Assignment 2 of the ESP course, topic: Smart City

1. SMART CITY
ESP Assignment 2
Truong Quang Tung (m5222101)
Nguyen Hoang Anh (m5222110)
Manoj Poudel (m5212201)

2. CONTENT
❖ Introduction to Smart City
• Definition with concept mapping
• Why Smart City is interesting
❖ Main components of Smart City
• Internet of Things
• Big Data
❖ Technology: Video analytics towards Vision zero
❖ Challenges and Difficulties
❖ Future Smart City
❖ Conclusion
❖ References

3. INTRODUCTION TO SMART CITY
Definition
• Smart City - incorporates information and communication
technologies.
• To enhance the quality and performance of urban services.
• Such as energy, transportation and utilities - to reduce resource
consumption, wastage and overall costs.
• Smart city – are hyper-connected cities - Internet of things (IoT)-
using sensors - to collect homogeneous data.
• Coordinating data collection and analysis across systems and sectors.
• Technologically equipped to improve the lives of their residents.

4. Concept
mapping
https://www.mindmeister.com

5. Smart City for the general public
• Characteristics: uses Information Technologies to:
• More efficiently use physical infrastructure
• Effectively engage with people in local governance
• Learn, adapt and innovate
• What is the main purpose of smart cities?
Improving the life of their citizens.
INTRODUCTION TO SMART CITY (cont)

6. Reduces Environmental
Footprint
Improves
Transportations
Increases Digital
Equality
More economic
development
opportunities
Upgrades
Infrastructure
Enhances
Public Security
Smart City for the general public
• Benefits of Smart City
INTRODUCTION TO SMART CITY (cont)

7. Smart City for computer science/engineering students
• Some common goals among students?
• Expand knowledge and training.
• Improve work performance.
• Attain a higher job role.
• For computer science/engineering students?
• Interested in new, innovating technologies.
• Wants job opportunities that deals with using
and making new applications.
INTRODUCTION TO SMART CITY (cont)

8. • Designs infrastructure, protocol
• Manages applications
• Gives job opportunities
• Offers professional workplace/infrastructure
Needs:
• Stable jobs
• Good facilities
• Innovations
Needs smart
people with IoT
knowledge
Relationship between Smart City and computer scientists/engineers
INTRODUCTION TO SMART CITY (cont)

9. IOT IN SMART CITY
• What is IoT?
• A system of computing devices, mechanical and digital machines, objects,
animals or people that:
• Has an unique identifier (UID)
• Has the ability to transfer data over a network without requiring human help.
• Fundamental characteristic of smart cities:
Collect and analyze data to efficiently manage resources
→ IoT is the solution

10. Control Flow
Data Flow
IOT IN SMART CITY (cont)
Outline of an IoT System

11. * Image source: https://www.scnsoft.com/blog/iot-systems-classification
Data is taken
using sensors:
Surveillance
camera, thermal
sensor, etc.
→ Assesses performance
of equipment
→ Proactive maintenance
→ Uncovers new patterns
and tendencies
→ identifies the problems
before the damage is
done
IOT IN SMART CITY (cont)

12. BIG DATA IN SMART CITY
Big Data helps uncover patterns and extract valuable information from data collected by IoT
* Image source: https://dzone.com/articles/how-big-data-has-the-biggest-impact-in-smart-citie

13. BIG DATA IN SMART CITY (cont.)
Sharing data while maintaining privacy is the key in Big Data management
* Image source: https://digitalguardian.com/blog/tackling-gdpr-challenge-4-privacy-design-and-default , http://veterinaryleadershipinstitute.org/balance-
is-key/ , http://beyondplm.com/2011/07/14/top-3-reasons-why-data-sharing-is-important-for-plm/

14. TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO
Road traffic injuries is among top
10 leading causes of death
* Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf

15. TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)
• Traditional crash reporting process is ineffective in preventing accidents
* Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf

16. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
Reduce minor conflicts
Significant fall in serious accidents
Don’t wait for crashes to happen!
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

17. Leverage city’s traffic camera system to:
➢ monitor counts and travel speed of all road user
groups (vehicle, pedestrian, and bicycle)
➢ Document the directional volume of all road user
groups in an intersection
➢ Assess unsafe “near miss” trajectories and
interactions between all road user groups.
* Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

18. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
• Captured videos are sent to cloud server for analysis
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

19. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
Object
detection by
Deep Neural
Networks
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

20. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
Trajectory
detection and
turning
movement
counts
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

21. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
Near-miss
event
detection
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

22. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
• Accomplishment1: Vision technologies achieve high performance in the sample traffic videos
Table 1: Object classification accuracy Table 2: Turning movement count accuracy
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

23. * Image source: https://www.psrc.org/sites/default/files/peer1707-pres-videoanalytics.pdf
• Accomplishment 2: A prototype end-to-end system is built and able to
operate at real-time
• Accomplishment 3: Pilot deployment of end-to-end system (running on
servers provided by Microsoft) in the City of Bellevue traffic control
center. The system will run off of a live feed.
TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)

24. TECHNOLOGY: VIDEO ANALYTICS TOWARD VISION ZERO (cont)
https://www.youtube.com/watch?v=rTAOwvU6Yj8

25. CHALLENGES AND DIFFICULTIES
• Existing Infrastructure
• Digital Security
• Privacy Concerns
• Educating and engaging the community
• Technology challenge with coverage and capacity
• Funding and Business models

26. FUTURE SMART CITY
• Smart Data
• Beating heart of the smart city
• Collecting data from residents, vehicles, infrastructure etc.

27. FUTURE SMART CITY (cont)
• Smart Transportation
• Fully automated vehicles
• Capable of understanding their surrounding environment to make decision

28. FUTURE SMART CITY (cont)
• Smart Energy
• Clean energy sources to power its city
• Convert street lights with LEDs to reduce energy

29. FUTURE SMART CITY (cont)
• Smart Infrastructure
• With the data collected, city planners and architects could create buildings that are
optimized for people based on previous data.
• Tested on citizens to ensure that they truly benefit

30. * Image source: http://www.alivio.me/
FUTURE SMART CITY (cont)
• Smart IoT with Artificial Intelligence (AI)
• The device that bring everything together with help of AI.
• Cloud Computing: The Future of IoT
• Digital intelligence making life safer and more efficient
• Predictive Maintenance Boost up by IoT

31. CONCLUSION
• Smart City – future of urban cities
• IoT and Big Data play an important role in Smart City
• IoT: connect devices and collect data
• Big Data: extract useful information from data
• Technology: Video Analytics towards Vision Zero
• Smart system analyzing traffic videos to prevent traffic accidents
• Already installed in Bellevue city with promising results
• Many challenges and difficulties remain
• Many future works to accomplish Smart City

32. REFERENCES
• https://www.microsoft.com/en-us/research/wp-
content/uploads/2017/05/ITE_Journal_March_2017_Cover-VAVZ.pdf
• https://medium.com/dataseries/big-data-and-smart-cities-why-we-need-them-now-a194b2498fb1
• https://blogs.unity3d.com/2018/01/23/designing-safer-cities-through-simulations/
• https://en.wikipedia.org/wiki/Accident_triangle
• www.scnsoft.com/blog/iot-systems-classification
• https://medium.com/datadriveninvestor/4-stages-of-iot-architecture-explained-in-simple-words-
b2ea8b4f777f
• https://www.masterstudies.com/article/Why-Smart-Cities-and-Engineers-are-a-Perfect-Fit/

33. THANK YOU FOR YOUR ATTENTION