Big data analytics are being applied to a wide range of applications domains, including those in charge of controlling critical real-time systems, challenging the need not only to efficiently processing extreme amounts of complex data, but also processing it in real-time. CLASS aims to develop a novel software architecture framework to help big data developers to efficiently distributing data analytics workloads along the compute continuum (from edge to cloud) in a complete and transparent way, while providing sound real-time guarantees. In particular, the CLASS framework will be able to gather the information from all the smart edges, as smart cameras and autonomous vehicles, in order to build the knowledge of the city. The city will then forward the useful information to the interested autonomous cars in real time, in order to immediately improve the navigability of the cars and with the long term goal to reduce both accidents and pollution. The capabilities of the CLASS framework are applied to a real smart-city use case in the City of Modena.

1. A Real-Time Smart City Framework
Micaela Verucchi - micaela.verucchi@unimore.it
Modena - 16/05/19

2. The city of tomorrow
2

3. Make Modena a Smart City
3

4. : Edge and oud Computation: a Highly
Distributed Software for Big Data Analytics
4
Sensor infrastructure to
collect and process in
real-time
Connected cars
equipped with
heterogeneous sensors
and V2X connectivity
Advanced urban
mobility applications
based on a
combination of data-
in-motion and data-
at-rest analytics

5. 5
Short term goals

6. 6
Long term goals

7. Project roadmap
7
city infrastructure (cameras, private network, fog nodes)
camera software
car software
fog and cloud stack
edge to city communication city to edge communication
data storing
data analysis

8. 8
8
Modena Automotive Smart Area

9. CLASS framework
9
Smart City use case
Data Analytic Platform
Computation Distribution
Cloud software
components
Edge software
components
Low level resource manager
Compute Continuum

10. CLASS framework
9
Smart City use case
Data Analytic Platform
Computation Distribution
Cloud software
components
Edge software
components
Low level resource manager
Compute Continuum

11. UNIMORE contribution
10
Edges
Self-driving
Car
Detection
Tracking
Communication edges → city : data gathering
Smart
cameras
Communication city → edges : data forwarding
Fog
nodes
Data
Anonymization

12. The Edges
CAMERAS
11
AUTONOMOUS CARS

13. The Edges
CAMERAS
11
AUTONOMOUS CARS
NVIDIA Tx2

14. The Edges
CAMERAS
11
AUTONOMOUS CARS
NVIDIA Tx2
NVIDIAPx2

15. Detection: You Only Look Once (YOLO)
12
https://github.com/pjreddie/darknet
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern
recognition. 2016.

16. Our YOLO
CAMERAS
http://detrac-db.rit.albany.edu/
UA-DETRACdataset
13
CARS
https://bair.berkeley.edu/blog/2018/05/30/bdd/
BDD100K dataset
YOLO optimized on TensorRT: https://github.com/ceccocats/tkDNN

17. Results of detection
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CAMERAS CARS
6 classes
~12 fps
10 classes
~16 fps

18. Tracking
15
Extended
Kalman
Filter
Nearest
Neighbor
Multiple
trackers

19. Tracking
15
Extended
Kalman
Filter
Nearest
Neighbor
Multiple
trackers

20. Results of tracking
16

21. Data anonymization
17
time
latitude
longitude
classification
orientation
speed

22. Communication with the city
18
FROM EDGES to CITY
The cars and cameras send to the city the
detected objects (anonymized data)

23. Communication with the city
18
FROM EDGES to CITY
The cars and cameras send to the city the
detected objects (anonymized data)
FROM CITY to CAR
The city sends to the car all the information it
has in the area surrounding the car

24. 19
The flow
Edge 1: smart cameras
Edge 2: self-driving car
Edge 3: self-driving car
< 10 ms
< 100 ms
16ms latency
Private 4G networkFog node

25. 19
The flow
City cloud
Edge 1: smart cameras
Edge 2: self-driving car
Edge 3: self-driving car
< 10 ms
< 100 ms
16ms latency
Private 4G networkFog node

26. Demo of May the 17th 2019
VIDEO DEMO
20

27. 21
Thank you for your attention!
Questions?
Micaela Verucchi - micaela.verucchi@unimore.it