1. Network Intelligence Driven Behaviour Modelling for a Sustainable
Connected World
Fahim Kawsar
Internet of Things Research
Bell Labs
@raswak

2. POPULATION
DATA
MORE DEVELOPED
COUNTRIES
LESS DEVELOPED
COUNTRIES
2013: 1.2 Billion
2050: 1.3 Billion
2013: 5.9 Billion
2050: 8.4 Billion
WORLD
2013: 7.1 Billion
2050: 9.7 Billion
Source : Population Reference Bureau

4. Source : UN Department of Economics and Social Fare
72% The expected growth of World’s
Urban Population by 2050

5. Source : McKinsey
30%
More Water
100%
More Vehicles
150%
More Energy
Increased Demand on Infrastructure
Projected for 2020
$1T

6. But Wait - We Have Got IoT Right!
Home Automobile
Transport
Environment
SecuritySignage
Industry
Healthcare

7. Source : CISCO

8. “The pitch is that the IoT will make our world a greener place. Environmental sensors can
detect pollution, the voices say. Smart thermostats can help us save money on our electric
bills. A new breed of agriculture tech can save water by giving crops exactly the amount they
need and no more.” and so as many other problems of our society will be solved by IoT ..AHEM.
- Wired Magazine

9. Waste Pollution Traffic
Quantified Self Quantified Home Quantified City
A Lot of Connected Things + Big Data = A Lot of Savings -
??
Sense
Learn
Act
Share

10. Can we have a conversation about what the
real environmental impact of these devices
will be and how we can minimize it ?
Image Source : Wired Magazie

11. It takes only 8weeks for nest thermostat to save enough energy to become carbon neutral, based
on the amount of energy it requires to be manufactured and distributed.

12. But How about the rest of IoT…
12
How about the rest of the IoT…

13. How can we shrink the foot print of the IoT devices?
How can we increase the life span of IoT devices?
How can we leverage existing infrastructures for Sensing and Learning and sharing?

14. How can we leverage existing infrastructures for Sensing and Learning and sharing?

15. The Circular Economy
From a linear “take-make-dispose”-economy to a circular-pattern.
Collaborative Consumption (“Shareconomy”): from ownership (product) to access (service).
Connect with people online into the offline world.
$3.5B in revenues from transactions in the sharing economy (ZipCar, Airbnb, TaskRabbit)

16. Network Sensing for Opportunistic Observation
By observing an individual’s engagement with network annotated with temporal and spatial
information, we can learn and infer behaviour.
“Your Noise is my Signal”
Sense
Learn
Act
Share
How can we leverage existing infrastructures for Sensing and Learning?

17. Location + Time + Venue Type = Activity

18. Location + Time + Application Type = Activity

19. Location + Time + Application Type = Activity

20. “Your Noise is my Signal”
Network Sensing for Opportunistic Observation
By observing an individual’s engagement with network annotated with temporal and spatial
information, we can learn and infer behaviour.
Implications

21. 21

22. Quantifying Yourself

23. COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Activity Aware Search Experience

24. COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Better Experience with Pervasive Spaces

25. Contextual Notification
25

26. COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Predictive Appliance Management of Homes
26

27. Smart Pricing and Personalised Content Delivery

28. Collective Measure
Network Resource Planning
28

29. Business Intelligence
29

30. Network Sensing for Opportunistic Observation
By observing an individual’s engagement with network annotated with temporal and spatial
information, we can learn and infer behaviour.
“Your Noise is my Signal”
Sense
Learn
Act
Share

31. Story of Three Cities

32. The Story of Seoul
Understanding User Behaviour form Mobile Network Traces

33. Dataset
10000 Users
30 Days
77000000 records
Mobile Web Dataset
User Data Records (UDR) Include
Web URL
Up and Down Traffic
Start and End Time
eNodeB Id
User Demography
R + Java + Hadoop + Hive
Anonymized Operator

34. UDR to Activity Trace Transformation Pipeline
03/03 10:02:05
03/03 10:02:06
03/03 10:12:05
03/03 10:02:06
http:/www.bbc.co.uk
http:/www.img1. bbc.co.uk
http:/www.facebook.com
http:/www.img2.bbc.co.uk
....................................................................
UDR Trace Activity Burst Detection Landmark Clustering at Each Burst
Tagging Landmark with
Activity Type for Each Session
Session Start Time Session End Time Activity
03/03 10:02:05 03/03 10:06:05 News
03/03 10:12:05 03/03 10:17:05 Social Network
Activity Trace
Detect session
by duration or with
time weighted TF-IDF
for multiple small
bursts
Landmark
Detection
90%Coverage of the Transactions across all users.
34

35. Feature 1 : Activity Diversity
Activity Diversity represents two aspects
Richness: Number of different activities a user is engaged with
Evenness: Relative contribution of each activity to user’s overall web time
H =
X
piln(pi)
Proportion of Activity Sessions of
Activity Type i
A Higher H Value indicates a diverse user who engages with many activities and spends time across activities evenly.
A Lower H value indicates a more stable and periodic user who spend large amount of time on a small number of activities.
50 Random Users
64% of the users
have an index of 2 or less
As activity increases, diversity and
evenness also increase mostly but
not always.
35

36. Feature 2 : Activity Density
Activity Density captures the level of user engagements as density at different parts of the day.
It measures the activity session time at different parts of the day.
Total Session Duration during ith Hours, i = Night | Morning | Afternoon | Evening
Total Durations of all the Sessions
Night Hours : 00:00 - 05:59
Morning Hours : 06:00 - 11:59
Afternoon Hours : 12:00 - 17:59
Evening Hours : 18:00 - 23:59
A pattern of activity density (low or high) indicates the
hours that are the dominant period for web activities
for an individual .
Di =
Si
d
|Sd|
Using this feature for clustering will segment
subscribers based on their temporal activity
footprint.
We used Hierarchical Agglomerative Clustering
Algorithms using cosine similarity to segment the
users into four temporal groups.
36

37. Feature 3 : Activity Popularity
Activity Popularity represents the relative popularity of different activity to an individual user and captures two aspects
Session Frequency: Number of Sessions of a Specific Activity
Session Duration: Total Session Durations of a Specific Activity
A Higher P Value indicates a popular activity, and a lower P value indicates the reverse
Total Number of Sessions
Number of Sessions of a specific activity
Total Session Duration of a specific activity
Maximum Session Duration for a single
activity across all activities
P = wai
d + (1 w)ai
f
NumberofSessions
Duration of Sessions (in Min)
37

38. Some Observations
We have also observed a strong positive co-relation between diverse users and evening users.
Night
Users
Morning
Users
Afternoon
Users
Evening
Users
Online
Games
Online
Video
Online
Music
Finance News
Social
Network
Blogs
Online
Shopping
We have observed a strong positive co-relation between between different temporal users and a set of activities.
38

39. The algorithm predicts activity patterns of future hour slots of current day by matching patterns of similar
days in the past.
Activity Prediction Algorithm
A Visual Explanation of the Algorithm with Three Sample Activity Types
39

40. Prediction Performance
Over 75% of the users have 65% activity coverage of with an accuracy of over 60%.
Here we have only considered accuracy by only inferring the activities that will occur.
Highly active users are the most predictable ones, and the prediction accuracy can reach up to 85% if we
increase the slot period.
Prediction Performance across all Users
CumulativeDistributionFunction(CDF)
Precision and Recall
Precision
Recall
F-Score
CumulativeDistributionFunction(CDF)
40

41. The Story of Kortrijk
Understanding User Behaviour form Home Network Traces

42. Dataset : Project LeYLab
In-Home Internet Activity Traces
Living Lab for Fiber based Services in the City of Kortrijk, Belgium.
ALU 7750 Service Router with Report and Analysis Manager (RAM) was used in the backbone.
86 Households
75 Applications
60 Days
9288000 Data Points

43. Web Communication
Web Activity
Types
Online Gaming
Web Browsing
File Sharing
Online Shopping
Video Watching
Home Working
Semantically identical applications were grouped together into 8 distinct Activity Types and most
popular 6activities were selected for subsequent study.
This selection was based on a combination of accumulated network traffic, frequency, duration
and temporal regularity
Social Networking
Application to Activity Mapping

44. Accumulated activity footprint of a representative household, activity is spread through out
the day, with higher engagements during the later hours.
Family Activity Trajectory
Time of the Day
NoofDays
0
5
10
15
20
25
6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM 12 AM 2 AM 4 AM
Web Communication
Soical Networking
Online Gaming
Home Working
Online Shopping
Video Watching

45. Feature 1 : Activity Diversity
Activity Diversity represents two aspects
Richness: Number of Different Activities a User is engaged with
Evenness: Relative contribution of each activity to user’s overall web time
H =
X
piln(pi)
Proportion of Activity Sessions of
Activity Type i
A Higher H Value indicates a diverse user who engages with many activities and spends time across activities evenly.
A Lower H value indicates a more stable and periodic user who spend large amount of time on a small number of activities.
50 Random Users
64% of the users
have an index of 2 or less
As activity increases, diversity and
evenness also increase mostly but
not always.

46. Feature 2 : Activity Periodicity
Activity Periodicity represents one aspect
Temporal Regularity of the Engagement
An individual usually engages with online shopping during the evening hours of every other weekends.
D = Days of the WeeksH = Hours of the Day
P = Periods

47. Feature 3 : Activity Popularity
Activity Popularity represents two aspects
Session Frequency: Number of Sessions of a Specific Activity
Session Duration: Total Session Durations of a Specific Activity
A Higher P Value indicates a popular activity, and a lower P value indicates the reverse
Total Number of Sessions
Number of Sessions of a specific activity
Total Session Duration of a specific activity
Maximum Session Duration for a single
activity across all activities
P = wai
d + (1 w)ai
f

48. We have identified four segments (10% of the households was not included) with distinct
behavioral features; representing internet savvy families, families with little digital footprint,
socially interactive families and families with working adults.
Household Segmentation
Credit: Xueli An
10%
12%
14%
23%
41%
Heavy Weight
Households
Light Weight
Households
Socially Interactive
Households
Semi Heavy Weight
Households
Independent
Households
Collective Interaction of Different Segments with Different Activities

49. Prediction Performance
60% of households activities can be predicted accurately 70% of times.
CumulativeDistributionFunction(CDF)
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
F-Measure

50. The Story of London
Understanding Urban Dynamics with Travel Network Trace

51. COPYRIGHT © 2012 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Dataset
4313954 Cards
30 Days
66237633 Trips
577 Stations
London TFL Transit Data
Each trip record includes:
Unique Oyster Card ID
Oyster Card Type
Start and End Time
Check-in and Check-out Stations
R + Java
Hadoop + Hive

52. Spatial Travel Intensity

53. Transition Modeling Demographically

54. Significant Place Detection
We have observed that this is true as we can clearly identify the individuals who are regular
or visitor to a specific area.
Single Individual Single Station
Resident
Visitor

55. Dataset
3264820 Points of Interest
298 Amenity Types
London Open Street Map

56. Early hours Work hours Evening hours
3 Morning clusters and 9 Day clusters and 6 Evening Clusters
Segmenting London Based on Functional Signature

57. Central Londoners Show High Introversion

58. Central Londoners Show Limited Spatial Diversity but High Temporal
Periodicity

59. City Hotspots have Strong Temporal Periodicity and Limited Origin
Diversity

60. Internet of Things Research @ Bell Labs
Behaviour Modeling
Smart Object Modeling
Mobile Sensing
Participatory Sensing
Evolutionary Graph
Real World SearchIndoor Localisation
HCI Studies
Pervasive Display
Pervasive Privacy
EF5
EF5
EF5
Novel Services For Retail Community
Novel Services For Enterprise Community
Novel Services For Urban Community

61. Thank You
Fahim Kawsar
@raswak
eMail: fahim.kawsar@bell-labs.com