook, Twitter)
  • Potential to obtain data of dynamic behaviour
  • High volume of data
  • Does Facebook matter?
  • Over 190 M users
  • Growth rates for 2008 around the world
  • Italy 2900, Argentina 2000, Indonesia 600

47
48
Power Law Degree Distribution
  • Crawled original Stanford (15043 Nodes), Harvard
    (18273 nodes) networks
  • From era when UIDs assign sequentially
  • Obtains friends of each user, and their
    affiliations
  • 2.1 million links, Maximum degree 911

48
49
Information Cascade thru Social Networks
  • Use Google geo-coding API - predict the
    geographical access patterns
  • T0........................................
    ........Tk

Texas
Illinois
Florida
49
50
Conclusions
  • Real World Data is Powerful!
  • Analyse Network Structure of Social Systems to
    Model Dynamics ? Emerging Research Area
  • Weighted networks
  • Modularity
  • Centrality (e.g. Degree)
  • Community evolution and dynamics
  • Network measurement metrics
  • Patterns of interactions
  • Plan purpose of data collection first that leads
    to decide data collection method
  • Solve ethic issues/approval in advance
  • Combine data collection using device and
    available online data for efficiency and accuracy

50
51
Conclusions
  • Real World Data is Powerful!
  • Analyse Network Structure of Social Systems to
    Model Dynamics ? Emerging Research Area
  • Weighted networks
  • Modularity
  • Centrality (e.g. Degree)
  • Community evolution and dynamics
  • Network measurement metrics
  • Patterns of interactions
  • Plan purpose of data collection first that leads
    to decide data collection method
  • Solve ethic issues/approval in advance
  • Combine data collection using device and
    available online data for efficiency and accuracy

Thank You!
51
52
3. Challenging Opportunities
  • Jon Crowcroft,
  • http//www.cl.cam.ac.uk/jac22

53
History (personal-)
  • Manet
  • Mobileman
  • Tschudin et al
  • Incredibles
  • Dtn
  • Interplanetary/Oceanographic
  • Pocket Switched Mobile Social
  • Oppnet
  • Drive-Thru
  • Disaster

54
Choosing Adversity
  • Perverse, but valid research motive
  • Make the network really really bad
  • (like it was in 1970s)
  • And maybe neat new ideas will emerge
  • Which will work really, really well on a
    rock-solid network

55
Compete with Infrastructure
  • They have the guns, we have the numbers
  • But maybe opportunities give us information the
    infrastructure guys cant or wont get

56
Incentives
  • Hard to compute
  • Mostly assume rational selfish players
  • Recent market failures prove this is nonsense
  • What to do instead?
  • Use a priori social knowledge
  • Travel plans, SIM, Fb/Buzz data

57
Privacy and Risk Aversion
  • May be over sold
  • Known younger people are more cavalier with
    their online presence than older (pre web)
    generation
  • But needs respect
  • at least informed choice (opt out) by user
  • Prob. With idloc is it is 2/3 of what you need
    to find out everything
  • (2 digits of postcode, age gender)
  • There may be some trigger event which will change
    public view

58
Back to drawing board 0
  • Information theory and opportunities
  • What can we infer
  • popularity in meeting
  • Popularity in communicating
  • Hub/centrality
  • Clique/giant component
  • Predictive patterns of behaviour
  • Latest barabasi science paper on locn
  • Other?

59
Back to drawing board 1
  • Non rational players
  • Tools to measure adapt to
  • Herding
  • Cascading
  • Opinion dynamics

60
Back to drawing board 2
  • One small step at a time
  • Pair of nodes -
  • why share anything?
  • Whats useful
  • What does it cost
  • Micro-research agenda

61
Share between just 1 pair of phones
  • Now a phone is much more than a computer
  • GPS, Camera, Mike,
  • Compass, Accelerometer
  • several networks
  • Several (heterogeneous) cores in processor
  • We could share these
  • e.g. lots of people taking panoramic tiled
    photos,
  • or 1 GPS providing lots of people with location

62
Lets look at actual resource costs
  • Phone OS now about same as Desktop
  • Android Linux
  • Iphone OSX
  • Windows Mobile 6 (actually Windows 7!)
  • Etc etc
  • Software uses resources too
  • E.g. Java garbage collector surprise
  • Power/network aware applications

63
Narseos results
  • Weve started looking at resource use in battery
    terms
  • Calibrate OS tools for battery charge reporting
  • By opening up phone and putting probe on
    battery)
  • Then run experiment with lots of users

64
Principal components on bs phone
65
Principal components on Ts phone
66
Ns phone charging correlogram
67
Ns cell location correlogram
68
Ns screen on correlogram
69
Js interaction v. location
70
Js net usage by location
71
PCA Analysis
72
Average principal components
73
Fooling the user
  • Buzz/Mobile Social
  • Driving License
  • Smart Badges)

74
Back to Drawing Board 3
  • What business model fools user best?
  • What are the ethics?
  • Buzz was first big bang social mix
  • Take 1 network (gmail contacts, sorted by
    frequency of interaction)
  • And bootstrap another with it
  • How big a cognitive dissonance would this be to
    do on an opportunistic net?
  • Without informed consent, would cause major major
    headaches
  • Possibly illegal viz healthcare workers

75
Acknowledgements
  • Thanks to MSR for a bunch of WiMo phones
  • Thanks to Google for a bunch of Android phones
  • Thanks to volunteers in Cambridge for abandoning
    almost all privacy -)

76
Questions
  • Do we need both the guns and the numbers?
  • The truth is out there

77
D3N 4 Programming Distributed Computation in
Pocket Switched Networks
  • Eiko Yoneki, Ioannis Baltopoulos and Jon
    Crowcroft
  • University of Cambridge Computer Laboratory
  • Systems Research Group

Data Driven Declarative Networking
78
Rise of Sparse Disconnected Networks
  • Haggle EU FP6 New communication paradigm using
    dynamic interconnectedness http//www.haggle
    project.org
  • Disconnected
  • By necessity or design
  • Mobile
  • With enough mobility for some connectivity over
    time
  • Path existing over time
  • Data has to be delay tolerant
  • Opportunistic Forwarding instead Routing
  • 116

78
79
Pocket Switched Networks
  • Topology changes every time unit
  • Node 35 is a hub

Human-to-Human Use of dynamic human
connectivity http//www.cl.cam.ac.uk/ey204/Haggl
e/Vis/
80
Haggle Node Architecture
  • Each node maintains a data store its current
    view of global namespace
  • Persistence of search delay tolerance and
    opportunism
  • Semantics of publish/subscribe and an
    event-driven asynchronous operation
  • Multi-platform
  • (written in C and C)
  • Windows mobile
  • Mac OS X, iPhone
  • Linux
  • Android

Unified Metadata Namespace
data
node
Append
Search
80
81
D3N Data-Driven Declarative Networking
  • How to program distributed computation?
  • Use Declarative Networking ?

82
Declarative Networking
  • Declarative is new idea in networking
  • e.g. Search what to look for rather than how
    to look for
  • Abstract complexity in networking/data processing
  • P2 Building overlay using Overlog
  • Network properties specified declaratively
  • LINQ extend .NET with language integrated
    operations for query/store/transform data
  • DryadLINQ extends LINQ similar to Googles
    Map-Reduce
  • Automatic parallelization from sequential
    declarative code
  • Opis Functional-reactive approach in OCaml

83
D3N Data-Driven Declarative Networking
  • How to program distributed computation?
  • Use Declarative Networking
  • Use of Functional Programming
  • Simple/clean semantics, expressive, inherent
    parallelism
  • Queries/Filer etc. can be expressed as
    higher-order functions that are applied in a
    distributed setting
  • Runtime system provides the necessary native
    library functions that are specific to each
    device
  • Prototype F .NET for mobile devices

84
D3N and Functional Programming I
  • Functions are first-class values
  • They can be both input and output of other
    functions
  • They can be shared between different nodes (code
    mobility)
  • Not only data but also functions flow
  • Language syntax does not have state
  • Variables are only ever assigned once hence
    reasoning about programs becomes easier
  • (of course message passing and threads ? encode
    states)
  • Strongly typed
  • Static assurance that the program does not go
    wrong at runtime unlike script languages
  • Type inference
  • Types are not declared explicitly, hence programs
    are less verbose

85
D3N and Functional Programming II
  • Integrated features from query language
  • Assurance as in logical programming
  • Appropriate level of abstraction
  • Imperative languages closely specify the
    implementation details (how) declarative
    languages abstract too much (what)
  • Imperative predictable result about performance
  • Declarative language abstract away many
    implementation issues

86
Overview of D3N Architecture
  • Each node is responsible for storing, indexing,
    searching, and delivering data
  • Primitive functions associated with core D3N
    calculus syntax are part of the runtime system
  • Prototype on MS Mobile .NET

86
87
D3N Syntax and Semantics I
  • Very few primitives
  • Integer, strings, lists, floating point numbers
    and other primitives are recovered through
    constructor application
  • Standard FP features
  • Declaring and naming functions through
    let-bindings
  • Calling primitive and user-defined functions
    (function application)
  • Pattern matching (similar to switch statement)
  • Standard features as ordinary programming
    languages (e.g. ML or Haskell)

87
88
D3N Syntax and Semantics II
  • Advanced features
  • Concurrency (fork)
  • Communication (send/receive primitives)
  • Query expressions (local and distributed select)

88
89
D3N Language (Core Calculus Syntax)
89
90
Runtime System
  • Language relies on a small runtime system
  • Operations implemented in the runtime system
    written in F
  • Each node is responsible on data
  • Storing
  • Indexing
  • Searching
  • Delivering
  • Data has Time-To-Live (TTL)
  • Each node propagates data to the other nodes.
  • A search query w/TTL travels within the network
    until it expires
  • When the node has the matching data, it forwards
    the data
  • Each node gossips its own metadata when it meets
    other nodes

90
91
Kernel Event Handler
  • Kernel maintains
  • An event queue (queue)
  • A list of functions for each event (fenc, fdep)
  • Kernel processes
  • It removes an event from the front of the queue
    (e)
  • Pattern matches against the event type
  • Calls all the registered functions for the
    particular event

91
92
Example Query to Networks
  • Queries are part of source level syntax
  • Distributed execution (single node programmer
    model)
  • Familiar syntax

D3N
select name from poll() where institute
Computer Laboratory
poll() gt filter (fun r -gt r.institute
Computer Laboratory) gt map (fun r -gt r.name)
F
E
C
A
B
Message
(code, nodeid, TTL, data)
D
93
Example Vote among Nodes
  • Voting application implements a distributed
    voting protocol of choosing location for dinner
  • Rules
  • Each node votes once
  • A single node initiates the application
  • Ballots should not be counted twice
  • No infrastructure-base communication is available
    or it is too expensive
  • Top-level expression
  • Node A sends the code to all nodes
  • Nodes map in parallel (pmap) the function
    voteOfNode to their local data, and send back the
    result to A
  • Node A aggregates (reduce) the results from all
    nodes and produces a final tally

93
94
Sequential Map function (smap)
  • Inner working
  • It sends the code to execute on the remote node
  • It blocks waiting for a response waiting from the
    node
  • Continues mapping the function to the rest of the
    nodes in a sequential fashion
  • An unavailable node blocks the entire computation

94
95
Parallel Map Function (pmap)
  • Inner working
  • Similar to the sequential case
  • The send/receive for each node happen in a
    separate thread
  • An unavailable node does not block the entire
    computation

A
pmap
C
E
F
G
B
D
95
96
Reduce Function
  • Inner working
  • The reduce function aggregates the results from a
    map
  • The reduce gets executed on the initiator node
  • All results must have been received before the
    reduce can proceed

96
97
Voting Application Code
97
98
Cascaded Map Function
  • Social Graph can be exploited for map function
  • Logical topology extracted from social networks
  • Construct a minimum spanning tree with node A
  • Use tree as navigation of task

D
G
B
E
F
A
C
(a) Social Graph
D
D
B
B
E
E
A
F
C
(b) Nodes for Map at A
(c) Nodes for Map at B
98
99
Outlook and Future Work
  • Current reference implementation
  • F targeting .NET platform taking advantage of a
    vast collection of .NET libraries for
    implementing D3N primitives
  • Future work
  • Security issues are currently out of the scope of
    this paper. Executable code migrating from node
    to node
  • Validate and verify the correctness of the design
    by implementing a compiler targeting various
    mobile devices
  • Disclose code in public domain
  • http//www.cl.cam.ac.uk/ey204
  • Email eiko.yoneki_at_cl.cam.ac.uk
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