Smart cities use digital technologies and information communication technologies to enhance quality and performance of urban services. This makes cities "smart" by providing smarter citizens, governance, environment, equality, context-aware and cost effective services. Technology like sensors, real-time data collection and analytics, and integrated services across a city help power smart cities. However, challenges remain around data quality, privacy, bias, and over-complexity that must be addressed for smart city technologies and data analytics to achieve their full potential.

1. What makes smart cities “Smart”?
1
Payam Barnaghi
Institute for Communication Systems (ICS)/
5G Innovation Centre
University of Surrey
Guildford, United Kingdom
November 5, Galway, Ireland

2. Desire for innovation
2
Driverless Car of the Future (1957)
Image: Courtesy of http://paleofuture.com

3. “A hundred years hence people will be so
avid of every moment of life, life will be so
full of busy delight, that time-saving
inventions will be at a huge premium…”
“…It is not because we shall be hurried in
nerve-shattering anxiety, but because we
shall value at its true worth the refining and
restful influence of leisure, that we shall be
impatient of the minor tasks of every day….”
The March 26, 1906, New Zealand Star :
Source: http://paleofuture.com

4. 4P. 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.

5. 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

6. Computing Power
6
−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.

7. 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.
7

8. Smart City
“A smart city uses digital technologies or information and
communication technologies (ICT) to enhance quality and
performance of urban services, to reduce costs and resource
consumption, and to engage more effectively and actively with
its citizens.” [Wikipedia]
8
Is this a good definition?

9. Cities of the future
9
http://www.globalnerdy.com/2007/08/28/home-electronics-of-the-future-as-predicted-28-years-ago/

10. 10
Source: BBC News

11. Source: The dailymail, http://helenography.net/, http://edwud.com/

12. What are smart cities?
12
“An ecosystem of systems enabled by the
Internet of Things and information
communication technologies.”
“People, resources, and information coming
together, operating in an ad-hoc and/or
coordinated way to improve city operations
and everyday activities.”

13. Source: Frost and Sullivan via http://raconteur.net/

14. What does makes smart cities “smart”?

15. Smart Citizens (more informed and more in control)
Smart Governance (better services and informed decisions)
Smart Environment
Providing more equality and wider reach
Context-aware and situation-aware services
Cost efficacy and supporting innovation
What does makes smart cities “smart”?

16. How do cities get smarter?

17. How do cities get smarter?
17
Continuous (near-) real-time sensing/monitoring
and data collection
Linked/integrated data
and linked/integrated services
Real-time intelligence and actionable-information
for different situations/services
Smart interaction and actuation
Creating awareness and effective participation

18. How can technology help to make
cities smarter?

19. The role of data
19
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

20. 20
“Each single data item can be 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.”
?

21. Smart city data
21

22. 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!
22

23. Smart data collection
− Smart data collection
− Intelligent data pProcessing
(selective attention and
information-extraction)
− Region Beta Paradox
23
(image source: KRISTEN NICOLE, siliconangle.com)

24. 24
“The ultimate goal is transforming the raw data
to insights and actionable information and/or
creating effective representation forms for
machines and also human users, and providing
automated services.”
This usually requires data from multiple sources,
(near-) real time analytics and visualisation
and/or semantic representations.

25. 25
“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.

26. Device/Data interoperability
26
The slide adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.

27. IoT environments are usually dynamic and (near-) real-
time
27
Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com

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

29. 29Source LAT Times, http://documents.latimes.com/la-2013/
A smart City example
Future cities: A view from 1998

30. 30
Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia
Back to the Future: 2013

31. Common problems
31
Guildford, Surrey

32. 32

33. 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…
33

34. EU FP7 CityPulse Project
34

35. Big (IoT) Data Analytics
.
.
.
Real World Data
Smart City Framework
Smart City Scenarios

36. Designing for real world problems

37. 101 Smart City scenarios
37http://www.ict-citypulse.eu/scenarios/
Dr Mirko Presser
Alexandra Institute
Denmark

38. 38
Data Visualisation

39. 39
Event Visualisation

40. CityPulse demo
40

41. Creating Patterns-
Adaptive sensor SAX
41
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data”, IEEE Sensors Journal, 2013.

42. Data abstraction
42
F. Ganz, P. Barnaghi, F. Carrez, "Information Abstraction for Heterogeneous Real World Internet Data", IEEE Sensors Journal, 2013.

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

44. 44
https://github.com/UniSurreyIoT/KAT
Website: http://kat.ee.surrey.ac.uk

45. Real world data
45

46. Analysing social streams
46
With

47. City event extraction from social streams
47
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

48. Geohashing
48
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

49. Social media analysis
49
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.

50. Social media analysis (deep learning –
under construction)
50
http://iot.ee.surrey.ac.uk/citypulse-social/

51. Accumulated and connected knowledge?
51
Image courtesy: IEEE Spectrum

52. Reference Datasets
52
http://iot.ee.surrey.ac.uk:8080/datasets.html

53. Importance of Complementary Data
53

54. Users in control or losing control?
54
Image source: Julian Walker, Flicker

55. Avoiding failures
55
Source: IEEE Spectrum, Lessons From a Decade of IT Failures

56. Things to avoid: Over-complexifying, Under-delivering
56
Source: IEEE Spectrum, Lessons From a Decade of IT Failures

57. Data Analytics solutions for smart cities
− 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;
− …
57

58. 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
58

59. In conclusion
−Smart cities are made of informed citizens, smart
environments and informed and intelligent decision
making and governance.
−Smart cities should promote innovation, equality and
wider reach of services to all citizens.
−IoT plays a key role in making cities smarter;
openness of data and interconnection and
interoperability between different data sources and
services is a key requirement.
−Technology alone won’t make cities smart.
59

60. IET sector briefing report
60
Available at: http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm

61. CityPulse stakeholder report
61
http://www.ict-citypulse.eu/page/sites/default/files/citypulse_annual_report.pdf

62. 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
62

63. Thank you
http://personal.ee.surrey.ac.uk/Personal/P.Barnaghi/
@pbarnaghi
p.barnaghi@surrey.ac.uk
Acknowledgement: CityPulse Consortium
http://www.ict-citypulse.eu