IEEE iThings2014, Panel Talk (EU-Taiwan collaboration panel), Taipei, Taiwan, 2014.

1. CityPulse: Large-scale data analytics
for smart cities
1
Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom

2. Smart City Data
− Data is multi-modal and heterogeneous
− Noisy and incomplete
− Time and location dependent
− Dynamic and varies in quality
− Crowed sourced data can be unreliable
− Requires (near-) real-time analysis
− Privacy and security are important issues
− Data alone may not give a clear picture -we need
contextual information, background knowledge, multi-source
information and obviously better data analytics
solutions…
2

3. Smart City Data
3
?

4. What happens if we only focus on data
− Number of burgers consumed per day.
− Number of cats outside.
− Number of people checking their facebook
account.
− What insight would you draw?
4

5. What type of problems we expect to solve
in
“smart” cities

6. Back to the future
6

7. Future cities: a view from 1998
Source LAT Times, http://documents.latimes.com/la-2013/ 7

8. Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/ 8
Source: wikipedia

9. 9

10. The IoT and its applications
10
Diffusion of innovation
IoT
image source: Wikipedia
The Most Hyped Technology
image source: Forbes via Gartner

11. Moving fast forward
11
Source: AdamKR via Flicker, http://www.flickr.com/photos/adamkr/5045295251/in/photostream/

12. 12
We need an Integrated Approach

13. 13
CityPulse Consortium
Partners:
Industrial
SIE (Austria,
Romania),
ERIC
SME AI
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
Duration: 36 months

14. 14
Processing steps

15. CityPulse – what we are going to
deliver
...
Data Streams
a) Software tools/libraries
in an integrated framework
b) Back-end support servers
Smart City Framework
Smart City Scenarios
a) 101 scenarios
b) 10 will be chosen to be prototyped
a) Data portals/ real-time access
interfaces
b) Interoperable formats
c) Common interfaces (REST/annotated)
a) Proof-of-
Concepts and
demonstrators
and evaluations;
Applications/App
s/Demos
Link: http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements

16. Stream Processing
...
Data Streams
CityPulse

17. Some of the key issues
− Data collection, representation, interoperability
− Indexing, search and selection
− Storage and provision
− Stream analysis, fusion and integration of multi-source,
multi-modal and variable-quality data
− Aggregation, abstraction, pattern extraction and
time/location dependencies
− Adaptive learning models for dynamic data
− Reasoning methods for uncertain and incomplete data
− Privacy, trust, security
− Scalability and flexibility of the solutions
17

18. Some of our recent in this domain
18

19. Use cases

20. Scenario ranking

21. 101 Smart City Use-case Scenarios
http://www.ict-citypulse.eu/page/content/smart-city-use-cases-and-requirements

22. 101 Scenarios
− http://www.ict-citypulse.eu/page/content/smart-city-
use-cases-and-requirements

23. Data abstraction
23
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

24. Ontology learning from real world data
24

25. Adaptable and dynamic learning
methods
http://kat.ee.surrey.ac.uk/

26. Social media analysis (collaboration with Kno.e.sis,
Wright State University)
26
Tweets from a city
City Infrastructure
https://osf.io/b4q2t/
P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, under review, 2014.

27. Correlation analysis
27

28. 28

29. Data analytics framework
Ambient
Intelligence
Social
systems Interactions Interactions
29
Data Data
Data:
Domain
Knowledge
Domain
Knowledge
Social
systems
Open
Interfaces
Open
Interfaces
Ambient
Intelligence
Quality and
Trust
Quality and
Trust
Privacy and
Security
Privacy and
Security
Open Data Open Data

30. In Conclusion
− Smart cities are complex social systems and no technological and data-analytics-
driven solution alone can solve the problems.
− Combination of data from Physical, Cyber and Social sources can give more
complete, complementary data and contributes to better analysis and
insights.
− Intelligent processing methods should be adaptable and handle dynamic,
multi-modal, heterogeneous and noisy and incomplete data.
− Effective visualisation and interaction methods are also key to develop
successful solutions.
− There are several solution for different parts of a data analytics framework in
smart cities. An integrated approach is more effective in which IoT devices,
communication networks, data analytics and learning algorithms and
methods, services and interaction and visualistions and methods (and their
optimisation algorithms) can work and cooperate together.
30

31. Q&A
− Thank you.
− EU FP7 CityPulse Project:
http://www.ict-citypulse.eu/
@ictcitypulse
p.barnaghi@surrey.ac.uk