The course is connected to the following study programs

Teaching language

English

Course contents

The increased migration of populations to urban areas raises a variety of challenges ranging from environmental pollution, community health, personalized mobility, energy consumption, safety and security to privacy which cut across different research themes including people, places, infrastructure and flow. The ultimate ambition of urban computing is the creation of sustainable smart cities for smart living that adapt to the needs of its citizens thru solutions that improve urban environments, human life quality, and city operations systems. These solutions make use of unobtrusive and ubiquitous sensing technologies for acquisition, integration, and analysis of big and heterogeneous data generated by and collected from sensors, devices, vehicles, buildings, and human activity. They help to understand the nature of urban phenomena and even predict the future of urban environments. 

This is a project-based, hands-on course which involves applying solutions from the state-of-the-art literature to address real-world problems. The course introduces sensor technologies, data management techniques for spatial and spatio-temporal data, cloud computing platforms and advanced machine learning techniques for big data analytics.

Learning outcomes

After completion of this course, students will

  • have a holistic overview of essential challenges and solutions to this evolving interdisciplinary field, its fundamental techniques, advanced models, and novel applications;
  • be familiar with the typical technologies that are needed in urban computing which address urban sensing, data management, knowledge fusion across heterogeneous data, and urban data visualization;
  • be able to identify data sources and barriers toward smarter and more efficient cities;
  • understand how large scale traces of data including communication, energy, social proximity or mobility data are generated, managed, and processed, how they can be modeled to address urban challenges, and what are the associated ethical issues;
  • have investigated an interesting aspect of urban computing;
  • have received hands-on training in the design and implemetation of a practical data-driven solution to a real-world problem including exposure to new machine learning algorithms;
  • have gained an appreciation of non-technological aspects of smart cities.

Teaching methods

The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. Guest lectures may be invited to cover specific aspect of smart cities.

The workload for the average student is approximately 200 hours.

Evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as Single Standing Module

Yes, if there are places available

Admission Requirement if given as Single Standing Module

Admission requirements for the course are the same as for the master’s programme in ICT.

Assessment methods and criteria

This course is given on a pass/fail basis:

  • Project: 50%
  • Presentation: 25%
  • Report: 25%
Last updated from FS (Common Student System) June 30, 2024 11:28:13 PM