Workshop on Smart City: Applications and Services, Budva, Montenegro, October 2015.

1. Smart Cities and Data Analytics:
Challenges and Opportunities
1
Payam Barnaghi
Institute for Communication Systems (ICS)/
5G Innovation Centre
University of Surrey
Guildford, United Kingdom
Workshop on Smart City: Applications and Services
Budva, Montenegro
October 2015

2. 2
IBM Mainframe 360, source Wikipedia

3. Apollo 11 Command Module (1965) had
64 kilobytes of memory
operated at 0.043MHz.
An iPhone 5s has a CPU running at speeds
of up to 1.3GHz
and has 512MB to 1GB of memory
Cray-1 (1975) produced 80 million Floating
point operations per second (FLOPS)
10 years later, Cray-2 produced 1.9G FLOPS
An iPhone 5s produces 76.8 GFLOPS – nearly
a thousand times more
Cray-2 used 200-kilowatt power
Source: Nick T., PhoneArena.com, 2014

4. Computing Power
4
−Smaller size
−More Powerful
−More memory and more storage
−"Moore's law" over the history of computing, the
number of transistors in a dense integrated circuit
has doubled approximately every two years.

5. Cyber-Physical-Social Data
5P. Barnaghi et al., "Digital Technology Adoption in the Smart Built Environment", IET Sector Technical Briefing, The Institution of Engineering and Technology
(IET), I. Borthwick (editor), March 2015.

6. Internet of Things: The story so far
RFID based
solutions
Wireless Sensor and
Actuator networks
, solutions for
communication
technologies, energy
efficiency, routing, …
Smart Devices/
Web-enabled Apps/Services,
initial products,
vertical applications, early
concepts and demos, …
Motion sensor
Motion sensor
ECG sensor
Physical-Cyber-Social
Systems, Linked-data,
semantics,
More products, more
heterogeneity,
solutions for control and
monitoring, …
Future: Cloud, Big (IoT) Data
Analytics, Interoperability, Enhanced
Cellular/Wireless Com. for IoT,
Real-world operational use-cases
and Industry and B2B
services/applications,
more Standards…
P. Barnaghi, A. Sheth, "Internet of Things: the story so far", IEEE IoT Newsletter, September 2014.
6

7. 7
“Each single data item is important.”
“Relying merely on data from sources that are
unevenly distributed, without considering
background information or social context, can
lead to imbalanced interpretations and
decisions.”
?

8. Data- Challenges
− 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 can be biased- we need to know our data!
8

9. Data Lifecycle
9
Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of
data driven systems for building, community and city-scale applications,
http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

10. 10
“The ultimate goal is transforming the raw data
to insights and actionable knowledge and/or
creating effective representation forms for
machines and also human users and creating
automation.”
This usually requires data from multiple sources,
(near-) real time analytics and visualisation
and/or semantic representations.

11. 11
“Data will come from various source and from
different platforms and various systems.”
This requires an ecosystem of IoT systems with
several backend support components (e.g.
pub/sub, storage, discovery, and access services).
Semantic interoperability is also a key
requirement.

12. Device/Data interoperability
12
The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.

13. Search on the Internet/Web in the early days
1313

14. Accessing IoT data
14
“ The internet/web norm (for now) is often to use
an interface to search for the data; the search
engines are usually information locators – return
the link to the information; IoT data access is
more opportunistic and context aware”.
The IoT requires context-aware and opportunistic
push mechanism, dynamic device/resource
associations and (software-defined) data routing
networks.

15. IoT environments are usually dynamic and (near-) real-
time
15
Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com

16. What type of problems we expect to solve
using the IoT and data analytics solutions?

17. 17Source LAT Times, http://documents.latimes.com/la-2013/
A smart City example
Future cities: A view from 1998

18. 18
Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
Back to the Future: 2013

19. Common problems
19
Source: http://www.me.undp.org &
Guildford, Surrey

20. 20

21. Applications and potentials
− Analysis of thousands of traffic, pollution, weather, congestion,
public transport, waste and event sensory data to provide
better transport and city management.
− Converting smart meter readings to information that can help
prediction and balance of power consumption in a city.
− Monitoring elderly homes, personal and public healthcare
applications.
− Event and incident analysis and prediction using (near) real-
time data collected by citizen and device sensors.
− Turning social media data (e.g.Tweets) related to city issues
into event and sentiment analysis.
− Any many more…
21

22. EU FP7 CityPulse Project
22

23. 23
CityPulse Consortium
Industrial
SIE (Austria,
Romania),
ERIC
SME AI,
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
Partners:
Duration: 36 months (2014-2017)

24. 24

25. Designing for real world problems

26. 101 Smart City scenarios
26http://www.ict-citypulse.eu/scenarios/
Dr Mirko Presser
Alexandra Institute
Denmark

27. 27
Data Visualisation

28. 28
Event Visualisation

29. CityPulse demo
29

30. Data abstraction
30
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

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

32. Correlation analysis
32

33. Analysing social streams
33
With

34. City event extraction from social streams
34
Tweets from a city
POS
Tagging
Hybrid NER+
Event term
extraction
GeohashingGeohashing
Temporal
Estimation
Temporal
Estimation
Impact
Assessment
Impact
Assessment
Event
Aggregation
Event
AggregationOSM LocationsOSM Locations SCRIBE ontologySCRIBE ontology
511.org hierarchy511.org hierarchy
City Event ExtractionCity Event Annotation
P. Anantharam, P. Barnaghi, K. Thirunarayan, A.P. Sheth, "Extracting City Traffic Events from Social Streams", ACM Trans. on Intelligent
Systems and Technology, 2015.
Collaboration with Kno.e.sis, Wright State University

35. Geohashing
35
0.6 miles
Max-lat
Min-lat
Min-long
Max-long
0.38 miles
37.7545166015625, -122.40966796875
37.7490234375, -122.40966796875
37.7545166015625, -122.420654296875
37.7490234375, -122.420654296875
4
37.74933, -122.4106711
Hierarchical spatial structure of geohash for
representing locations with variable precision.
Here the location string is 5H34
0 1 2 3 4 5 6
7 8 9 B C D E
F G H I J K L
0 1
7
2 3 4
5 6 8 9
0 1 2 3 4
5 6 7
0 1 2
3 4 5
6 7 8

36. Social media analysis
36
City Infrastructure
Tweets from a city
P. Anantharam, P. Barnaghi, K. Thirunarayan, A. Sheth, "Extracting city events from social streams,“, ACM Transactions on TICS, 2014.

37. Social media analysis (deep learning –
under construction)
37
http://iot.ee.surrey.ac.uk/citypulse-social/

38. Accumulated and connected knowledge?
38
Image courtesy: IEEE Spectrum

39. Reference Datasets
39
http://iot.ee.surrey.ac.uk:8080/datasets.html

40. Importance of Complementary Data
40

41. Users in control or losing control?
41
Image source: Julian Walker, Flicker

42. Data Analytics solutions for IoT data
− Great opportunities and many applications;
− Enhanced and (near-) real-time insights;
− Supporting more automated decision making and in-depth
analysis of events and occurrences by combining various
sources of data;
− Providing more and better information to citizens;
− …
42

43. However…
− We need to know our data and its context (density, quality,
reliability, …)
− Open Data (there needs to be more real-time data)
− Complementary data
− Citizens in control
− Transparency and data management issues (privacy, security,
trust, …)
− Reliability and dependability of the systems
43

44. In conclusion
− IoT data analytics is different from common big data analytics.
− Data collection in the IoT comes at the cost of bandwidth, network,
energy and other resources.
− Data collection, delivery and processing is also depended on multiple
layers of the network.
− We need more resource-aware data analytics methods and cross-layer
optimisations.
− The solutions should work across different systems and multiple platforms
(Ecosystem of systems).
− Data sources are more than physical (sensory) observation.
− The IoT requires integration and processing of physical-cyber-social data.
− The extracted insights and information should be converted to a feedback
and/or actionable information.
44

45. IET sector briefing report
45
Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

46. CityPulse stakeholder report
46
http://www.ict-citypulse.eu/page/sites/default/files/citypulse_annual_report.pdf

47. Other challenges and topics that I didn't talk about
Security
Privacy
Trust, resilience and
reliability
Noise and
incomplete data
Cloud and
distributed computing
Networks, test-beds and
mobility
Mobile computing
Applications and use-case
scenarios
47

48. Q&A
− Thank you.
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/
@pbarnaghi
p.barnaghi@surrey.ac.uk