According to the McKinsey Global Institute’s extensive study of global cities: - 80% of global GDP is generated in cities with - 50% in the 380 major cities of the developed world - 10% in the largest 220 cities of the developing world. Some 235 million households earning more than $20,000 will live in the emerging economy cities = growth of a global urban middle class = > high expectations of public services and the quality of the urban infrastructure and environment.

1. Powering the Smart City
Giulio Soro
Solutions Architect Manager, AWS
@giuliosoro
gsoro@amazon.com

2. According to the McKinsey Global Institute’s extensive study of global cities:
• 80% of global GDP is generated in cities with
• 50% in the 380 major cities of the developed world
• 10% in the largest 220 cities of the developing world.
Some 235 million households earning more than $20,000 will live in the
emerging economy cities = growth of a global urban middle class = > high
expectations of public services and the quality of the urban infrastructure and
environment
WHY ARE CITIES SO IMPORTANT?

3. SMART CITIES?

4. ‘…a Smart City should enable
every citizen to engage with all the services on
offer,
public as well as private,
in a way best suited to his or her needs.
-- Smart Cities Background Paper - Gov.uk (2013)
Citizen Centric
Services
Mobility
& urban
planning
Innovation
ACCORDING TO THE UK GOV

5. ‘… a Smart City is a city seeking to address
public issues via ICT-based solutions on the
basis of a multi-stakeholder, municipally
based partnership’.
-- Mapping Smart Cities In The EU: Study - European Parliament
(2014).
Citizen Centric
Services
Mobility
& urban
planning
Innovation
ACCORDING TO THE EU

6. ‘..a City may be called ‘Smart’ when
investments in human and social capital
and traditional and modern
communication infrastructure fuel
sustainable economic growth and a high
quality of life, with a wise management
of natural resources, through
participatory governance’.
-- Schaffers et al. (2011)
Citizen Centric
Services
Mobility
& urban
planning
Innovation
ACCORDING TO THE LITERATURE

7. COLLABORATIVE CITIES
SMART CITIES

8. 26Billion connected units will be installed by 2020
Key Factors:
• Lower price point of sensors, processors and networking
• Increased connectivity
• Reducing cost of data storage and compute
Economic value impact is estimated to be up to $11T by 2025
• Large quantities of data to be analyzed and processed
• Every industry vertical and domain expected to be impacted
https://www.gartner.com/doc/3086918/market-guide-iot-platforms
http://www.mckinsey.com/insights/business_technology/the_internet_of_things_the_value_of_digitizing_the_physical_world
INTERNET OF THINGS (IOT)

9. PARKING SOLUTIONS

11. AIR QUALITY

12. Easily and securely connect devices to
the cloud.
Reliably scale to billions of devices and
trillions of messages.
AWS IoT

14. SENSORS
EXPENSIVE TO DEPLOY AND MAINTAIN
MOBILE DEVICES
UBIQUITOUS AND COST-EFFECTIVE

15. IOT + MOBILE
EXPAND EXISTING INFRASTRUCTURE’S
CAPABILITIES

19. PARKSMART

20. COLLABORATIVE CITIES
CITIZENS ARE THE BACKBONE OF
THE CITY NERVOUS SYSTEM

21. Mobile Applications on AWS
Developer’s mobile app code
User identity mgmt. and auth
User data synchronization
Asynchronous communication
Active devices analytics
User behavior analytics
Engagement analytics
Push notifications
Event triggers
Platform-agnostic mobile back end
Data validation and transformation
File and media storage
Shared database storage
Data collection
and more….
AWS cloud infrastructure
Amazon Cognito
Amazon Mobile Analytics
Amazon SNS Mobile Push
AWS Lambda
Mobile-optimized connectors
(Amazon Kinesis, Amazon S3,
Amazon DynamoDB, Amazon SQS)

22. AWS capabilities for building mobile apps

23. OPEN DATA / CITIZEN DATA
SOCIAL MEDIA CITIZEN DEVICES OPEN DATA

24. OPEN DATASETS
Open Data | Citizen Data
DATA ANALYTICS
Machine Learning | Data Warehousing
SENSOR DATA
Device Data | Voice Data | Touch Data

25. APPS POWERED
BY OPEN DATASETS

29. DATA ANALYTICS

30. It’s never been easier and less expensive to
collect, store, analyze & share data

31. FULLY LOADED FOR BIG DATA
High Performance Databases
Amazon DynamoDB
Amazon Aurora
Sources of Truth
Amazon S3
Amazon EFS
Amazon Redshift
Analysis Platforms
Amazon EMR
Real time
Amazon Kinesis
Predictive Analytics
Amazon Machine Learning

35. PUTTING IT TOGETHER

36. AWS Lambda
Amazon
Machine Learning
Amazon
Kinesis
Amazon
DynamoDB
AWS IoT
Custom
Dashboards
DATA
SOURCES
Smart Devices
Public
Data Sets
Citizen Data
DATA
RETRIEVAL
DATA
PROCESSING
DATA
VISUALISATION
Citizen Apps
DATA
OUTPUT
Processed
Data
Amazon
Redshift Amazon
QuickSight / Other
BI Tools
Amazon EMR

38. Thank You
@giuliosoro
gsoro@amazon.com