Ambient Intelligence – Instituto Politécnico de Coimbra

Base Knowledge

Concepts learned in the Machine Learning course unit will be used.

Teaching Methodologies

Theoretical classes are expository classes.

Practical classes are based on following the development of the practical work interleaved with solving exercises. Some classes will be exclusively dedicated to solving exercises.

All elements of support for theoretical-practical classes are made available to students.

Learning Results

It is intended that students acquire a set of knowledge and skills in the area of Ambient Intelligence, namely:

Know and understand the concepts and technologies
Know, understand and apply data acquisition and fusion techniques from different sensors
Select and apply appropriate machine learning techniques to the data collected to infer patterns about the context and its dimensions
Understand the requirement for adaptability of interfaces to user needs
Know and promote privacy in the acquisition, protection and treatment of the data collected

Program

Theoretical component:

1. Introduction

Comparison and definition of AmI and Ubiquitous Computing
Mark Weiser’s vision. The ISTAG vision
Fundamental concepts of AmI

2. Location-based systems

Spatial Databases
Geographic Information Systems
Geospatial data analysis

3. Sensors, Actuators and Modelling

Opportunistic sensors and private sensors
Internet of Things (IoT)
Context-Aware Computing
Context-Aware Computing for IoT

4. Machine learning for AmI

Intelligent algorithms and data structures that use context: Map Matching, Routing, Voronoi diagrams
Data fusion and clustering
Contextual data classification
Intelligence of Things

5. Privacy

Security vs. Privacy
General Data Protection Regulation
Technical solutions

6. User experience at AmI

Human-Computer Interaction and adaptability
Intelligent interfaces
Field studies

7. Ambient Intelligence applications

Intelligent Transport Systems
Smart cities and Urban Computing
Smart Environments
Precision Agriculture
Industry 4.0

Practical component:

1. Data collection through mobile devices

Different context sensors (location, movement, orientation, temporal, environmental)
Open data platforms
Contextual open data available

2. Storage, visualisation and application of algorithms in contextual data

Spatial Databases
Geographic Information System
Mapping, routing and creating Voronoi diagrams Data
Anonymisation Techniques

3. Machine learning in contextual data

Pre-processing and data cleaning
Exploratory Data Analysis (EDA)
Classification and Clustering

Curricular Unit Teachers

Ana Cristina Costa Oliveira Alves

Internship(s)

NAO

Bibliography

Mandatory: 

[1] Augusto, J., Callaghan, V., Cook, D., Kameas, A., Satoh, I. (2013). Intelligent Environments: a Manifesto. Human-centric Computing and Information Sciences, 3:12 (https://doi.org/10.1186/2192-1962-3-12 )

[2] Ubiquitous Computing Fundamentals. (2010). Edited by John Krumm, ISBN: 978-1420093605, CRC Press. [1A-21-1 (ISEC) – 18970]

[3] Chin, J., Callaghan, V., & Allouch, S. B. (2019). The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective. Journal of Ambient Intelligence and Smart Environments, 11(1), 45-69 (https://doi.org/10.3233/AIS-180506)

[4] Gams, M., Gu, I. Y. H., Härmä, A., Muñoz, A., & Tam, V. (2019). Artificial intelligence and ambient intelligence. Journal of Ambient Intelligence and Smart Environments, 11(1), 71-86 (https://doi.org/10.3233/AIS-180508)

[5] Dunne, R., Morris, T., & Harper, S. (2021). A Survey of Ambient Intelligence. ACM Computing Surveys, 54, 4, Article 73 (May 2022), 27 pages (https://doi.org/10.1145/3447242)

Optional:
[6] Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 128, 105-140 (https://doi.org/10.1016/j.jnca.2018.10.021)

[7] Müller, A., C., & Guido, S. (2017). Introduction to machine learning with Python : a guide for data scientists. ISBN 978-1-449-36941-5, O’Reilly. [ 1A-4-197 (ISEC) – 18236]

[8] Witten, I., Frank, E., Hall, M., Pal, C. (2016). Data Mining: Practical Machine Learning Tools and Techniques. ISBN: 978-0128042915, Morgan Kaufmann, 4th Edition.

[9] Ferraro, R., Aktihanoglu, M. (2011) Location Aware Applications. ISBN: 978-1935182337, Manning Publications.

Other relevant papers (available online via b-on)