Ubiquitous Systems

Academic year
Subject Area
Language of Instruction
Other Languages of Instruction
Mode of Delivery
ECTS Credits
2nd Cycle Studies - Mestrado

Recommended Prerequisites

As this course addresses the application of competencies in various areas – programming, data analysis, communication protocols, and others – it has as prerequisite a transversal use of the knowledge acquired in a bachelor’s degree in informatics engineering or computer science. 

Teaching Methods

Teaching methodologies include theoretical classes and lab classes, plus the development of a project along the semester.

Resources include (non-exhaustive): slides, demonstration systems, specific hardware (e.g. smartphones, sensors, and drones for data acquisition) 

Evaluation comprises a project and an exam.

Both exam and project score 50% of the final classification in the unit.

Learning Outcomes

Ubiquitous Systems are systems that establish a computational layer between the physical environment and individuals or groups of individuals.  In conjunction with the Internet of Things they create the digital solutions for smart cities, sustainable mobility, sustainable energy, precision agriculture, and smart manufacturing.

In this unit the students acquire the knowledge and competences for conception and development of ubiquitous systems and their integration in the operating environment. This comprises familiarization with the use of geodata and Geographic Information Systems, Outdoor and Indoor Positioning Technologies, and algorithms for Behaviour Modeling.

Experimental work will make use of a Geographic Information System, a Machine Learning Toolkit, and smartphone apps for data acquisition.

Work Placement(s)




Ubiquitous computing: everytime, everywhere. Context-awareness, location and activity sensing, privacy and social concerns.
 Applications for smart cities, sustainable energy, precision agriculture and smart manufacturing.

Geographic Information Systems

What is a GIS? Functional requirements. Models for geospatial information. WebGIS. Web mapping services. Integration with Android Studio.


Wireless communications. Positioning concepts. Positioning methods. Global Navigation Satellite Systems (GNSS). Based on the cellular network: GSM, LTE, 5G. Indoor location.

Behaviour Modeling

Models. Data acquisition. Algorithms for inference of route and mode choices, departure time, activity schedule, land use. Algorithms for prediction of the next place.

Head Lecturer(s)

Carlos Manuel Robalo Lisboa Bento

Assessment Methods

Project: 50.0%
Exam: 50.0%


- KUPPER, Axel. Location-based Services, 2nd Edition. John Wiley & Sons, 2007.

- FRATTASI, Simone, ROSA, Francescantonio Della. Mobile Positioning and Tracking, 2nd Edition. IEEE Press, 2017.

- WORBOYS, Michael, DUCKHAM, Matt. GIS – A Computing Perspective, 2nd Edition. CRC Press, 2004.

- F Li, C Zhao et al. A reliable and accurate indoor localization method using phone inertial sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pages 421-430.

- Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, WeiYing Ma.  Understanding Mobility Based on GPS Data. UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing, Pages 312-321.

- Salvatore Scellato1, Mirco Musolesi2, Cecilia Mascolo1, Vito Latora3, and Andrew T. Campbell. NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems.  International Conference on Pervasive Computing Pervasive 2011: Pervasive Computing pp 152-169.