Owing to Internet of Things (IoT), the volume of Live Data is expected to grow exponentially for the foreseeable future. In this regard, a recent Cisco report (from June 2017) mentioned, in part, the following: * There will be 3.5 networked devices per capita and 27.1 billion networked devices by 2021. * Live internet video will make up 13% of internet video traffic by 2021. * Live video will grow 15-fold from 2016 to 2021. * IDC is predicting 44 zettabytes of newly created data by 2020 and 165 zettabytes by 2025. * Virtual reality (VR) and augmented reality (AR) traffic will increase 20-fold between 2016 and 2021 globally, a compound annual growth rate of 82%. The above highlights the dire need for a generic high throughput and ultra-low latency messaging and reactive platform which can democratize the availability of Live Data and real-time processing. To this end, Satori recently announced its Live Data platform. The platform can potentially be leveraged in a very wide variety of domains. For instance, Satori's platform can be used to help realize the promise of Smart Cities along the following fronts: * Transportation: Fleet management, smart logistics, smart roadways, connected vehicles * Safety: Emergency response, pedestrian and bike safety, crime forecasting, flood detection * Environment: Energy efficiency, air quality, water management, smart street lighting In this talk, we shall walk the audience through the architecture of Satori, its salient features and a concrete country-scale case study. In particular, Satori has partnered with the New Zealand Transportation Agency (NZTA) to deliver a Mobility as a Service (MaaS) Marketplace of smart city apps and services that reduce traffic congestion in high-growth urban areas. Satori is closely collaborating with public agencies and private transit agencies in New Zealand for NZTA's MaaS project. In collaboration with site planners, data managers, and city administrators, Satori is coalescing streaming data feeds from all transportation data sources into a single live and reactive open data channel for the entire country of New Zealand.

1. LEVERAGING LIVE DATA
TO REALIZE THE SMART CITIES VISION
SANDRA SKAFF, DHRUV CHOUDHARY, FRANCOIS ORSINI, ARUN KEJARIWAL

2. PROJECTIONS
2
2050
2.5B increase in urban population
Asia and Africa
90% increase in urban population
North America, Latin America, the Caribbean, Europe
Top urbanized regions
100 cities-1M people in the next 10 years
Cities to be built
1950
30% of the world’s population urban
60M increase/year
Urban Residents
2014
54% of the world’s population urban
https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf

3. PROJECTIONS
2015 2020e
$14.85
$34.35
GLOBAL SPENDING ON
SMART CITIES
(BILLIONS USD)
SOURCE: CTA/UPS THE EVOLUTION OF SMART CITIES AND CONNECTED COMMUNITIES
70%
OF THE WORLD’S
POPULATION FORECAST
TO LIVE IN CITIES BY
2025
1.6 BILLION CONNECTED DEVICES
WERE USED BY SMART CITIES IN 2016,
UP 39% FROM 2015

4. DATA
Four Quadrants
DATA QUADRANTS

5. LIVE DATA
Why?
5
Obviate the need for massive storage
Reduce energy footprint
Efficiency
New Use Cases
Business Opportunities
React faster
Speedup decision making
Improve prediction
Eliminating Silos

6. SMART CITIES
Overview and Case Study
6
Smart Transport
Car or Train or Bus, …
Smart Living
Energy, lighting
Smart Environment
Pollution, waste mgmt
Smart Planning
Routing, Life Organization
Data
Disparate sources
Smart Monitoring
Surveillance

7. Smart Screens
NYC’s City 24/7
SMART CITY SOLUTIONS
7
Connected public lighting with
smart cities
Amsterdam’s Intelligent
Lighting Networks
cloud connecting various
entities
Busan Metropolitan
Government
IBM & Nice partnership
Smart ligthing, smart circulation
Collecting real-time data
Chicago’s Array of Things
Singapore’s Smart City
Monitor everything
Queenstown’s MAAS
Real-time transport app

8. IOE AND SMART CITIES
Move from IOT to IOE
8
• machine-to-machine (M2M)
communication
• smart grids
• smart buildings
• smart cities
• person-to-machine (P2M)
• person-to-person (P2P)
With the world becoming more connected…
P2M
P2P
M2M
INTELLIGENCE
People
Data
Things
IoT (Internet of Things) IoE (Internet of Everything)
http://internetofeverything.cisco.com/sites/default/files/docs/en/ioe_public_sector_vas_white%20paper_121913final.pdf
https://www.cisco.com/c/dam/en_us/solutions/industries/docs/gov/everything-for-cities.pdf

9. P2P:PERSONALIZATION 9
Personalized Social Billboards
Advertising
Social hotspots
Route Recommendation
Hotspot Recommendation
Group Behaviors
Public Transport
Commute Incentivization
Citizen Services
Applications
Clustering
Matching data from devices to match people
with like-minded people using clustering

10. SMART SANTANDER
Case Study on IOE and Smart Cities
10
Santander is the capital of the autonomous community
and historical region of Cantabria, situated on the north
coast of Spain.
Smart Santander
❖ In 2011, the city began “SmartSantander” to
improve city operations and give residents a
greater sense of involvement in the operation
of the city.
❖ The City Council oversees implementation of
the SmartSantander project.
❖ The equipment, including the sensors, is
owned and maintained by the city.
❖ Data gathered via the system is also owned
by the city but is shared widely with the
general public.
http://internetofeverything.cisco.com/sites/default/files/pdfs/SmartSantander_Jurisdiction_Profile__051214REV.pdf

11. SMART SANTANDER
Case Study on IOE and Smart Cities
11
Objective
❖ Improve city operations
❖ Improve quality of life
Strategy
❖ Secure leadership and
support
❖ Leverage academic
relationships
Solution
❖ Network of > 25K sensors
for monitoring
❖ Open access to data and
encouraging interaction
Impact
❖ 80% reduction in traffic
congestion
❖ Reduction in travel times
and environmental pollution
http://internetofeverything.cisco.com/sites/default/files/pdfs/SmartSantander_Jurisdiction_Profile__051214REV.pdf

12. ROLE OF DEEP LEARNING
12
Object Detection
Anomaly Detection
Computer Vision
Machine Translation
Sentiment Analysis
Topic Modeling
Natural Language Processing Cost Optimization
Self Driving Cars
Traffic Light Control
Robotics
Deep Reinforcement Learning
Text to Speech
Audio Classification
Audio Analysis
ROLE OF DEEP LEARNING

13. CASE STUDY 13
Computer Vision
Self Driving CarsTraffic Light Control

14. 14
Look at the same location and take pictures
from two different times
Which place appears safer?
Map an entire city
http://cameraculture.media.mit.edu/how-to-use-computer-vision-to-improve-cities/
Computer Vision Approach
SAFE CITIES
} Live Updates
Safe Car Navigation Safe Pedestrian Navigation

15. SMART BUILDINGS
15
Detect environmental and occupancy changes
Adjust lighting
Lighting control
Use sensor and occupancy data
Direct cooling or heating or ventilation
Smart Aire
Provide detailed, non-intrusive views of
workspaces and employee movement
Increase productivity, drive cost-savings
Smart Space
http://www.enlightedinc.com/
Walking around mode
Dialogue
mode
Study
mode
Watching TV
mode
“Applications of Human Motion Tracking: Smart Lighting Control”, CVPRW 2013

16. TRANSPORTATION
An Integral Component of Smart City Initiatives
16
Smart Sensing
Smart Transportation
Smart Cities

17. TRAFFIC ANALYSIS
Congestion Control
Distribute Traffic
Pollution, Noise
Surveillance
Crime Prevention
Debris/
Maintenance
Capacity Planning
Road Building
Routing
Public Transport

18. CONGESTION CONTROL 18
Emergency Control
Optimization Objectives
Average Trip Time
Average Delay/Suffering
Average Noise
Average Pollution Per Inch

19. PARKING SMARTLY
19
EXAMPLES
City of Valencia, Spain
Sensity Systems, Sunnyvale, CA, USA
BENEFITS
Saving infrastructure costs
Saving parking search in term reducing traffic jams
OBJECTIVES
Improve the efficiency in the management of parking lots
Real-time visibility into the availability of parking spaces to citizens
CHALLENGES
Reorganizing parking space
Addressing changes in traffic flow
https://www.us-ignite.org/apps/msqLZMSsMmJTZvHkQTa6bM/
http://www.sensity.com/

20. SMART CITIES CHALLENGES
20
Is there a solution addressing all these challenges?
Social
Accepting sharing data
Political
A lack of shared goals
Economical
Reduced budgets
Operational inefficiencies
Technological
Advances have increased data available
and communication
Privacy
City sharing data including images, videos

21. SATORI
Satori is the only live data platform that enables immediate integration, interaction, correlation, and
intelligent response at high throughput and ultra-low latency.
OVERVIEW
A Unified Live Data Platform
21

22. 22

23. 23

24. NEW ZEALAND TRANSPORTATION
Case Study
24
✦Identify and support
sustainable forms of
transportation
✦Build intelligent public
transportation systems
based on live information
✦ Increase mobility
✦ Reducing:
✴ congestion
✴ fuel consumption
✴ gas emissions
✴ energy consumption
✦ Improve citizens lives
Challenges Outcome

25. NEW ZEALAND TRANSPORTATION
Command Center
25

26. NEW ZEALAND TRANSPORTATION
Command Center with AI
26
✦Traffic Routing
✦Fleet Management
✦Passenger Load
Scheduling
✦Point
✦Trend
✦Spatial
✦Changepoints
Transportation Anomaly Detection

29. CONTACT US
29
@FrancoisOrsini_
FRANCOIS ORSINI
@arun_kejariwwal
ARUN KEJARIWAL
@sandraskaff
SANDRA SKAFF
@choudharydhruv
DHRUV CHOUDHARY

30. That’s all.

31. READINGS
31
✦ “Transforming the City of New York New Platform for Public-Private Cooperation Ushers in Smart Cities of the Future”, CISCO REPORT 2012.
✦ https://arrayofthings.github.io/
✦ “France's Nice Cote d'Azur Region Taps IBM to Help Build a Smarter, Sustainable City”, SMART CITIES COUNCIL 2013.
✦ “Smart+Connected City Services Cloud-Based Services Infrastructure Enables Transformation of Busan Metropolitan City”, CISCO REPORT
2011.
✦ “Dutch port taps smart street lighting, with IoT on the horizon”, LEDs MAGAZINE 2017.
✦ “Singapore Is Taking the ‘Smart City’ to a Whole New Level”, WALL STREET JOURNAL 2016.
✦ “Choice - the new real-time transport app”, https://www.nzta.govt.nz/traffic-and-travel-information/choice-the-new-real-time-transport-
app/.
✦ “IoE-Driven SmartSantander Initiative Reduces Traffic Congestion, Pollution, Commute Times”, CISCO REPORT 2014.
✦ “Computer vision uncovers predictors of physical urban change”, PNAS 2017.
✦ “Applications of Human Motion Tracking: Smart Lighting Control”, CVPRW 2013.
✦ https://www.parkassist.com/
✦ “Success Story: How Infopulse Applied IoT and Computer Vision to Create Two Smart Parking Solutions”, INFOPULSE 2017.