A taxonomy proposal for clasifying future cognitive cities, reusing what we know about them and smartcities.

1. Cognitive smart cities and
deep learning:A
classification framework
ICEDEG 2019
Quito, Ecuador
Servio Fernando Lima Reyna, PhD (c ) University of Fribourg, Switzerland

2. Agenda
 Why this research?
 Proposed taxonomy
 Why this taxonomy?
 Q&A

3. Why this
research?
Database Years
Num articles
raw
ACM 2006-2019 9
IEEE Xplore 2006-2019 6
Elsevier library 2006-2019 148
Springer library 2006-2019 354
TOTAL 517
Final cut
1
1
0
1
3
Too few papers currently researching about how to use deep learning in
cognitive cities
Note: Papers were selected as long as their title, abstract or keywords contained one of the following combination of words:
cognitive cities, deep learning in Nov 2018.

4. Why this
research?
 Smart city :based on IoT devices.Concerns are related to
efficiency and sustainability
 Cognitive city: On top of a smart city. Same concerns + resilience
(i.e. memory)
To better understand smart vs cognitive cities

5. Why this
research?
 CC take into account more variables to process: Devices + persons
+ government in an any to any communication environment.
¨…the subjacent idea behind a cognitive city is that it learns from all
the possible interactions between humans and machines, humans to
humans and machines to machines so that it keeps memory of those
interactions, retrieve experiences, learns from them and improves
the efficiency, sustainability and resilience of the city¨
To better understand cognitive cities (CC)…

6. Why this
research?
 Because it can ingest all the variables and data that can be
gathered in a cognitive city (IoT, social media, government db, etc)
 Because its accuracy in prediction.
 Because its three types of algorithms that allows a wide range of
application: Discriminative, Generative and Hybrid
To better understand why deep learning is
needed in cognitive cities…

7. Proposed
taxonomy
Taxonomy
elements
Deep
learning
Technique
Challenge
Use case
Method
(technology)
Discriminative
Generative
Hybrid
Efficiency
Sustainability
Resilience
Health
Emergency
Power grid
Traffic Management
Home
Others
Vehicle
Humans
Networks
Smart cities
realm
Cognitive cities
realm

8. Proposed
taxonomy

9. Proposed
taxonomy
Examples of applications in cognitive cities:
¨LSTM is of particular interest for cognitive cities because it implements memory
mechanisms that analyzes data of the past (past years, months, days, hours) and the
present to find patterns.¨
Discriminative algorithms and resilience
Long ShortTerm Memory (LSTM)

10. Proposed
taxonomy
Examples of applications in cognitive cities:
¨In regards to resilience in cognitive cities, Autoencoders can be used to detect
anomalies such as early epidemic outbreaks, atypical traffic build up and plan
before the problem gets worst´
Generative algorithms and resilience
Autoenconder (AE)

11. Proposed
taxonomy
Examples of applications in cognitive cities:
¨There is still an open field of research of GANs for improving resilience in
cognitive cities, but one field of research may be the creation of synthetic data
that simulates network outages and train DL algorithms faster.´
Hybrid algorithms and resilience
Generative adversarial
networks (GAN)

12. Conclusions
 The present study leverages well known taxonomies of smart
cities and adds the dimensions of resilience and deep learning
algorithms that are particular of cognitive cities
 This taxonomy shows the need of deep learning algorithms to
process the huge amount of data that cognitive cities should
consider.
 This taxonomy shows how several types or techniques of deep
learning algorithms (i.e. discriminative, generative and hybrid)
can be used to solve different problems related to resilience,
that is the feature that differentiate cognitive cities.

13. Q&A

14. Muchas gracias!
Servio Fernando Lima Reina, PhD student
Human-IST institute, University of Fribourg, Switzerland
servio.lima@unifr.ch

15. Annex
Glossary DL